Intrusion Detection System Enhanced by Hierarchical Bidirectional Fuzzy Rule Interpolation

2018 ◽  
Author(s):  
Shangzhu Jin ◽  
Yanling Jiang ◽  
Jun Peng
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Fuzzy Rule

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.

scholarly journals A New Neural Network-Based Intrusion Detection System for Detecting Malicious Nodes in WSNs

2020 ◽  
Vol 1 (3) ◽  
pp. 1-8
Author(s):  
Narmatha C ◽  
Keyword(s):  
Neural Network ◽  
Intrusion Detection ◽  
Key Management ◽  
Intrusion Detection System ◽  
Detection System ◽  
Denial Of Service ◽  
Fuzzy Rule ◽  
Sensor Nodes ◽  
Abnormal Behavior ◽  
Malicious Nodes

The Wireless Sensor Networks (WSNs) are vulnerable to numerous security hazards that could affect the entire network performance, which could lead to catastrophic problems such as a denial of service attacks (DoS). The WSNs cannot protect these types of attacks by key management protocols, authentication protocols, and protected routing. A solution to this issue is the intrusion detection system (IDS). It evaluates the network with adequate data obtained and detects the sensor node(s) abnormal behavior. For this work, it is proposed to use the intrusion detection system (IDS), which recognizes automated attacks by WSNs. This IDS uses an improved LEACH protocol cluster-based architecture designed to reduce the energy consumption of the sensor nodes. In combination with the Multilayer Perceptron Neural Network, which includes the Feed Forward Neutral Network (FFNN) and the Backpropagation Neural Network (BPNN), IDS is based on fuzzy rule-set anomaly and abuse detection based learning methods based on the fugitive logic sensor to monitor hello, wormhole and SYBIL attacks.

Fuzzy Rule-Based Layered Classifier and Entropy-Based Feature Selection for Intrusion Detection System

2021 ◽  
pp. 289-309
Author(s):  
Devaraju Sellappan ◽  
Ramakrishnan Srinivasan
Keyword(s):  
Feature Selection ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Fuzzy Rule ◽  
Intrusion Detection Systems ◽  
Rule Based ◽  
Detection Systems ◽  
Positive Rate ◽  
Rule Based Classifier

Intrusion detection systems must detect the vulnerability consistently in a network and also perform efficiently with the huge amount of traffic. Intrusion detection systems must be capable of detecting emerging and proactive threats in the networks. Various classifiers are used to classify the threats as normal or intrusive by supervising the system activity. In this chapter, layered fuzzy rule-based classifier is proposed to detect the various intrusions, and fuzzy entropy-based feature selection is proposed to identify the relevant features. Layered fuzzy rule-based classifier is proposed to improve the performance of the intrusion detection system. KDD dataset contains various attacks; these attacks are grouped into four classes, namely Denial-of-Service (DoS), Probe, Remote-to-Local (R2L), and User-to-Root (U2R). Real-time dataset is also considered in this research. Experimental result shows that the proposed method provides good detection rate, minimizes the false positive rate, and less computational time.

Intrusion detection system using a new fuzzy rule-based classification system based on genetic algorithm

2021 ◽  
pp. 1-7
Author(s):  
Zahra Asghari Varzaneh ◽  
Marjan Kuchaki Rafsanjani
Keyword(s):  
Genetic Algorithm ◽  
Intrusion Detection ◽  
Classification System ◽  
Intrusion Detection System ◽  
Computer Network ◽  
Detection System ◽  
Fuzzy Rule ◽  
Classification Systems ◽  
Classification Rate ◽  
Rule Based

Intrusion can compromise the integrity, confidentiality, or availability of a computer system. Intrusion Detection System (IDS) is a type of security software designed to monitor network traffic and identify network intrusions. In this paper, A Fuzzy Rule – Based classification system is used to detect intrusion in a computer network. In order to improve the classification rate, a new method is proposed based on Genetic Algorithm (GA) for rule weights specification. The proposed method is tested on KDD99 dataset. Experimental results show the proposed method improves the performance of the fuzzy rule-based classification systems in terms of detection rate and false alarm rate.

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