A DESIGN OF AN ITERATIVE METHOD FOR MOBILE FORENSIC ANALYSIS USING DEEP FEATURE FUSION AND MULTIMODAL GRAPH-BASED FUSION

Abstract

ABSTRACT The present invention relates to a design of an iterative method for mobile forensic analysis using deep feature fusion and multimodal graph-based fusion. The method involves collecting various data types such as location, communication logs, multimedia, app usage, and sensor data from the device and, if needed, associated cloud storage. Data is preprocessed for quality, followed by hybrid analysis: convolutional neural networks (CNNs) process multimedia files, while machine learning models analyze patterns and detect anomalies in user behavior. A comprehensive report highlights critical patterns, anomalies, and behavioral insights, accessible through a user-friendly interface. This innovative approach enhances forensic analysis by delivering faster, more accurate interpretations of complex mobile data, enabling investigators to efficiently uncover valuable insights in digital investigations.

Specification

Description:FIELD OF INVENTION
The present invention generally relates to the field mobile forensic data analysis, specifically to a hybrid system that utilizes deep learning and machine learning techniques for classifying mobile forensic data, detecting anomalous user behaviors, and identifying potential security threats in mobile devices. The framework is particularly suited for large-scale, composite datasets, integrating methods such as autoencoders, graph neural networks (GNNs), generative adversarial networks (GANs), convolutional neural networks (CNNs), and Q-learning.
BACKGROUND OF THE INVENTION
Mobile devices store vast amounts of sensitive data, including communications, location histories, and app interactions, making forensic analysis essential to identify security threats and unauthorized access. However, conventional forensic methods face significant limitations in effectively handling the complexities of mobile data. Traditional machine learning (ML) models, such as Decision Trees and Support Vector Machines (SVM), often struggle with high-dimensional mobile data and require extensive feature engineering, which makes them less adaptable to evolving data patterns. Additionally, many forensic systems focus on single-modality data analysis (e.g., app permissions only), limiting their ability to capture multi-dimensional threats that span different data types, leading to reduced detection accuracy. Conventional tools also overlook the sequential nature of data, missing behavioral patterns that unfold over time, such as anomalies in GPS routes or app usage at specific times, which are critical in forensic investigations. Further, traditional shallow models fall short in capturing complex data relationships, reducing their effectiveness in distinguishing between benign and malicious behaviors. Noise and adversarial attacks present additional challenges, as these methods often fail to differentiate between genuine signals and manipulated data, resulting in higher rates of false positives and negatives. Lastly, traditional models lack the adaptability needed to keep up with the fast-evolving mobile landscape, as they cannot easily generalize to new data or emerging threats. To address these limitations, a hybrid approach that combines deep learning and machine learning techniques is necessary. Such a framework leverages advanced models like autoencoders, graph neural networks, generative adversarial networks, and transfer learning, providing robust, adaptive, and noise-resistant analysis that enhances accuracy and resilience in mobile forensic investigations.
Therefore, there remains a need in the art for a design of an iterative method for mobile forensic analysis using deep feature fusion and multimodal graph-based fusion that does not suffer from the above-mentioned deficiencies or at least provides a viable, economical and effective solution.
OBJECTS OF THE INVENTION
Some of the objects of the present disclosure, which at least one embodiment herein satisfies, are as follows.
It is an object of the present disclosure to ameliorate one or more problems of the prior art or to at least provide a useful alternative.
An object of the present disclosure is to provide a design of an iterative method for mobile forensic analysis using deep feature fusion and multimodal graph-based fusion.
An object of the present disclosure is to provide a design of an iterative method for mobile forensic analysis using deep feature fusion and multimodal graph-based fusion that can combines deep learning and machine learning techniques to improve the precision and reliability of mobile forensic analysis by capturing complex patterns and multidimensional data relationships..
An object of the present disclosure is to provide a design of an iterative method for mobile forensic analysis using deep feature fusion and multimodal graph-based fusion that can adapt to emerging threats and new data types in the dynamic mobile landscape, enabling effective analysis of modern mobile data and user behavior without the need for extensive reconfiguration.
An object of the present disclosure is to provide a design of an iterative method for mobile forensic analysis using deep feature fusion and multimodal graph-based fusion that can integrates and analyzes data from multiple modalities (e.g., GPS data, app interactions, communication logs) to identify potential security threats that span different data sources, providing a comprehensive view of device usage.
An object of the present disclosure is to provide a design of an iterative method for mobile forensic analysis using deep feature fusion and multimodal graph-based fusion that can capture temporal and contextual patterns in user behavior, such as anomalies in GPS routes and app usage over time, to detect suspicious activities more accurately..
An object of the present disclosure is to provide a design of an iterative method for mobile forensic analysis using deep feature fusion and multimodal graph-based fusion that can minimize incorrect classifications, improving the system's ability to distinguish between normal and malicious behavior in a mobile environment.
An object of the present disclosure is to provide a design of an iterative method for mobile forensic analysis using deep feature fusion and multimodal graph-based fusion that can ensure that the forensic analysis respects user privacy and maintains data integrity by applying ethical standards and data protection measures within the analytical processes.
An object of the present disclosure is to provide a design of an iterative method for mobile forensic analysis using deep feature fusion and multimodal graph-based fusion that can allow it to operate effectively on mobile devices or in resource-constrained environments while delivering high performance and accuracy.
SUMMARY OF THE INVENTION
The following presents a simplified summary of the invention in order to provide a basic understanding of some aspects of the invention. This summary is not an extensive overview of the present invention. It is not intended to identify the key/critical elements of the invention or to delineate the scope of the invention. Its sole purpose is to present some concept of the invention in a simplified form as a prelude to a more detailed description of the invention presented later.
An embodiment of the present invention is to provide a novel method for mobile forensic analysis using a combination of deep feature fusion and multimodal graph-based fusion to offer an advanced approach to gathering and interpreting data from mobile devices. that employs a hybrid model integrating deep learning and machine learning to analyze data from mobile devices for forensic and security purposes. The method is designed to address the limitations of traditional forensic tools, which often struggle to provide accurate results due to isolated data processing and lack of contextual awareness in detecting threats.
In accordance with an embodiment of the present invention, the primary components of this method include a data acquisition module, a hybrid analytical model, a contextual analysis module, and an output module. The data acquisition module collects diverse data from the mobile device, such as GPS data, communication logs, and app usage, to provide a comprehensive input for analysis. This multimodal data approach enables the method to capture a more holistic view of user behavior, ensuring a greater likelihood of detecting unusual patterns associated with potential security threats.
In accordance with an embodiment of the present invention, the hybrid model lies at the core of the method's intelligence, combining the strengths of deep learning and machine learning algorithms to process the data effectively. For instance, convolutional neural networks (CNNs) are used for image-based data, while recurrent neural networks (RNNs) or long short-term memory (LSTM) networks process temporal or sequential data. This hybrid approach enhances detection capabilities, making it robust against a variety of security threats, including those that might be adversarial disguised. The model also includes traditional machine learning techniques, like support vector machines (SVMs) or decision trees, for further classification and refinement of detected anomalies, minimizing false positives and negatives. The contextual analysis module adds a temporal and behavioral profiling layer, enabling the method to examine patterns over time and adapt to individual user behavior. By considering both sequential and temporal data, this module can more accurately detect anomalies indicative of suspicious activities that would otherwise appear as normal variations in user behavior. This continuous learning ability allows the method to dynamically adjust its thresholds based on evolving user profiles, improving accuracy and reducing noise.
In accordance with an embodiment of the present invention, the output module provides a comprehensive forensic report that includes anomaly scores, behavioral trends, and specific flagged events for review by investigators. The output is accessible through a visualization interface that aids forensic professionals in understanding the patterns detected and in making informed decisions based on the analysis.
In accordance with an embodiment of the present invention, the additional features improve the practicality and effectiveness of the method. For instance, the invention includes a resource optimization module to balance computational load on the mobile device, ensuring smooth performance and battery efficiency. To enhance privacy and security, the method supports offline data collection, synchronizing the data when connectivity is restored. A cloud-based backend also enables remote access and integration with broader forensic databases, making the method suitable for large-scale or enterprise deployments.
BRIEF DESCRIPTION OF THE DRAWINGS
So that the manner in which the above recited features of the present invention can be understood in detail, a more particular description of the invention, briefly summarized above, may have been referred to by embodiments, some of which are illustrated in the appended drawings. It is to be noted, however, that the appended drawings illustrate only typical embodiments of this invention and are therefore not to be considered limiting of its scope, for the invention may admit to other equally effective embodiments.
These other features, benefits, and advantages of the present invention will become apparent by reference to the following text figure, with like reference numbers referring to like structures across the views, wherein
Fig. 1: illustrate a model architecture of the proposed classification process, in accordance with an embodiment of the present invention.
Fig. 2: illustrate an overall flow of the Proposed Forensic Analysis Process, in accordance with an embodiment of the present invention.
DETAILED DESCRIPTION OF THE INVENTION
The following description is of exemplary embodiments only and is not intended to limit the scope, applicability or configuration of the invention in any way. Rather, the following description provides a convenient illustration for implementing exemplary embodiments of the invention. Various changes to the described embodiments may be made in the function and arrangement of the elements described without departing from the scope of the invention.
Fig. 1: illustrate a model architecture of the proposed classification process, in accordance with an embodiment of the present invention. The method for conducting mobile forensic analysis using a hybrid deep learning and machine learning approach, the method comprises acquiring data from a mobile device, including location data, communication logs, app usage statistics, multimedia files, and device sensor data; pre-processing the acquired data to clean, format, and normalize the data for analysis; analyzing the pre-processed data with a hybrid analysis module that integrates deep learning and machine learning algorithms to identify patterns and predict user behavior; generating a report summarizing the analysis results, highlighting key findings and anomalies; and presenting the report and visualized results through a user interface for interpretation by forensic investigators. The data acquisition step further comprises acquiring data from cloud storage associated with the mobile device to enhance the analysis with additional user activity data. The pre-processing step includes normalizing the acquired data to ensure consistency across different data types and formats, and applying data transformation techniques to improve data quality. The analysis step further comprises utilizing a convolutional neural network (CNN) to analyze multimedia content and detect significant features within images or videos present in the acquired data. The method is configured to enable customization of analysis parameters through the user interface, allowing forensic investigators to define specific thresholds or conditions for the analysis. The report generated includes a summary of detected anomalies, user behaviour patterns, and recommendations for further investigation based on the analysis results.
In accordance with an embodiment of the present invention, to overcome issues of low efficiency & high complexity which are present in existing forensic analysis methods, this section discusses Design of an Iterative Method for Mobile Forensic Analysis Using Deep Feature Fusion and Multimodal Graph-Based Fusion Process. Initially, as per figure 1, the Deep Feature Fusion model for Mobile Forensic Data harnesses the power of autoencoders to distill and fuse features from a myriad of mobile data sources, including call logs, text messages, and GPS data samples. This method's efficacy stems from its ability to learn complex, hierarchical representations of raw data automatically, offering a sophisticated approach to extracting meaningful features that encapsulate the intricate patterns and relationships inherent in mobile forensic data samples. The justification for selecting autoencoders in this context lies in their unparalleled efficiency in identifying and reconstructing the salient features from high-dimensional data, making them particularly suited for the nuanced demands of forensic analysis. This capability not only complements other methods within the forensic analysis framework by providing a robust foundation for feature extraction but also significantly enhances the overall classification accuracy and correlation strength, as evidenced by improved F1-scores and Pearson correlation coefficients. The design of the Deep Feature Fusion process is anchored in a series of operations that encapsulate the underlying operations of the autoencoder-based model. The encoding phase is represented via equation 1, where the input data x is transformed into a compressed representation h as follows,
h=σ(We*x+be)…(1)
Where, We and be represent the weights and bias of the encoder, respectively, and σ represents the Sigmoid activation function, facilitating the capture of non-linear relationships in the data samples. Delineation of the decoding phase is represented via equation 2, where the compressed representation h is mapped back to reconstruct the input data, represent d as x',
x^'=σ(Wd*h+bd)…(2)
In this process, Wd and bd correspond to the weights and bias of the decoder. This reconstruction phase is crucial for the model to learn the most relevant features necessary for accurate data representation. Reconstruction Loss L is introduced via equation 3, which quantifies the difference between the original input x and the reconstructed input x',
L(x,x^' )=∣x-x^' ∣^2…(3)
This loss function, typically the mean squared error, drives the training of the autoencoder, ensuring that the model learns to capture and reconstruct the essential features of the input data samples. To enhance the model's capability for forensic analysis, a regularization term R is added to the loss function, as represented via equation 4,
Ltotal=L(x,x^' )+λ*R(W)…(4)
Where, λ is a regularization coefficient, and R(W) is a regularization function applied to the weights W, which include L2 regularization operations.
This addition helps prevent overfitting, ensuring the model generalizes well to unseen data samples. Next, the fusion of features from different data sources is done via equation 5, utilizing a weighted sum approach to integrate encoded representations h1,h2,...,hn from n different sources,
hfused=∑_(i=1)^n▒〖αi*hi…(5)〗
In this process, αi represents the weight assigned to the ith source's features, reflecting their relative importance in the fusion process. This mechanism allows the model to effectively combine features from various sources, enhancing its analytical power. Finally, the model classify & correlate forensic data by applying a softmax layer to the fused features hfused, yielding the probability distribution over classes C via equation 6,
P(C∣hfused)=softmax(Wc*hfused+bc)….(6)
Where, Wc and bc are the weights and bias of the classification layer, respectively. This layer transforms the fused features into predictions, facilitating the identification of suspicious patterns or relationships in the forensic data samples.
Next, as per figure 2, the Multimodal Graph-Based Fusion Process, underpinned by Graph Neural Networks (GNNs), offers a sophisticated process to encapsulate and analyze complex relationships inherent in data from diverse mobile forensic sources. This methodological choice is driven by the unique ability of GNNs to model relational data, allowing for the effective integration and analysis of heterogeneous information. The versatility of GNNs in handling varying data structures and their adeptness at capturing the intricate dependencies between data elements make them particularly suitable for forensic analysis where data sources are not only varied but deeply interconnected. The integration of multimodal data through a graph-based approach complements existing forensic analysis techniques by providing a holistic view of the relationships and interactions across different data types, enhancing the framework's overall analytical capabilities. The foundation of the graph construction is represented via equation 7, where V represents the nodes and E the edges of the graph,
G=(V,E)…(7)
Where, nodes (V) correspond to data samples from various sources, while edges (E) depict the relationships or correlations between these samples. This graph structure serves as the input for the GNN model. Feature representation of nodes, xv, for each node v in the graph is estimated via equation 8,
xv=Conv(Dv)…(8)
In this context, Dv represents the raw data associated with node v, and Conv is a convolutional feature extraction function that transforms this data into a high-dimensional feature vector xv into feature sets This function is designed to accommodate the heterogeneity of data types across different forensic sources. Next, the neighborhood aggregation process which is a critical component of GNNs, and updates the feature representation of a node based on its own features and the features of its neighbors is represented via equation 9,
hv(k)=AGG(k)({hu(k-1):u∈N(v)})…(9)
Where, hv(k) represents the feature vector of node v at iteration k, N(v) represent s the set of neighbors of v, and AGG(k) is a function that combines the feature vectors of these neighbors to update hv sets. This iterative process allows the model to capture the local graph topology around each of the nodes. The node update function is represented via equation 10, which integrates the node's previous feature representation with the aggregated neighborhood features to produce the updated node representations.
hv(k)=hv(k-1)⋃▒hv(k) …(10)
The union function ensures that both the node's own information and its contextual neighborhood information contribute to its final feature representation, enhancing the model's capacity to capture both local and global structural information sets. After this, the edge update mechanism, crucial for dynamically adjusting the relationships between nodes based on their evolving feature representations via equation 11,
euv(k)=EU(k)(hu(k),hv(k))…(11)
Where, euv(k) represents the updated edge weight or relationship strength between nodes u and v at iteration k, based on their feature vectors in different scenarios. This dynamic edge update mechanism allows the model to refine the graph structure, enhancing its representational fidelity to the underlying data relationships. Finally, the graph-level output generation, which aggregates the updated node features to produce a unified graph representation, facilitating downstream analysis tasks such as classification via equation 12,
zG=READOUT({hv(K):v∈V})…(12)
The READOUT function combines the feature representations of all nodes in the graph, after K iterations of update, into a comprehensive graph-level output zG sets. This output encapsulates the fused information from all data sources, embodying the integrated insights necessary for enhanced correlation analysis and decision-making in forensic contexts.
The Adversarial Noise-Aware Classification process, leveraging Generative Adversarial Networks (GANs), constitutes a novel approach aimed at enhancing the robustness of classification models against noisy data prevalent in mobile forensic environments. This method stands out due to its dual capability: not only does it improve model resilience by simulating adversarial noise conditions, but it also distinguishes between genuine and spurious data patterns. The choice of GANs for this task is motivated by their inherent architecture, comprising two competing networks-the generator and the discriminator-that engage in a continuous game of optimization, making them adept at generating and recognizing complex data distributions, including those manipulated by noise.
Next, the core design of the Adversarial Noise-Aware Classification process is integrated, thus, capturing the essence of the adversarial training dynamics and the method's efficacy in handling noisy forensic data samples. First, the generator G is tasked with creating data samples x′ from a stochastic noise vector z, aiming to mimic the real forensic data distribution via equation 13,
x^'=G(z;θg)…(13)
Where, θg represents the parameters of the generator. This process underscores the generator's role in producing synthetic data that is indistinguishable from actual forensic data, thereby serving as a proxy for potential noise within the dataset samples. Second, the discriminator D evaluates the authenticity of both real data samples x and generated samples x′, assigning a probability that a given sample is real D(x';θd), where θd represent s the parameters of the discriminator. The discriminator's output ranges from 0 to 1, with values closer to 1 indicating real data and values near 0 suggesting synthetic or noisy data samples. The adversarial training process is captured by the objective function, which encapsulates the minimax game between G and D: via equation 14,
min┴G⁡〖max┴D⁡〖V(D,G)〗 〗=E(x∼pdata(x))[logD(x)]+E(z∼pz(z))[log(1-D(G(z)))]…(14)
This process represents the discriminator's aim to maximize its ability to distinguish real from fake data, while the generator simultaneously strives to minimize the discriminator's accuracy, thereby improving its capacity to generate realistic synthetic samples. The gradient update rule for the discriminator is represented via equation 15, illustrating how it adjusts its parameters to improve classification accuracy levels.
θd←θd+α∇(θ,d) 1/m ∑[logD(x(i))+log(1-D(G(z(i))))]…(15)
Where, α is the learning rate and m is the mini-batch size. This gradient ascent step enables the discriminator to better differentiate between real and generated data samples. The update mechanism for the generator is represented via equation 16, showcasing its parameter adjustment in response to the discriminator's current performance,
θg←θg-α*∇(θ,g) 1/m ∑log(1-D(G(z(i))))…(16)
This gradient descent step allows the generator to refine its synthetic data production, making it increasingly difficult for the discriminator to identify generated samples. Finally, the noise-aware classification metric is introduced via equation 17, enabling the discriminator to assign a noise probability score to each of the real data samples.
Pnoise(x)=1-D(x)…(17)
This metric quantifies the likelihood that a real data sample is affected by noise, based on the discriminator's assessment. Lower scores indicate a higher confidence in the sample's authenticity and vice versa for this process. In deploying the Adversarial Noise-Aware Classification process, the model harnesses the adversarial dynamics between the generator and discriminator to simulate and recognize noisy conditions, thereby enhancing the noise resilience of the classification models. This innovative approach not only complements existing forensic analysis methods by improving data quality and reliability but also significantly reduces false positive rates in noise detection, as evidenced by metrics such as precision and recall. Through the meticulous design and implementation of these equations, the process establishes a robust framework for distinguishing genuine data from noise, marking a substantial advancement in the field of mobile forensic analysis.
Based on this, the Temporal Convolutional Sequence Mining process is introduced, employing one-dimensional Convolutional Neural Networks (1D CNNs), which presents a sophisticated framework for analyzing sequential patterns in time-series data typical of mobile forensic datasets & samples. This methodology is predicated on the capability of 1D CNNs to parse through temporal sequences, identifying nuanced patterns and anomalies indicative of user behavior over temporal instance sets. The choice of 1D CNNs for this task is motivated by their exceptional efficiency in handling sequential data, owing to their ability to capture local dependencies and temporal features without the extensive preprocessing required by traditional time-series analysis methods. This characteristic makes 1D CNNs particularly adept at uncovering behavioral patterns within the inherently noisy and complex data environments of mobile forensics, complementing other analytical methods by providing a focused lens on temporal dynamics. First, the convolution operation within a 1D CNN is defined via equation 18,
y(t)=(x*w)(t)=∑_(τ=-∞)^∞▒x(τ)w(t-τ) …(18)
Where, x is the input time-series data, w is the convolutional kernel, and y is the output feature map. This process highlights the convolutional layer's role in extracting temporal features by applying the filter across the input sequence. Next, to incorporate non-linearity into the model, an activation function f is applied to the output of the convolution operation using Rectified Linear Unit (ReLU) enabling the model to capture complex patterns beyond linear relationships. Next the pooling operation is used for reducing dimensionality of the feature maps, thus enhancing the model's generalization capability via equation 19,
p(t)=max⁡(τ)=tt+sa(τ)…(19)
Where, p(t) is the pooled feature map and s is the size of the pooling window in this process. The operation extracts the most salient features within each window, summarizing the data while retaining critical temporal information sets. Next, the process of flattening the pooled feature maps in preparation for classification is represented via equation 20,
z=flatten(p(t))…(20)
Where, z is a vector representing the flattened feature maps from all convolutional and pooling layers, serving as the input to the fully connected layers. Next, the fully connected layer integrates the high-level features for anomaly detection via equation 21,
h=W*z+b…(21)
Where, h is the output vector of the fully connected layer, W represents the weights, and b is the bias. This layer plays a crucial role in synthesizing the extracted features into actionable insights regarding behavioral patterns. Finally, to produce the probability distribution over possible behavioral categories or anomaly scores, a softmax function is applied to the output of the fully connected layer via equation 22,
P(C∣h)=softmax(h)=(e^hC)/(∑e^hj )…(22)
Where, P(C∣h) represent s the probability of each category C given the feature vector ℎ, hC and hj are the components of ℎ corresponding to category C and any category j, respectively. This process facilitates the final decision-making step, distinguishing between normal and anomalous behavioral patterns. The Temporal Convolutional Sequence Mining process, articulated through these equations, encapsulates a robust framework for dissecting the temporal dynamics within mobile forensic datasets & samples. By leveraging the analytical prowess of 1D CNNs, this method not only enhances the detection of abnormal behavioral patterns but also integrates seamlessly with other forensic analysis techniques to offer a comprehensive understanding of user activities over temporal instance sets. Its capability to increase accuracy in anomaly detection, as evidenced by improved AUC and F1-score metrics, underscores its value and efficacy in advancing mobile forensic investigations for different scenarios.
Finally, the adoption of Hybrid Transfer Learning, particularly integrated with Q-learning-a form of reinforcement learning (RL)-presents a pioneering approach. This model synergizes the foundational strengths of pre-trained neural networks with the dynamic adaptability of Q-learning to refine performance on domain-specific tasks, such as the classification and analysis of mobile forensic data samples. The rationale for selecting this hybrid methodology lies in its ability to capitalize on the vast, generalized knowledge encapsulated in pre-trained models while simultaneously harnessing the RL framework to iteratively optimize performance based on unique, task-specific feedback. This strategy not only accelerates model convergence but also significantly enhances generalization capabilities across varied mobile forensic datasets, thereby complementing existing analytical frameworks with its adaptive learning capacity. Firstly, the initialization of the Q Value function is represented via equation 23,
Q(s,a;θ)=E[Rt∣st=s,at=a;θ]…(23)
Where, Q(s,a;θ) represent s the expected reward Rt given state s and action a at timestamp t, with θ embodying the parameters of the neural network model. This operation forms the basis for decision-making in Q-learning, guiding the model to select actions that maximize expected rewards. Secondly, the update rule for the Q Value function, pivotal for learning optimal policies, is defined by the Bellman Process via equation 24,
Qnew(s,a)=Q(s,a)+α[r+γ max┴(a^' )⁡Q(s^',a^' )-Q(s,a)]…(24)
Where, α is the learning rate, r is the immediate reward, γ is the discount factor representing the importance of future rewards, and s′ and a′ are the subsequent state and action, respectively. This recursive equation enables the iterative refinement of Q Values towards optimal policy derivation scenarios. The integration of pre-trained models into the Q-learning framework is achieved by initializing the Q Value function with weights θ derived from these models θpre-trained→θinitial process. This process leverages the deep feature representations learned from large-scale datasets, providing a robust starting point for domain-specific adaptations. The adaptation to mobile forensic analysis involves the fine-tuning of the model based on task-specific data, represented via equation 25,
θ(final)=θ(initial)+Δθ(task-specific)…(25)
Where, Δθ(task-specific) signifies the adjustments made to the model parameters to optimize performance on forensic data, facilitating the hybrid learning process. The optimization of the Q-learning model in the context of mobile forensic analysis is further encapsulated by an objective function via equation 26,
min┴θ⁡〖∑(s,a,r,s')〗 [r+γ max┴(a^' )⁡Q(s^',a^';θ)-Q(s,a;θ)]^2…(26)
This function minimizes the difference between the predicted Q Values and the target Q Values, ensuring that the model accurately reflects the dynamics of decision-making in forensic analysis. Lastly, the convergence of the model is quantitatively evaluated through metrics including classification accuracy and loss, represented via equations 27 & 28,
Accuracy=Number of Correct Predictions/Total Number of Predictions…(27)
Loss=∑_((s,a,r,s'))▒〖[r+γ max┴(a^' )⁡Q(s^',a^';θ)-Q(s,a;θ)]^2…(28)〗
These metrics serve as critical indicators of the model's performance, reflecting its effectiveness in classifying and analyzing mobile forensic data with improved precision and speed of convergence. Through the meticulous design and application of these operations, the Hybrid Transfer Learning model utilizing Q-learning emerges as a robust framework for mobile forensic analysis. By combining the pre-learned generalizations from large datasets with the adaptive optimization capabilities of reinforcement learning, this approach significantly enhances the model's ability to discern complex patterns and anomalies within mobile data, thereby advancing the field of forensic science. Performance of this model is discussed in the next section of this text, where it is compared with existing methods in terms of different evaluation metrics.
Result Analysis
The experimental setup for this work is meticulously designed to evaluate the efficacy of the proposed methods: Deep Feature Fusion using Auto Encoders, Multimodal Graph-Based Fusion Process using GNN, Adversarial Noise-Aware Classification Process using GAN, Temporal Convolutional Sequence Mining Process using 1D CNN, and Hybrid Transfer Learning using Q Learning. This section delineates the experimental conditions, parameter settings, and contextual dataset samples utilized to substantiate the framework's performance.
Deep Feature Fusion using Auto Encoders: Encoder/Decoder Layers: 3 layers each, with [512, 256, 128] neurons. Activation Function: ReLU for intermediate layers, Sigmoid for the output layer. Loss Function: Mean Squared Error (MSE). Optimizer: Adam with a learning rate of 0.001. Epochs: 50. Batch Size: 64.
Multimodal Graph-Based Fusion Process using GNN: Graph Convolutional Layers: 2, with 128 and 64 output features respectively. Activation Function: ReLU. Edge Weight Update Mechanism: Implemented via attention mechanism. Loss Function: Cross-Entropy. Optimizer: Adam with a learning rate of 0.001. Epochs: 100. Batch Size: 32.
Adversarial Noise-Aware Classification Process using GAN: Generator/Discriminator Layers: 4 layers each, with [256, 512, 256, 128] neurons for the generator and discriminator. Activation Function: LeakyReLU for intermediate layers, Sigmoid for discriminator output. Loss Function: Binary Cross-Entropy. Optimizer: Adam with a learning rate of 0.0002. Epochs: 200. Batch Size: 128.
Temporal Convolutional Sequence Mining Process using 1D CNN: Convolutional Layers: 3, with filter sizes [128, 64, 32] and kernel size 3. Activation Function: ReLU. Pooling: MaxPooling with pool size 2. Loss Function: Cross-Entropy. Optimizer: Adam with a learning rate of 0.001. Epochs: 100. Batch Size: 64.
Hybrid Transfer Learning using Q Learning: Q-Learning Discount Factor (γ): 0.9. Learning Rate (α): 0.05. Episodes: 1000, with early stopping if convergence is detected. Replay Memory Size: 10000. Batch Size for Replay: 64.
Datasets
The framework was evaluated using a composite dataset amalgamated from several publicly available mobile forensic datasets, including the DREBIN dataset for Android malware detection, the CICAndMal2017 dataset for anomaly detection, and custom-simulated datasets representing typical user behaviors and interactions derived from synthetic call logs, SMS, and GPS data samples.
DREBIN Dataset: Comprises features extracted from 129,013 Android applications, including requested permissions, API calls, and network addresses.
CICAndMal2017 Dataset: Contains network traffic and system logs from 42 different Android applications, with labels for benign and malicious activities.
Custom-Simulated Datasets: Include synthetic datasets with 50,000 entries for call logs, 50,000 entries for SMS, and 30,000 GPS coordinates, simulating typical and atypical user behaviors.
Each dataset was preprocessed to align with the input requirements of the respective models, involving normalization, tokenization, and embedding processes where necessary. The framework's performance was evaluated using metrics such as F1-score, Precision, Recall, and AUC, across various scenarios including noise levels, adversarial attacks, and behavioral pattern variations for different scenarios. The experimental setup, characterized by its comprehensive parameter configurations and diverse dataset samples, was crafted to rigorously assess the proposed framework's capabilities in enhancing mobile forensic analysis, thereby providing a robust foundation for future advancements in the field.
Based on this setup, we present a comprehensive analysis of the performance of the proposed integrative data forensic framework against three existing methods, identified as [4], [12], and [28], across various contextual datasets & samples. The datasets were chosen to reflect a broad spectrum of mobile forensic analysis scenarios, including malware detection, anomaly detection, and user behavior analysis. The proposed model's efficacy is measured using standard evaluation metrics: Accuracy, Precision, Recall, F1-score, and Area Under the Receiver Operating Characteristic Curve (AUC). These metrics provide a holistic view of the model's performance, with a particular focus on its ability to classify and analyze mobile forensic data samples accurately.
Table 2: Performance on DREBIN Dataset
Method Accuracy (%) Precision (%) Recall (%) F1-score (%) AUC (%)
[4] 89.2 88.1 90.3 89.2 94.5
[12] 91.5 90.6 92.4 91.5 96.2
[28] 92.3 91.8 93.7 92.7 97.1
Proposed Model 95.4 94.8 96.1 95.4 98.6
Table 2 showcases the superior performance of the proposed model on the DREBIN dataset, a benchmark for Android malware detection. The proposed model outperforms the existing methods in all metrics, demonstrating its enhanced ability to capture the intricate patterns and malicious behaviors encoded within app data samples. The significant improvement in AUC suggests the model's robustness in distinguishing between benign and malicious applications, a critical capability in mobile forensic analysis.
Table 3: Performance on CICAndMal2017 Dataset
Method Accuracy (%) Precision (%) Recall (%) F1-score (%) AUC (%)
[4] 87.5 86.9 88.1 87.5 93.4
[12] 90.2 89.8 91.6 90.7 95.8
[28] 91.0 90.4 92.3 91.3 96.5
Proposed Model 94.7 94.1 95.3 94.7 98.3
In Table 3, the analysis of the CICAndMal2017 dataset, utilized for anomaly detection, reveals that the proposed model achieves noteworthy advancements in accuracy and AUC, indicating a higher success rate in identifying anomalous behavior accurately. This improvement underscores the model's effective utilization of temporal and behavioral patterns to discern between normal and anomalous activities.
Table 4: Performance on Synthetic Call Log Dataset
Method Accuracy (%) Precision (%) Recall (%) F1-score (%) AUC (%)
[4] 85.7 84.5 87.9 86.2 91.6
[12] 88.4 87.6 89.2 88.4 93.9
[28] 89.1 88.3 90.0 89.1 94.7
Proposed Model 93.5 92.9 94.1 93.5 97.5
The synthetic call log dataset, simulating user call behavior, serves as a basis for Table 4. Here, the proposed model significantly surpasses the performance of methods [4], [12], and [28], especially in terms of Recall and AUC. This highlights the model's proficiency in leveraging sequential and temporal data for identifying patterns indicative of suspicious or fraudulent behavior.
Table 5: Performance on Synthetic SMS Dataset
Method Accuracy (%) Precision (%) Recall (%) F1-score (%) AUC (%)
[4] 84.3 83.7 85.9 84.8 90.4
[12] 87.6 86.9 88.3 87.6 93.1
[28] 88.5 87.9 89.2 88.5 94.0
Proposed Model 92.8 92.2 93.4 92.8 96.9
Table 5 delves into the performance metrics on a Synthetic SMS Dataset, created to mirror typical and atypical SMS messaging patterns among users. The proposed model exhibits an outstanding balance between Precision and Recall, emphasizing its capacity to discern nuanced behavioral cues from text messages. The high AUC value further attests to the model's effectiveness in segregating suspicious messages from benign ones, showcasing its adaptability to different forms of communication data samples.
Table 6: Performance on Synthetic GPS Data
Method Accuracy (%) Precision (%) Recall (%) F1-score (%) AUC (%)
[4] 86.2 85.5 87.9 86.7 92.4
[12] 89.3 88.7 90.0 89.3 94.8
[28] 90.1 89.5 91.2 90.3 95.6
Proposed Model 94.3 93.7 94.9 94.3 98.1
In Table 6, analyzing Synthetic GPS Data aimed at tracking user movement and identifying patterns indicative of suspicious activities, the proposed model significantly outperforms the other methods. This underscores the model's precision in spatial data analysis, critical for mobile forensic analysis in cases involving location tracking and movement patterns.
Table 7: Comparative Analysis on Noise Detection and Classification
Method Accuracy (%) Precision (%) Recall (%) F1-score (%) AUC (%)
[4] 83.7 82.9 85.5 84.2 89.6
[12] 86.5 85.8 87.2 86.5 91.9
[28] 87.4 86.7 88.1 87.4 92.7
Proposed Model 91.9 91.2 92.6 91.9 96.4
Table 7 presents the model's performance in the context of Noise Detection and Classification, a critical component for ensuring data quality in forensic analysis. The proposed model demonstrates superior performance, highlighting its enhanced capability to distinguish between genuine and spurious patterns effectively. This improvement is pivotal for reducing false positives, thereby enhancing the reliability of forensic analyses.
Table 8: Overall Performance on Behavioral Pattern Analysis
Method Accuracy (%) Precision (%) Recall (%) F1-score (%) AUC (%)
[4] 85.0 84.4 86.6 85.5 91.3
[12] 88.2 87.5 89.0 88.2 93.7
[28] 89.0 88.4 90.3 89.3 94.5
Proposed Model 93.6 93.0 94.2 93.6 97.8
Table 8 encapsulates the framework's overall efficacy in Behavioral Pattern Analysis across various datasets & samples. The proposed model's standout performance in terms of Accuracy, Precision, Recall, F1-score, and AUC affirms its comprehensive ability to analyze and identify behavioral anomalies and patterns within mobile forensic data samples. This capability is essential for discerning potential security threats and understanding user behavior comprehensively. The tables collectively illustrate the proposed model's substantial advancements over existing methods across a spectrum of datasets and forensic analysis scenarios. These results validate the model's robustness, versatility, and improved performance in mobile forensic analysis, marking a significant stride towards sophisticated, data-driven forensic investigations for different scenarios. Next, we discuss a practical use case of the proposed model, which will assist readers to further understand the entire classification process.
Practical Use Case
In the exploration of advanced methodologies for enhancing mobile forensic analysis, the research delves into the application of a suite of computational techniques tailored to dissect and interpret the complex landscape of mobile data samples. The analysis encompasses five pivotal processes: Deep Feature Fusion using Auto Encoders, Multimodal Graph-Based Fusion Process using GNN, Adversarial Noise-Aware Classification Process using GAN, Temporal Convolutional Sequence Mining Process using 1D CNN, and Hybrid Transfer Learning using Q Learning. Each process targets specific aspects of the data, from feature extraction and noise identification to behavioral pattern recognition, culminating in a comprehensive analytical framework. The ensuing discussion presents the outputs of these processes, articulated through the lens of sample blocks and data samples, embodying a diverse array of features and indicators to illustrate the model's efficacy. The examination of each process is facilitated through a structured analysis of sample blocks and data samples, encompassing various features and indicators. This analysis employs a practical example wherein data samples are derived from a composite mobile forensic dataset. The dataset includes features such as app usage patterns, GPS locations, call logs, and text messages, each encoded with a mix of continuous and categorical variables representing user behavior and interaction with mobile devices & sets.
Table 9: Deep Feature Fusion using Auto Encoders
Objective: To extract and fuse high-level feature representations from raw mobile forensic data, enhancing the model's ability to identify intricate patterns within the data samples.
Feature Raw Value Encoded Representation
App Usage Frequency High (0.9) 0.87
GPS Location Variability Medium (0.5) 0.65
Call Log Frequency Low (0.2) 0.45
Text Message Sentiment Positive (0.8) 0.75
Table 9 illustrates the transformation of raw mobile forensic data into high-level feature representations through the Deep Feature Fusion process using Auto Encoders. The encoded representations offer a nuanced view of user behavior, facilitating improved pattern recognition and analysis.
Table 10: Multimodal Graph-Based Fusion Process using GNN
Objective: To integrate features extracted from different sources into a unified graph representation, capturing the relationships between data samples.
Data Source Feature Encoded Representation Node Connection Strength
GPS Data Location Variability 0.65 High (0.8)
Call Logs Call Frequency 0.45 Medium (0.6)
Text Messages Sentiment 0.75 Low (0.4)
Table 10 presents the results of the Multimodal Graph-Based Fusion Process using GNN, showcasing how features from diverse data sources are integrated into a unified graph. The node connection strengths indicate the relative importance of each data source in the overall analysis.
Table 11: Adversarial Noise-Aware Classification Process using GAN
Objective: To enhance the robustness of the model against noisy data by distinguishing between genuine and spurious patterns.
Feature Encoded Representation Noise Probability
App Usage Frequency 0.87 Low (0.1)
GPS Location Variability 0.65 Medium (0.5)
Call Log Frequency 0.45 High (0.7)
Table 11 demonstrates the model's capacity to identify and classify noise within the data, a critical step in ensuring data integrity and reliability in forensic analysis.
Table 12: Temporal Convolutional Sequence Mining Process using 1D CNN
Objective: To detect behavioral patterns and anomalies in sequential data representing user activities over temporal instance sets.
Time Period Feature Anomaly Score
Week 1 Call Frequency Low (0.2)
Week 2 GPS Location Change High (0.8)
Week 3 App Usage Pattern Medium (0.5)
Table 12 outlines the outcomes of the Temporal Convolutional Sequence Mining Process using 1D CNN, highlighting its effectiveness in uncovering temporal anomalies and patterns indicative of suspicious behavior.
Table 13: Hybrid Transfer Learning using Q Learning
Objective: To leverage pre-trained models and domain-specific fine-tuning to improve the model's performance on mobile forensic data samples.
Feature Pre-tuning Accuracy Post-tuning Accuracy
Overall 88% 95%
Table 13 encapsulates the significant enhancements in model accuracy achieved through Hybrid Transfer Learning using Q Learning, illustrating the model's improved generalization and analytical precision. The structured presentation of results across tables 7 to 11 underscores the multifaceted approach of the proposed model in dissecting mobile forensic data samples. Through the application of advanced computational techniques, each tailored to address specific aspects of the data analysis process, the model demonstrates a profound capacity for extracting meaningful insights from complex and diverse datasets & samples. The transformation of raw data into nuanced, high-level representations (Table 9) lays the groundwork for a more sophisticated analysis, enabling the identification of intricate patterns that traditional methods may overlook. The integration of these representations into a unified graph (Table 10) further illustrates the model's ability to capture and analyze the relationships between different data sources, a critical component in understanding the multifaceted nature of mobile user behavior. The effectiveness of this approach is highlighted by the strength of node connections, which signifies the relevance of each data source in the overall analysis, showcasing the model's capability to discern the importance of varied data inputs in a cohesive manner.
Moreover, the model's proficiency in enhancing data reliability through noise detection and classification (Table 11) addresses a significant challenge in mobile forensic analysis-maintaining data integrity. By effectively distinguishing between genuine data and noise, the model ensures the accuracy and trustworthiness of the analysis, a crucial factor in forensic investigations where the quality of evidence is paramount.
The temporal analysis component (Table 12) further sets the proposed model apart, offering a dynamic view of user behavior over time. This capability not only aids in the detection of anomalies and patterns but also provides insights into behavioral trends, facilitating a deeper understanding of user activities and potential security threats. Finally, the application of Hybrid Transfer Learning using Q Learning (Table 13) epitomizes the model's adaptability and learning efficiency. The significant improvement in post-tuning accuracy underscores the effectiveness of combining pre-trained models with domain-specific data, a strategy that enhances the model's generalization performance and accelerates its convergence on mobile forensic datasets & samples. In summary, the presented tables and discussions encapsulate the comprehensive and nuanced approach of the proposed model to mobile forensic analysis. By leveraging a suite of advanced computational techniques, the model not only surpasses existing methods in performance but also introduces a new paradigm in the analysis of mobile forensic data, characterized by its depth, precision, and adaptability. Looking ahead, the continued refinement of these techniques and the exploration of new datasets and analytical challenges promise to further extend the capabilities of the model, offering exciting avenues for future research and development in the field of mobile forensics.
While considerable emphasis has been placed herein on the specific features of the preferred embodiment, it will be appreciated that many additional features can be added and that many changes can be made in the preferred embodiment without departing from the principles of the disclosure. These and other changes in the preferred embodiment of the disclosure will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter is to be interpreted merely as illustrative of the disclosure and not as a limitation.
, Claims:We Claim,
1. A method for conducting mobile forensic analysis using a hybrid deep learning and machine learning approach, the method comprising:
acquiring data from a mobile device, including location data, communication logs, app usage statistics, multimedia files, and device sensor data;
pre-processing the acquired data to clean, format, and normalize the data for analysis;
analyzing the pre-processed data with a hybrid analysis module that integrates deep learning and machine learning algorithms to identify patterns and predict user behavior;
generating a report summarizing the analysis results, highlighting key findings and anomalies; and
presenting the report and visualized results through a user interface for interpretation by forensic investigators.
2. The method as claimed in claim 1, wherein the data acquisition step further comprises acquiring data from cloud storage associated with the mobile device to enhance the analysis with additional user activity data.
3. The method as claimed in claim 1, wherein the pre-processing step includes normalizing the acquired data to ensure consistency across different data types and formats, and applying data transformation techniques to improve data quality.
4. The method as claimed in claim 1, wherein the analysis step further comprises utilizing a convolutional neural network (CNN) to analyze multimedia content and detect significant features within images or videos present in the acquired data.
5. The method as claimed in claim 1, wherein the method configured to enable customization of analysis parameters through the user interface, allowing forensic investigators to define specific thresholds or conditions for the analysis.
6. The method as claimed in claim 1, wherein the report generated includes a summary of detected anomalies, user behaviour patterns, and recommendations for further investigation based on the analysis results.

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