Distributed agents model for intrusion detection based on AIS

Authors:
Jin Yang;XiaoJie Liu;Tao Li;Gang Liang;SunJun Liu
Affiliations:
School of Computer Science, Sichuan Normal University, Chengdu 610068, China and School of Computer Science, Sichuan University, Chengdu 610065, China;School of Computer Science, Sichuan University, Chengdu 610065, China;School of Computer Science, Sichuan University, Chengdu 610065, China;School of Computer Science, Sichuan University, Chengdu 610065, China;School of Computer Science, Sichuan University, Chengdu 610065, China
Venue:
Knowledge-Based Systems
Year:
2009

Citing 6
Cited 8

A Quantitative Model of the Security Intrusion Process Based on Attacker Behavior

IEEE Transactions on Software Engineering
Experience with EMERALD to Date

Proceedings of the Workshop on Intrusion Detection and Network Monitoring
Self-Nonself Discrimination in a Computer

SP '94 Proceedings of the 1994 IEEE Symposium on Security and Privacy
Architecture for an Artificial Immune System

Evolutionary Computation
Controlling the heating system of an intelligent home with an artificial immune system

ICARIS'06 Proceedings of the 5th international conference on Artificial Immune Systems
Advances in artificial immune systems

IEEE Computational Intelligence Magazine

Intelligent agent based artificial immune system for computer security--a review

Artificial Intelligence Review
An incremental SVM for intrusion detection based on key feature selection

IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
A cooperative immunological approach for detecting network anomaly

Applied Soft Computing
Multiagent-based dendritic cell algorithm with applications in computer security

ACIIDS'11 Proceedings of the Third international conference on Intelligent information and database systems - Volume Part I
An ensemble design of intrusion detection system for handling uncertainty using Neutrosophic Logic Classifier

Knowledge-Based Systems
Short Communication: An efficient negative selection algorithm with further training for anomaly detection

Knowledge-Based Systems
Host-based intrusion detection systems adapted from agent-based artificial immune systems

Neurocomputing
Executing SQL queries over encrypted character strings in the Database-As-Service model

Knowledge-Based Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

Artificial immune systems (AIS) is a complicated system with the ability of self-adapting, self-learning, self-organizing, parallel processing and distributed coordinating, and it also has the basic function to distinguish self and non-self and clean non-self. One significant feature of the theory immunology is the ability to adapt to changing environments and dynamically learning continuously. Inspired by the theory of artificial immune systems, a novel model of Agents of Network Danger Evaluation is presented. The concepts and formal definitions of immune cells are given, and dynamically evaluative equations for self, antigen, immune tolerance, mature-lymphocyte lifecycle and immune memory are presented, and the hierarchical and distributed management framework of the proposed model are built. Furthermore, the idea of dynamic immunological surveillance period is applied for enhancing the self-learning ability to adapt continuously variety environments. The experimental results show that the proposed model has the features of real-time processing that provide a good solution for network surveillance.