Sergei V. Bezobrazov

Neural Network and Artificial Immune Systems for Malware and Network Intrusion Detection

Neural network techniques and artificial immune systems (AIS) have been successfully applied to many problems in the area of anomaly activity detection and recognition. The existing solutions use mostly static approaches, which are based on collection viruses or intrusion signatures. Therefore the major problem of traditional techniques is detection and recognition of new viruses or attacks. This chapter discusses the use of neural networks and artificial immune systems for intrusion and virus detection. We studied the performance of different intelligent techniques, namely integration of neural networks and AIS for virus and intrusion detection as well as combination of various kinds of neural networks in modular neural system for intrusion detection. This approach has good potential to recognize novel viruses and attacks.

Development of neural network immune detectors for computer attacks recognition and classification

There is developed a combined method that is based on the integration of neural network detectors in an artificial immune system. This allowed them to adapt to new attacks with the help of cloning and mutation operations.

Intelligent system for detection of networking intrusion

A modified neural detector and a method of principal components analysis were considered to improve a quality of network attacks detection.

Application of Neural Networks to the Electroencephalogram Analysis for Epilepsy Detection

Many techniques were used in order to detect and to predict epileptic seizures on the basis of electroencephalograms. One of the approaches for the prediction of the epileptic seizures is the use the chaos theory, namely determination largest Lyapunovs exponent or correlation dimension of the scalp EEG signals. This paper presents the neural network technique for epilepsy detection. It is based on computing of the largest Lyapunovs exponent. The results of experiments are discussed.

Evolution of immune detectors in intelligent security system for malware detection

In this paper we present the basic principles of the evolution of detectors in intelligent malware detection system. This system based on integration of both AI methods: artificial neural networks and artificial immune systems. The goal of the evolution is adaptation of detectors to new, unknown malicious code for increasing of quality of detection.

Neural Networks for Artificial Immune Systems: LVQ for Detectors Construction

This paper presents a non-standard approach for solving computer viruses detection problem based on the artificial immune system (AIS) method. The AIS is the biologically-inspired technique which have powerful information processing capabilities that makes it attractive for applying in computer security systems. Computer security systems based on AIS principles allow detect unknown malicious code. In this work we are describing model build on the AIS approach in which detectors represent the learning vector quantization (LVQ) neural networks. Basic principles of the biological immune system and comparative analysis of unknown computer viruses detection for different antivirus software and our model are presented.

Intelligent Cyber Defense System Using Artificial Neural Network and Immune System Techniques

Over the past few decades, the application of Artificial Immune Systems (AIS) and Artificial Neural Networks (ANN) has been growing rapidly in different domains. We sincerely believe that integration of these both techniques can allow constructing the Intelligent Cyber Defense System. In this paper an original method for detecting the network attacks and malicious code is described. The method is based on main principles of AIS where immune detectors have an ANN’s structure. The main goal of proposed approach is to detect previously unknown (novel) cyber-attack (malicious code, intrusion detection, etc.). The proposed Intelligent Cyber Defense System can improve the reliability of intrusion detection in computer systems and, as a result, it may reduce financial losses of companies from cyber attacks.

High performance adaptive system for cyber attacks detection

To increase the security of intrusion detection system, generalized structure of highly performance adaptive system for cyber attacks detection was developed. To improve its robustness, methods of artificial intelligence were proposed. Neural immune detectors were used as the main tool for identifying cyber attacks. These detectors for cyber attacks identification and classification and other vulnerable subsystems were implemented in programmable logic arrays. To provide high performance, the Mamdani fuzzy inference rules were used and relevant subsystem structures were developed.

Convolutional neural network based solar photovoltaic panel detection in satellite photos

The aim of this work is the detection of solar photovoltaic panels in low-quality satellite photos. It is important to receive the geospatial data (such as country, zip code, street and home number) of installed solar panels, because they are connected directly to the local power. It will be helpful to estimate a power capacity and an energy production using the satellite photos. For this purpose, a Convolutional Neural Network was used. For training and testing dataset consists of 3347 low-quality Google satellite images was used. The experimental results show a high rate accuracy of detection with low rate incorrect classifications of the proposed approach. The proposed approach has enormous implementation and can be improved in future.

Artificial immune system for Android OS

The paper presents and discusses a method of creating a security system for Android operating system. Based on the application of an Artificial Immune System and neural networks we construct the “antivirus” system especially for Android system that can detect and block undesirable and malicious applications. This system can be characterized by self-adaption and self-evolution and can detect even unknown and previously unseen malicious applications.