A Real Time Approach to Strengthen Computer Security By Host Cum Network Agent Based Intrusion Detection System (HCN-AIDS)

2018 ◽  
Vol 6 (7) ◽  
pp. 204-210
Author(s):  
S. K. Tiwari ◽  
D. S. Pandey ◽  
V. Namdeo
Keyword(s):  
Intrusion Detection ◽  
Computer Security ◽  
Real Time ◽  
Intrusion Detection System ◽  
Detection System ◽  
Agent Based

DECISION MAKING FOR NETWORK HEALTH ASSESSMENT IN AN INTELLIGENT INTRUSION DETECTION SYSTEM ARCHITECTURE

2004 ◽  
Vol 03 (02) ◽  
pp. 281-306 ◽  
Author(s):  
AMBAREEN SIRAJ ◽  
RAYFORD B. VAUGHN ◽  
SUSAN M. BRIDGES
Keyword(s):  
Intrusion Detection ◽  
Computer Security ◽  
Intrusion Detection System ◽  
Detection System ◽  
Health Assessment ◽  
Fuzzy Rule ◽  
Fuzzy Cognitive Maps ◽  
Causal Knowledge ◽  
Knowledge Reasoning ◽  
Decision Engine

This paper describes the use of artificial intelligence techniques in the creation of a network-based decision engine for decision support in an Intelligent Intrusion Detection System (IIDS). In order to assess overall network health, the decision engine fuses outputs from different intrusion detection sensors serving as "experts" and then analyzes the integrated information to present an overall security view of the system for the security administrator. This paper reports on the workings of a decision engine that has been successfully embedded into the IIDS architecture being built at the Center for Computer Security Research, Mississippi State University. The decision engine uses Fuzzy Cognitive Maps (FCM)s and fuzzy rule-bases for causal knowledge acquisition and to support the causal knowledge reasoning process.

Adaptive Ensemble Multi-Agent Based Intrusion Detection Model

2010 ◽  
pp. 36-48 ◽  
Author(s):  
Tarek Helmy
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Machine Learning Algorithms ◽  
Agent Based ◽  
Detection Model ◽  
Detection Analysis ◽  
Proposed Model ◽  
Multi Agent

The system that monitors the events occurring in a computer system or a network and analyzes the events for sign of intrusions is known as intrusion detection system. The performance of the intrusion detection system can be improved by combing anomaly and misuse analysis. This chapter proposes an ensemble multi-agent-based intrusion detection model. The proposed model combines anomaly, misuse, and host-based detection analysis. The agents in the proposed model use rules to check for intrusions, and adopt machine learning algorithms to recognize unknown actions, to update or create new rules automatically. Each agent in the proposed model encapsulates a specific classification technique, and gives its belief about any packet event in the network. These agents collaborate to determine the decision about any event, have the ability to generalize, and to detect novel attacks. Empirical results indicate that the proposed model is efficient, and outperforms other intrusion detection models.

SwiftIDS: Real-time intrusion detection system based on LightGBM and parallel intrusion detection mechanism

2020 ◽  
Vol 97 ◽  
pp. 101984 ◽  
Author(s):  
Dongzi Jin ◽  
Yiqin Lu ◽  
Jiancheng Qin ◽  
Zhe Cheng ◽  
Zhongshu Mao
Keyword(s):  
Intrusion Detection ◽  
Real Time ◽  
Intrusion Detection System ◽  
Detection System ◽  
Detection Mechanism
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