Information-extreme machine learning of a cyber attack detection system

Abstract

The study aims to increase the functional efficiency of a machine learning cyber attack detection system. An information-extreme machine learning method of the cyberattack detection system with optimization of control tolerances for recognition features that reflect the traffic properties of the info-communication system has been developed. The method is developed within the framework of the functional approach to modeling of cognitive processes of natural intelligence at the formation and acceptance of classification decisions. This approach, in contrast to known methods of data mining, including neuron-like structures, allows giving the recognition system adaptability to arbitrary initial conditions of the learning matrix and flexibility in retraining the system by expanding the recognition classes alphabet. The method idea is to maximize the information capacity of the attack detection system in the machine learning process. A modified Kullback information measure is used as a criterion for optimizing machine learning parameters. According to the proposed categorical functional model, algorithmic software for attack detection system in the mode of machine learning with the depth of the second level has been developed and implemented. However, the depth level is determined by the number of machine learning parameters, which were optimized. The geometric parameters of the recognition hyperspherical containers classes and the control tolerances on the recognition features were considered as optimization parameters, which played the role of input data quantization levels in the transformation of the input Euclidean learning matrix of the type "object-property" into a working binary learning matrix given in the Hamming space. Admissible transformations of the working training matrix of the offered method allow adapting the input mathematical description of the attacks detection system to the maximum full probability of the correct classification decisions acceptance. Based on the results of information-extreme machine learning within the geometric approach, decisive rules are constructed as practically invariant to the multidimensionality of the recognition features space. The computer simulation results of information-extreme machine learning of the attack detection system to recognize four host traffic of different profiles confirm the developed method's efficiency.

Stochastic forecasting of variable small data as a basis for analyzing an early stage of a cyber epidemic

“…It is impossible to unambiguously answer this question in numerical and parametric form based on the conducted research. This point needs additional investigation in the context of implementing proactive technologies of AI-powered protection of assets against cyberattacks [46][47][48] . However, these aspects do not affect the functionality and adequacy of the material presented in the article.…”

Energy Consumption of Methods for Pattern Recognition using Microcontrollers

“…In future, authors are going to explore machine learning methods and various types of neural networks [26,35] for a more detailed analysis in energy efficiency terms and power consumption optimization of autonomous MCU units in IoT.…”

Security-Informed Safety Analysis of Autonomous Transport Systems Considering AI-Powered Cyberattacks and Protection

“…Keeping in mind the SIS approach mentioned above, the analysed references were subdivided into the following: the references describing stages of the Modified Cybersecurity Kill Chain for AI-powered attacks [ 5 , 6 ]; the references addressing AI-powered attacks utilised against various components of the ATS infrastructure [ 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 ]; the references presenting ATSs cybersecurity assessment and assurance techniques, including decisions based on the application of AI and AI platform (AIP) [ 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 ]; the references highlighting ATS safety issues in the context of the ATS cybersecurity assessment [ 35 , 36 , 37 , 38 ]. …”

“…the references presenting ATSs cybersecurity assessment and assurance techniques, including decisions based on the application of AI and AI platform (AIP) [ 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 ];…”

“…The system has vulnerabilities that lead to the risk of being exploited, causing operational impacts. In this case, it is necessary to systemise and define requirements for AI means, assess the risks of critical failures, and mitigate risks by proper choice of countermeasures [ 33 , 34 ]. However, it is also important to note that ATS cybersecurity assessment should be performed considering safety issues [ 35 , 36 , 37 , 38 ].…”