A virus detection system based on artificial immune system

Proceedings of the International Conference on IT …, 2012

Using artificial immune system techniques for malware detection has two major benefits. First, increasing the ability to come over some of the traditional detector's drawbacks, like dealing with the new and polymorphic malware and the increased number of false alarms caused by wrong decision. Second take advantages of the capabilities to learn, adapt, self-tolerance and memories actions, which make it a good example that we can take for solving some major problems in many fields, including the problem of malware detection in computer security which suffering from the rapid increasing in the malware and the problem of false positive alarms. In this paper, we try to highlight the recent techniques applied in malware detection using the artificial immune system from two points of view: self-nonself theory, danger theory.

International Journal of Computing, 2014

This paper presents an approach for solving unknown computer viruses detection problem based on the Artificial Immune System (AIS) method, where immune detectors represented neural networks. 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 (BIS) and comparative analysis of unknown computer viruses detection for different antivirus software and our model are presented.

2009

Abstract As viruses become more complex, existing antivirus methods are inefficient to detect various forms of viruses, especially new variants and unknown viruses. Inspired by immune system, a hierarchical artificial immune system (AIS) model, which is based on matching in three layers, is proposed to detect a variety of forms of viruses. In the bottom layer, a non-stochastic but guided candidate virus gene library is generated by statistical information of viral key codes.

The protection against computer viruses is becoming increasingly difficult, viruses are becoming a real risk on every one who uses computers, especially large companies and institutions that deal with huge and sensitive data files. The viruses are becoming more intelligent day after day, which has made the anti-viruses mission more complicated. Detecting viruses has been considered a hot and important topic. In this research we are developing software based on the mechanics of artificial immune systems, in particular based on colonial selection. The T-cells and the B-cells evolution and behavior is modeled, its ability to build its own expert system is utilized to build an application that can fight computer viruses. The Evolutionary Programming Algorithm (EP) is used to optimize the initially extracted antivirus patterns of the artificial immune system (AIS). We use the EP as the optimizer algorithm in benchmark learning and testing environments. Virtual environments of different groups of viruses and infected files are used. The virtual environment provides the flexibility and the scalability needed to do simulations. The system proposed in this paper, is adopting simple version of colonial selection. The developed Evolutionary Programming Immune System (EP-IMMUN) is using the EP to optimize immature antiviruses (lymphocytes). It has been proved that the performance of the system was leveraged with this process. The paper concludes that the developed algorithm (EP-IMMUN), which is created to detect viruses, is competitive, and can be relied on. The developed algorithm can be utilized to be applied on other types of malware that have signatures.

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.

Applied Soft Computing, 2013

This paper presents a novel approach for computer viruses detection based on modeling the structures and dynamics of real life paradigm that exists in the bodies of all living creatures. It aims to develop an algorithm based on the concept of the artificial immune system (AIS) for the purpose of detecting viruses. The algorithm is called Virus Detection Clonal algorithm (VDC), and it is derived from the clonal selection algorithm. The VDC algorithm consists of three basic steps: cloning, hyper-mutation and stochastic reselection. In later stage, the developed VDC algorithm is subjected to validation, which consists of two phases; learning and testing. Two main parameters are determined; one of them is setting the number of signatures per clone (Fat), while the other defines the hypermutation probability (Pm). Later on, the Genetic Algorithm (GA) is used as a tool, to improve the developed algorithm by searching the values of the main parameters (Fat and Pm) to reproduce better results. The results have shown that the detection rate of viruses, by using the developed algorithm, is 94.4%, whereas the detection rate of false positives has reached 0%. These percentages indicate that the VDC algorithm is sufficient and usable in this field. Moreover, the results of employing the GA to optimize the VDC algorithm have shown an improvement in the detection speed of the algorithm.

This paper presents a structure, learning rule and functioning of immune detectors based on artificial neural network. Neuronet immune detectors are key elements of neuronet artificial immune system for malware detection. Combinations of artificial immune system method and artificial neural network methot make it possible to construct security system of next generation.

International Journal of Engineering Sciences & Research Technology, 2014

Artificial immune systems (AIS) are a class of computationally intelligent systems which consider many properties of natural immune system .Several AIS are widely used in different application areas such as classification, clustering, web mining, virus detection, learning, image processing, robotics control, bio-informatics and anomaly detection. Among this classification and clustering are widely used areas. Most of the the artificial immune system used in the classification and clustering area make use some key features of AIS such as feature extraction, recognition and learning. This paper gives an effective survey aboutartificial immune systems which are used in the classification and clustering areasand also make use of the features such as feature selection, pattern recognition and machine learning.

International Journal of Engineering Sciences & Research Technology, 2012

International Journal of Computer Applications

The inspiration of framing the artificially developed immune system (AIS) is done through the biological immune system which compromise of signified information processing and self-adapting system. Since it originated in the 1990s, the branch of AIS gets a significant success in the field of Computational Intelligence. Present paper insights major works in the area of AIS and explore current advancements in applied system since past years. It has been observed that the particular research focused on three major considerable algorithms of AIS: (1) clonal selection algorithms (2) negative selection algorithm (3) artificial immune networks. However, computer scientists and engineers are motivated by the biological immune system to evolve new models and problem solving approaches. Developed AIS applications in extensive amount have received a lot of researcher's attention who were planning to establish models based on immune system and techniques in order to provide solutions for complicated problems of engineering. This paper presents a survey of current models of AIS and its algorithms.