DEEP ARTIFICIAL IMMUNE SYSTEM (DAIS) FOR INTRUSION DETECTION IN IOT ECOSYSTEMS

Abstract

The present invention relates to a deep artificial immune system for intrusion detection in IoT ecosystems. The intrusion detection system in IoT ecosystems comprises: a biologically inspired deep artificial immune system (DAIS) model; an innate immune layer for initial anomaly detection based on predefined patterns; an adaptive immune layer employing deep learning techniques for detecting novel and unknown threats; a memory mechanism for storing knowledge of past intrusions to improve future detection; and a real-time detection pipeline system for optimization in resource-limited IoT devices. DAIS mimics natural immune processes to detect and mitigate both known and zero-day threats. DAIS is a robust, scalable, and lightweight intrusion detection system that outperforms traditional data-centric methods, achieving benchmark accuracy of 99.8 percentages on the MQTTset dataset and 87.6 percentages on the imbalanced KDD-CUP-99 dataset.

Specification

Description:Field of the Invention
The present invention relates to a Deep Artificial Immune System (DAIS), more particular to artificial immune system architecture, derived on biological principles, designed to safeguard IoT ecosystems from cyber threats and intrusions.
Background of the Invention
The rapid expansion of the Internet of Things has introduced a large volume of data flow in decentralized and dynamic environments, leading to increased vulnerability to cyberattacks. Current Intrusion Detection Systems (IDS), especially in IoT ecosystems, face several key challenges:
1. Resource Constraints: IoT devices generally possess constrained computational capabilities, memory, and energy resources, making it challenging to implement security solutions that require significant resources.
2. Diverse Attack Vectors: IoT devices face multiple security threats, such as zero-day attacks, DDoS attacks, and network intrusions, that can evade conventional detection systems.
3. Scalability: The growth of IoT devices is accelerating rapidly, which necessitates an intrusion detection system that is scalable and adaptive.
4. Ineffective Traditional IDS: Conventional IDS approaches struggle to cope with the decentralized, dynamic, and heterogeneous nature of IoT environments. Many of these systems fail to detect zero-day attacks or are unable to adapt quickly enough to novel threats.
The research investigates the application of machine learning in IoT intrusion detection through a bio-inspired artificial immune system framework, DAIS. The architecture emulates intrinsic immunity, securing the dynamic IoT environment from 'zero-day' attacks. It outperforms existing data-centric methods, achieving 99.87% and 87.64% accuracy.[ Soni et al.(2024)]
The study presents an innovative methodology that integrates an anomaly-based extended isolation forest, the BAT algorithm, and Nevergrad, demonstrating its efficacy in identifying known and undiscovered threats while minimizing false positives, addressing the growing cybersecurity issue of zero-day network attacks and enhancing network traffic analysis. [Soni et al.(2023)]
The study presents a deep learning approach for anomaly detection using a Restricted Boltzmann Machine (RBM) and a deep belief network. The method achieves a detection rate of 97.9% on the DARPA KDDCUP'99 test dataset, outperforming previous methods. Future work is suggested for larger datasets and attack classes. [K. Alrawashdeh and C. Purdy (2016)]
This paper explores intrusion detection in networks, categorizing existing methods into three groups and presents a new approach encouraged by the human defence system. The model's architecture and characteristics are compared to network-based IDS requirements. [Kim et al. (2001)]
None of the prior art indicated above either alone or in combination with one another disclose what the present invention has disclosed.
The proposed Deep Artificial Immune System (DAIS) addresses these issues by offering a bio-inspired solution that mimics the human immune system's adaptive and self-learning processes. This allows DAIS to detect both known and unknown (zero-day) attacks while being computationally efficient and scalable for resource-constrained IoT devices.
Drawings
Fig.1 Diagram of the Architecture of Innate Immunity Layer of DAIS
Detailed Description of the Invention
The following description includes the preferred best mode of one embodiment of the present invention. It will be clear from this description of the invention that the invention is not limited to these illustrated embodiments but that the invention also includes a variety of modifications and embodiments thereto. Therefore, the present description should be seen as illustrative and not limiting. While the invention is susceptible to various modifications and alternative constructions, it should be understood, that there is no intention to limit the invention to the specific form disclosed, but, on the contrary, the invention is to cover all modifications, alternative constructions, and equivalents falling within the spirit and scope of the invention as defined in the claims.
In any embodiment described herein, the open-ended terms "comprising," "comprises," and the like (which are synonymous with "including," "having" and "characterized by") may be replaced by the respective partially closed phrases "consisting essentially of," consists essentially of," and the like or the respective closed phrases "consisting of," "consists of, the like. As used herein, the singular forms "a", "an", and "the" designate both the singular and the plural, unless expressly stated to designate the singular only.
In order to defend Internet of Things (IoT) settings against both current and future threats, the DAIS architecture mimics the immune system's inherent and self-adaptive mechanisms. The DAIS architecture provides a full end-to-end pipeline for protecting ever-changing IoT network ecosystems and shows improved resilience against real-world attack scenarios. The present system comprises:
a) A biologically inspired deep artificial immune system (DAIS) model;
b) An innate immune layer for initial anomaly detection based on predefined patterns;
c) An adaptive immune layer employing deep learning techniques for detecting novel and unknown threats;
d) A memory mechanism for storing knowledge of past intrusions, improving future detection;
e) A real-time detection pipeline optimized for resource-constrained IoT devices.
Wherein the deep learning detection engine comprises convolutional neural networks (CNNs) and recurrent neural networks (RNNs) for perceiving complex attack patterns.
The system is optimized for detecting zero-day attacks through self-learning mechanisms.
The Deep Artificial Immune System (DAIS) is a unique intrusion detection system designed to secure IoT environments. Its bio-inspired architecture mimics the human immune system, detecting both known and unknown threats. DAIS's self-learning capability allows it to adapt to new attack patterns and evolve over time. Its detection accuracy is remarkable, with rates of 99.87% on the MQTTset and 87.64% on the KDD-CUP-99 datasets. DAIS is resource-efficient, and suitable for deployment on resource-constrained IoT devices without compromising performance. Its robustness against zero-day attacks and scalability makes it a cost-effective solution for large-scale IoT networks. The system's combination of bio-inspired mechanisms, self-learning capabilities, high accuracy, and resource efficiency makes it a groundbreaking solution for IoT security.
Advantages of the Innovation
The Deep Artificial Immune System (DAIS) provides numerous notable benefits, largely by employing a bio-inspired methodology that emulates the human immune system. DAIS utilises a technique that allows it to effectively detect both familiar and unfamiliar threats, such as zero-day attacks, that are generally overlooked by conventional systems. DAIS utilises biological immune mechanisms to produce exceptional detection accuracy, with a rate of 99.87% on the MQTTset dataset and 87.64% on the KDD-CUP-99 dataset. This makes it an extremely dependable solution for IoT contexts.
DAIS excels in its capacity to identify zero-day assaults by utilising a self-learning and adaptive immune-like mechanism. This provides a substantial enhancement compared to traditional intrusion detection systems, which have difficulties in dealing with unfamiliar and innovative threats. Furthermore, DAIS is specifically designed to be highly efficient on IoT devices with limited resources, making it significantly more effective than conventional deep learning models that tend to use excessive resources in such contexts. DAIS guarantees the security of IoT devices by maintaining excellent performance while minimising power, memory, and processor resource usage, eliminating the need for expensive hardware updates.
DAIS has the significant benefit of scalability. The architecture is meant to scale alongside the expanding quantity of IoT devices in the network while preserving its detecting capabilities. This guarantees that when Internet of Things (IoT) ecosystems grow, the system maintains its strong and reliable security measures. Moreover, DAIS is an affordable security solution. The lightweight and efficient architecture of the system minimises the requirement for costly hardware and infrastructure changes, hence reducing the overall expenses associated with protecting extensive IoT networks.
DAIS possesses the capability to promptly detect and adjust to threats in real-time using its inherent and adaptable immune layers, enabling it to evolve in the face of novel dangers. DAIS's dynamic capacity guarantees uninterrupted defence against shifting threat patterns. Furthermore, the system effectively mitigates the occurrence of false positives, a prevalent issue in conventional Intrusion Detection Systems (IDS), by leveraging its biologically inspired pattern recognition process. DAIS provides a complete security solution that covers all aspects of data gathering and threat response, making it a versatile intrusion detection system for dynamic and decentralised IoT environments.
Advancements in Technology
DAIS has made a major technical advancement by incorporating the modelling of both innate and adaptive immunity into its architecture. This design enables the system to integrate inherent pattern recognition with adaptive learning, imitating the mechanism by which the human immune system protects against diseases. DAIS achieves an exceptional level of threat identification, especially in situations where new threats are encountered, by combining both types of immune response. Furthermore, DAIS demonstrates outstanding performance on imbalanced datasets, such as KDD-CUP-99, highlighting its resilience in various real-world attack scenarios that pose challenges for many other systems.
Cost Benefit
DAIS provides various cost advantages. The lightweight aspect of the system decreases the requirement for high-performance computer gear, hence reducing infrastructure and operational expenses. DAIS offers a cost-effective solution for protecting IoT networks, particularly in extensive implementations. In addition, the self-adapting capabilities of the system minimise the requirement for regular updates or manual intervention, hence reducing the overall expenses associated with maintaining IoT security in the long run. DAIS offers cost reductions in both the short-term and long-term, while also providing a high level of protection for IoT environments.

, Claims:1. A system for detecting intrusions in IoT environments, comprises of:
a) A biologically inspired deep artificial immune system (DAIS) model;
b) An innate immune layer for initial anomaly detection based on predefined patterns;
c) An adaptive immune layer employing deep learning techniques for detecting novel and unknown threats;
d) A memory mechanism for storing knowledge of past intrusions, improving future detection; and
e) A real-time detection pipeline optimized for resource-constrained IoT devices.
2. The system for detecting intrusions in IoT environments as claimed in the claim 1, wherein the deep learning detection engine comprises convolutional neural networks (CNNs) and recurrent neural networks (RNNs) for perceiving complex attack patterns.
3. The system for detecting intrusions in IoT environments as claimed in the claim 1, wherein the system is optimized for detecting zero-day attacks through self-learning mechanisms.
4. The system for detecting intrusions in IoT environments as claimed in the claim 1, wherein system further comprises an automated response mechanism to isolate compromised devices and alert network administrators.
5. The system for detecting intrusions in IoT environments as claimed in the claim 1, wherein system detection accuracy is remarkable, with rates of 99.87% on the MQTTset and 87.64% on the KDD-CUP-99 datasets.
6. The system for detecting intrusions in IoT environments as claimed in the claim 1, wherein system's combination of bio-inspired mechanisms, self-learning capabilities, high accuracy, and resource efficiency makes it a groundbreaking solution for IoT security.

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