Most current anomaly detection systems employ supervised methods or unsupervised methods Supervised methods rely on labeled training data, however, in practice, this training data is typically expensive to produce In contrast, unsupervised method can work without the need for massive sets of pre-labeled training data, but its accuracy is quite low This paper put forward a semi-supervised cluster algorithm to detect anomalies in network connections, we evaluate our method by performing experiments over network records from the KDD CUP99 data set The result shows that the feasibility of this method is much better than the others

Applying Semi-supervised Cluster Algorithm for Anomaly Detection

Secured data communication over internet and any other network is always under threat of intrusions and misuses. Intrusions pose a serious security threat for the stability and the security of information in a network environment. An intrusion is defined as any set of actions that attempt to compromise the integrity, confidentiality or availability of a resource. It includes attempting to destabilize the network, gaining unauthorized access to files with privileges, or mishandling and misusing of software. Intrusion Detection System (IDS) have become a needful component in terms of computer and network security. The goal of intrusion detection is to monitor network activities automatically, detect malicious attacks and to establish a proper architecture of the computer network security. Fuzzy logic dealing with vagueness and imprecision has a capability to represent imprecise forms of reasoning in areas where firm decisions have to be made in indefinite environments and is found to be appropriate for intrusion detection. This paper surveys the various IDS and the fuzzy approaches to IDS. Keywords: Fuzzy Logic, malicious threats, networks security, intrusion detection

http://www.ijarcs.info/index.php/Ijarcs/article/download/1513/1501

Fuzzy Approach for Intrusion Detection System: A Survey

This article developped a Calibration PC software for Motorcycle Electronically Controlled Engine System based on LabVIEW ,This software can accurately control the calibration parameters of engine working process,including injection pulse width and ignition timing.It is flexible, intuitive and convenient to motorcycle electronic control system’s calibration processing with the software of host computer monitoring by using LabVIEW.

Design of the Calibration PC Software for Motorcycle Electronically Controlled Engine System Based on LabVIEW

In this paper, we present an adaptive anomaly detection framework that is applicable to network-based intrusion detection. Our framework employs fuzzy cluster algorithm to detect anomalies in an online, adaptive fashion without a priori knowledge of the underlying data. We evaluate our method by performing experiments over network records from the KDD CUP99 data set.

A Framework for an Adaptive Anomaly Detection System with Fuzzy Data Mining

An intrusion detection system (IDS) is used to manage network traffic and monitors for suspicious activity and alerts the system or network administrator. One of the major properties of IDS is to respond for anomalous or malicious traffic by taking action such as blocking the user or source IP address from accessing the network. IDS can identify threats in various ways: 1) it detects specific signatures of known threats and protects against malware 2) it detects based on comparing traffic patterns against a baseline and looking for anomalies. 3) There are some IDS that simply generate an alert and 4) Some IDS perform an action or actions in response to a detected threat. In this paper, we have studied different fuzzy approaches for intrusion detection system specifically for anomaly detection system using Fuzzy set theory and we analyze Fuzzy rule and the fitness function of Genetic algorithm for anomaly based attack detection.

/pdf/a-study-on-fuzzy-rules-for-intrusion-detection-system-4jcutz9a7i.pdf

A Study on Fuzzy Rules for Intrusion Detection System

Most current intrusion detection system employ signature-based methods or data mining-based methods which rely on labeled training data, however, in practice, this training data is typically expensive to produce. In contrast, unsupervised anomaly detection has great utility within the context of network intrusion detection system. Such a system can work without the need for massive sets of pre-labeled training data and has the added versatility of being free of the over specialization that comes with systems tailored for specific sets of attacks. Thus, with a system that seeks only to define and categorize normalcy, there is the potential to detect new types of network attacks without any prior knowledge of their existence. This paper discusses the creation of such a system that uses a fuzzy cluster algorithm to detect anomalies in network connections; we evaluate our method by performing experiments over network records from the KDD CUP99 data set.

Applying fuzzy data mining to network unsupervised anomaly detection

Most current intrusion detection system employ signature-based methods that rely on labeled training data, however, in practice, this training data is typically expensive to produce. In contrast, unsupervised anomaly detection has great utility within the context of network intrusion detection system. Such a system can work without the need for massive sets of pre-labeled training data. Thus, with a system that seeks only to define and categorize normalcy, there is the potential to detect new types of network attacks without any prior knowledge of their existence. This paper discusses the creation of such a system that uses Fuzzy ART to detect anomalies in network connections; we evaluate our method by performing experiments over network records from the KDD CUP99 data set.

Application of Fuzzy ART for Unsupervised Anomaly Detection System

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Applying fuzzy data mining to network unsupervised anomaly detection

Application of Fuzzy ART for Unsupervised Anomaly Detection System

A Framework for an Adaptive Anomaly Detection System with Fuzzy Data Mining