Detekcija napada zasnovana na veštačkom imunom sistemu

Danijela Protić

doi:10.5937/vojtehg68-27954

Abstract

Introduction/purpose: The artificial immune system is a computational model inspired by the biological or human immune system. Of particular interest in artificial immune systems is the way the human body reacts to new pathogens and adapts to remain immune for a long period after a disease has been combated, which refers to the recognition of known malicious attacks and the way the immune system identifies self-cells not to be reacted to, which refers to the anomaly detection. Methods: Negative selection, positive selection, clonal selection, immune networks, danger theory, and dendritic cell algorithm are presented. Results: A variety of algorithms and models related to artificial immune systems and two classification principles are presented; one based on the detection of a particular attack and the other based on anomaly detection. Conclusion: Artificial immune systems are often used in intrusion detection since they are accurate and fast. Experiments show that the models can be used in both known attack and anomaly detection. Eager machine learning classifiers show better results in the decision, which is an advantage if runtime is not a significant parameter. Dendritic cell and negative selection algorithms show better results for real-time detection.

Highlights

Artificial immune systems (AISs) are computational models inspired by the human immune system (HIS)
In order to map the processes involved in the HIS to AISs, a few considerations have to be taken into account: how to represent antigens and antibodies, what are memory cells, how to calculate affinity, etc
A Java application has four modules: 1) input; two types of files are to be fed into the input module – the self file is used for training and generation of the detector set while the test file uses a packet of normal traffic that is to be monitored, 2) the network converter converts the data into binary strings, 3) the negative selection module generates detectors, and 4) classification

Summary

Introduction

Artificial immune systems (AISs) are computational models inspired by the human immune system (HIS). Of interest is the way the immune system identifies self-cells which are not to be reacted to (anomaly detection). The negative selection algorithm (NSA) consists of two phases: generation of a detector set and monitoring with detection of new instances.

Results

Conclusion