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DEEP CONVOLUTIONAL FOREST: A DYNAMIC DEEP ENSEMBLE APPROACH FOR SPAM DETECTION IN TEXT
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ORDINARY APPLICATION
Published
Filed on 21 November 2024
Abstract
3. Abstract: The surge in mobile messaging usage has facilitated the proliferation of social engineering attacks, particularly phishing, wherein spam texts serve as primary vehicles for stealing sensitive data like credit card details and passwords. Concurrently, the rampant dissemination of rumors and inaccurate medical information about the COVID-19 pandemic on social media has fueled public fear and confusion. Hence, effective spam content filtration becomes imperative to mitigate associated risks and threats.Past research has predominantly relied on machine learning and deep learning methodologies for spam classification, encountering two significant challenges. Machine learning models necessitate manual feature engineering, while deep neural networks incur substantial computational overhead. To address these limitations, this study proposes a novel dynamic deep ensemble model for spam detection, capable of autonomously adjusting its complexity and extracting features.The model leverages convolutional and pooling layers for efficient feature extraction and incorporates base classifiers like random forests and extremely randomized trees to categorize texts as spam or legitimate. Furthermore, ensemble learning techniques such as boosting and bagging are employed to enhance classification accuracy.The outcomes of the proposed model exhibit commendable performance metrics, including high precision, recall, F1-score, and accuracy, achieving a notable success rate of 98.38%. This underscores the effectiveness of the dynamic deep ensemble approach in combating spam and misinformation propagation across mobile messaging platforms and social media networks.
Patent Information
Application ID | 202441090453 |
Invention Field | COMPUTER SCIENCE |
Date of Application | 21/11/2024 |
Publication Number | 48/2024 |
Inventors
Name | Address | Country | Nationality |
---|---|---|---|
D.Kalpana | Assistant Professor, Computer Science and Engineering – Data Science, Malla Reddy Engineering College, Maisammaguda, Secundrabad State: TELANGANA Email ID:kalpanamrec23@gmail.com Contact:9959967192 | India | India |
Archana Bathula | Assistant Professor, Computer Science and Engineering – Data Science, Malla Reddy Engineering College, Maisammaguda, Secundrabad State: TELANGANA Email ID:archana.prakash09@gmail.com Contact:9059296995 | India | India |
Dr.Ramu Vankudoth | Professor, Computer Science and Engineering – Data Science, Malla Reddy Engineering College, Maisammaguda, Secundrabad State: TELANGANA Email ID:ramuds@mrec.ac.in Contact:8309175449 | India | India |
R.Sanghavi | Assistant Professor, Computer Science and Engineering – Data Science, Malla Reddy Engineering College, Maisammaguda, Secundrabad State: TELANGANA Email ID:ragamsanghavi@gmail.com Contact:9032565785 | India | India |
Mood Purna Chandar | Assistant Professor, Computer Science and Engineering – Data Science, Malla Reddy Engineering College, Maisammaguda, Secundrabad State: TELANGANA Email ID:purnc133@gmail.com Contact: 989927762 | India | India |
Applicants
Name | Address | Country | Nationality |
---|---|---|---|
Malla Reddy Engineering College | Malla Reddy Engineering College Dhulapally post via Kompally Maisammaguda Secunderabad -500100 | India | India |
D.Kalpana | Assistant Professor, Computer Science and Engineering – Data Science, Malla Reddy Engineering College, Maisammaguda, Secundrabad State: TELANGANA Email ID:kalpanamrec23@gmail.com Contact:9959967192 | India | India |
Specification
Description:Description
1. Title: DEEP CONVOLUTIONAL FOREST: A DYNAMIC DEEP ENSEMBLE APPROACH FOR SPAM DETECTION IN TEXT
2. FieldofInvention:Deep learning and Machine learning
3. Abstract:
The surge in mobile messaging usage has facilitated the proliferation of social engineering attacks, particularly phishing, wherein spam texts serve as primary vehicles for stealing sensitive data like credit card details and passwords. Concurrently, the rampant dissemination of rumors and inaccurate medical information about the COVID-19 pandemic on social media has fueled public fear and confusion. Hence, effective spam content filtration becomes imperative to mitigate associated risks and threats.Past research has predominantly relied on machine learning and deep learning methodologies for spam classification, encountering two significant challenges. Machine learning models necessitate manual feature engineering, while deep neural networks incur substantial computational overhead. To address these limitations, this study proposes a novel dynamic deep ensemble model for spam detection, capable of autonomously adjusting its complexity and extracting features.The model leverages convolutional and pooling layers for efficient feature extraction and incorporates base classifiers like random forests and extremely randomized trees to categorize texts as spam or legitimate. Furthermore, ensemble learning techniques such as boosting and bagging are employed to enhance classification accuracy.The outcomes of the proposed model exhibit commendable performance metrics, including high precision, recall, F1-score, and accuracy, achieving a notable success rate of 98.38%. This underscores the effectiveness of the dynamic deep ensemble approach in combating spam and misinformation propagation across mobile messaging platforms and social media networks.
4. Background: Spam detection in text is a critical task for maintaining the integrity and efficiency of communication systems, ranging from email platforms to social media networks. Traditional methods of spam detection often rely on heuristic-based approaches or simple statistical models, which may struggle to capture the nuanced patterns and evolving nature of spam. Recent advances in machine learning and natural language processing (NLP) offer promising alternatives, particularly through the application of deep learning techniques that can handle complex and dynamic text data more effectively. Convolutional Neural Networks (CNNs) have demonstrated remarkable performance in various domains, including image and text classification, due to their ability to automatically extract hierarchical features from raw data. In the context of text analysis, CNNs can leverage their spatial hierarchies to capture context and semantic relationships, which are crucial for distinguishing between legitimate and spam content. However, a single CNN model may still face limitations in terms of adaptability and generalization across diverse spam patterns.
5. Objective of Invention: The primary objective of the invention " DEEP CONVOLUTIONAL FOREST: A DYNAMIC DEEP ENSEMBLE APPROACH FOR SPAM DETECTION IN TEXT"istodevelop and evaluate a dynamic deep ensemble model that combines deep convolutional neural networks with decision forests, aiming to enhance the accuracy and robustness of spam detection in text by leveraging both the hierarchical feature extraction capabilities of deep learning and the decision-making strengths of ensemble methods.
6. Summary of the invention:" DEEP CONVOLUTIONAL FOREST: A DYNAMIC DEEP ENSEMBLE APPROACH FOR SPAM DETECTION IN TEXT" is The project presents "Deep Convolutional Forest," an advanced deep learning ensemble method designed to enhance spam detection in text. This approach combines deep convolutional networks with a dynamic forest ensemble model to improve the accuracy and efficiency of spam classification.
The methodology leverages the strength of convolutional neural networks (CNNs) to extract intricate features from textual data, which are then processed by a forest-based ensemble system to make robust classification decisions. By integrating CNNs, the model captures hierarchical and contextually relevant features in text, while the ensemble component ensures diverse and complementary decision-making processes.
The dynamic nature of the ensemble allows the system to adapt to evolving spam patterns and content, providing a scalable solution for real-time spam detection. The results from extensive experiments demonstrate that this approach outperforms traditional spam detection methods, offering higher precision, recall, and overall effectiveness in distinguishing between spam and legitimate messages.
7. Informationaboutdrawing: None
8. Best Methods for Coming out the Invention: To effectively bring the " DEEP CONVOLUTIONAL FOREST: A DYNAMIC DEEP ENSEMBLE APPROACH FOR SPAM DETECTION IN TEXT" inventionto fruition, several key methods should be employed. Extracts features from the input word matrix by applying convolutional operations, resulting in feature maps.Reduces the risk of overfitting by down-sampling the feature maps.Contains four base classifiers-two random forests and two extremely randomized trees-to predict the probabilities for Spam and Not-Spam. Random Forest (RF) classifiers aggregate predictions from individual decision trees, while Extremely Randomized Trees (ERT) classifiers aim to further diversify the predictions.Multiple weak learners (random forests and ERTs) operate independently in parallel, combining their outputs to improve model performance.DCF sequentially adds levels, with each new level correcting errors made by the previous level. This improves the model's overall robustness.The final prediction is determined by averaging the probabilities for Spam and Not-Spam from the last level's output. The class with the maximum average probability is selected as the final prediction.The methodology is evaluated using the SMS spam dataset, a collection of messages labeled as 1 (Spam) or 0 (Not-Spam). The DCF model's adaptive nature and ability to combine bagging and boosting techniques make it a powerful tool for classifying text data in scenarios where accuracy and scalability are critical.In summary, the combination of word embeddings for text representation and the DCF model for classification provides a robust and adaptive approach for spam detection and similar text classification tasks.
a. PYTHONLIBRARIES:
b. NumPy:Used for numerical computations and handling multi-dimensional arrays, which are essential for data manipulation and preprocessing.
c. Pandas: Provides data structures and data analysis tools for handling tabular data, crucial for data preprocessing and exploration.
NLTK (Natural Language Toolkit): Provides utilities for text processing, including tokenization, stemming, and stop-word removal, which are important for preparing textual data.
d. TensorFlow: An open-source platform formachinelearning,oftenusedforbuilding and training deep learning models, including CNNs and RNNs.
e. Webbrowser:Itprovidesinterfacefordisplayingweb-baseddocumentstousers.
f. PyTorch:Anotherdeeplearninglibrarythatoffersflexibilityanddynamic computation graphs, also suitable for building various neural network architectures.
g. SpaCy: Another libraryfor advanced natural language processing, useful fornamed entity recognition, dependency parsing, and more.
9. Industrial Applicability: The " DEEP CONVOLUTIONAL FOREST: A DYNAMIC DEEP ENSEMBLE APPROACH FOR SPAM DETECTION IN TEXT" invention has significant industrial applications across various sectors. The "Deep Convolutional Forest (DCF): A Dynamic Deep Ensemble Approach for Spam Detection in Text" has several industrial applications across various sectors, thanks to its advanced capabilities in spam detection, dynamic feature extraction, and adaptability. Here are some key areas of applicability:Companies can use DCF to filter genuine customer support requests from spam or irrelevant messages, thereby optimizing customer service operations.DCF can be deployed to protect customer service desks from spam and potential phishing attempts, ensuring the security of customer interactions.Cybersecurity companies can integrate DCF into their threat detection and mitigation solutions, offering enhanced protection against spam and phishing attacks.DCF's adaptable architecture allows cybersecurity firms to customize spam detection and filtering solutions based on the specific needs of their clients.By integrating the Deep Convolutional Forest model into these industries, organizations can enhance security, protect sensitive information, and improve the quality of communication, thereby fostering a safer and more reliable digital environment.
, Claims:CLAIMS
What is claimed is:
The"DEEP CONVOLUTIONAL FOREST: A DYNAMIC DEEP ENSEMBLE APPROACH FOR SPAM DETECTION IN TEXT" project presents a comprehensive solution to the pervasive issue of misinformation in digital media. The following claims encapsulate the innovative contributions and potential impact of this endeavor:
The claims for the "Deep Convolutional Forest: A Dynamic Deep Ensemble Approach for Spam Detection in Text" are centered around its innovative architecture and effectiveness in spam detection tasks. Based on the document, the following claims can be made:
1. High Accuracy in Spam Detection
The Deep Convolutional Forest (DCF) achieves a high accuracy rate of 98.38% in detecting spam and non-spam text messages. This surpasses the performance of traditional machine learning models and existing deep learning techniques.
2. Dynamic Model Adaptability
Unlike traditional deep neural networks with a fixed number of hidden layers, DCF dynamically adjusts its depth by adding new levels only when they improve accuracy. This adaptability allows the model to handle datasets of varying sizes efficiently without overfitting.
3. Automatic Feature Extraction
DCF eliminates the need for manual feature engineering by automatically extracting high-level features from textual data using convolutional and pooling layers. This reduces the dependency on domain expertise and enhances the scalability of the model.
4. Integration of Ensemble Learning Techniques
The model incorporates ensemble learning techniques like bagging and boosting. Bagging reduces variance by combining predictions from multiple weak learners operating in parallel, while boosting reduces bias by sequentially correcting the errors of previous levels. This combination helps improve classification accuracy and robustness.
5. Reduction of Computational Overhead
Compared to deep neural networks that require extensive hyper-parameter tuning, DCF minimizes computational overhead by using fewer hyper-parameters and optimizing model complexity based on performance gains. This makes the model more efficient and faster to train.
6. Enhanced Spam and Misinformation Mitigation
DCF is particularly effective in mitigating risks associated with social engineering attacks (such as phishing) and the spread of misinformation. Its ability to filter out harmful content on messaging platforms and social media networks protects users from various cyber threats.
7. Versatility in Handling Imbalanced Datasets
DCF is capable of handling class imbalances in datasets, as demonstrated by its application on the SMS spam dataset where spam messages were significantly fewer than legitimate ones. The model achieves balanced and accurate results by using techniques like SMOTE for oversampling.
8. Applicability Across Languages and Domains
While the current implementation focuses on English text, the architecture can be extended to support spam detection in other languages and domains by modifying the preprocessing and embedding layers to accommodate different linguistic features.
Documents
Name | Date |
---|---|
202441090453-COMPLETE SPECIFICATION [21-11-2024(online)].pdf | 21/11/2024 |
202441090453-DRAWINGS [21-11-2024(online)].pdf | 21/11/2024 |
202441090453-EDUCATIONAL INSTITUTION(S) [21-11-2024(online)].pdf | 21/11/2024 |
202441090453-EVIDENCE FOR REGISTRATION UNDER SSI [21-11-2024(online)].pdf | 21/11/2024 |
202441090453-EVIDENCE FOR REGISTRATION UNDER SSI(FORM-28) [21-11-2024(online)].pdf | 21/11/2024 |
202441090453-FIGURE OF ABSTRACT [21-11-2024(online)].pdf | 21/11/2024 |
202441090453-FORM 1 [21-11-2024(online)].pdf | 21/11/2024 |
202441090453-FORM FOR SMALL ENTITY [21-11-2024(online)].pdf | 21/11/2024 |
202441090453-FORM FOR SMALL ENTITY(FORM-28) [21-11-2024(online)].pdf | 21/11/2024 |
202441090453-FORM-9 [21-11-2024(online)].pdf | 21/11/2024 |
202441090453-PROOF OF RIGHT [21-11-2024(online)].pdf | 21/11/2024 |
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