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An Autonomous Self-learning and Self-adversarial Training Neural Architecture for Intelligent and Resilient Cyber Security Systems 1

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24th International Conference on Engineering Applications of Neural Networks (EANN)

作者： Demertzis, Konstantinos Iliadis, Lazaros Hellen Open Univ Sch Sci & Technol Informat Studies Patras Greece Democritus Univ Thrace Sch Engn Fac Math Programming & Gen Courses Dept Civil Engn Xanthi Greece

ISBN: (数字)9783031342042

ISBN: (纸本)9783031342035;9783031342042

Cybersecurity systems have become increasingly important as businesses and individuals rely more on technology. However, the increasing complexity of these systems and the evolving nature of cyber threats require innovative solutions to protect against cyber attacks. One promising approach is the idea of autonomous self-learning and auto-training neural architectures. Autonomous self-learning refers to the ability of the system to adapt to new threats and learn from past experiences without human intervention. Auto-training, on the other hand, refers to the ability of the system to improve its performance over time by automatically adjusting its parameters and algorithms. This research proposes an autonomous Self-Learning and Self-Adversarial Training (SLSAT) neural architecture for intelligent and resilient cyber security systems. It is an extension of the next-generation continuous-time reservoir computing (CTRC) that was proposed by the authors recently. The CTRC is a time-series anomaly detection system controlled by time-varying differential equations. It uses Reinforcement Learning (RL) to dynamically fine-tune the reservoir computing parameters in order to identify the aberrant changes in the data. The proposed method in this research improves the CTRC's architecture by including a Conditional Tabular Generative Adversarial Network (CTGAN). Specifically, including CTGAN allows the SLSAT architecture to generate synthetic data based on the identified abnormalities to improve the model's performance and adapt to new and evolving threats without manual intervention. This, as proved experimentally, helps the model identify aberrant changes in the data and fend off poison and zero-day attacks.

关键词： reservoir computing continuous-time reservoir computing Cyber Defense time Series Analysis

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Next Generation Automated reservoir computing for Cyber Defense 19th

Next Generation Automated Reservoir Computing for Cyber Defe...

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19th International Conference on Artificial Intelligence Applications and Innovations (AIAI)

作者： Demertzis, Konstantinos Iliadis, Lazaros Hellen Open Univ Sch Sci & Technol Informat Studies Patras Greece Democritus Univ Thrace Fac Math Programming & Gen Courses Sch Engn Dept Civil Engn Xanthi Greece

ISBN: (纸本)9783031341090;9783031341076;9783031341069

It is a fact, that important information related to systems' behavior and dynamics, can be revealed as time passes. Observing changes over time, can often lead to the detection of patterns and trends that might not be immediately apparent from a single system's snapshot. Additionally, the concept of time can be essential in understanding cause-and-effect relationships. Observing how changes in one variable over time affect changes in another, can gain insights into the causal relationships between different system components. time series analysis of data that change over time, can be a powerful tool for understanding complex systems in the field of cyber security. reservoir computing (RC) is a Machine Learning technique, using a fixed and randomly generated high-dimensional dynamic system, called a reservoir, to transform and classify input data. The reservoir acts as a nonlinear and temporal filter of the input data, which is then readout by a linear output layer. continuous-time reservoir computing (CTRC) is a type of recurrent neural network, aiming to model the continuous-time dynamics of the network's neurons. It is particularly useful for applications where time is critical and it can provide insights into the underlying system's dynamics. This paper proposes a next-generation CTRC for cyber defense, where the reservoir neurons are modeled as continuous-time dynamical systems. This means that their behavior is described by a system of differential equations that change over time. In order to model the drift phenomenon, identify the abnormal changes in the data, and adaptively stabilize the learning system. The CTRC parameters are optimized using the Reinforcement Learning (RL) method. The proposed system, as proved experimentally, has several advantages over discrete systems, including the ability to handle signals with high sampling rates and to effectively capture real cyber security signals' continuous nature.

关键词： reservoir computing continuous-time reservoir computing Cyber Defense time Series Analysis

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