A Study on Data Mining Based Intrusion Detection System

In recent years security has remained unsecured for computers as well as data network systems. Intrusion detecting system used to safeguard the data confidentiality, integrity and system availability from various types of attacks. Data mining techniques that can be applied to intrusion detection system to detect normal and abnormal behavior patterns. This paper studies nature of network attacks and the current trends of data mining based intrusion detection techniqu

Intrusion Detection System with Data Mining Approach: A Review

Despite of growing information technology widely, security has remained one challenging area for computers and networks. Recently many researchers have focused on intrusion detection system based on data mining techniques as an efficient strategy. The main problem in intrusion detection system is accuracy to detect new attacks therefore unsupervised methods should be applied. On the other hand, intrusion in system must be recognized in realtime, although, intrusion detection system is also helpful in off-line status for removing weaknesses of network2019;s security. However, data mining techniques can lead us to discover hidden information from network2019;s log data. In this survey, we try to clarify: first,the different problem definitions with regard to network intrusion detection generally; second, the specific difficulties encountered in this field of research; third, the varying assumptions, heuristics, and intuitions forming the basis of erent approaches; and how several prominent solutions tackle different problems

A Survey on Attacks and Advances of Intrusion Detection Systems

Now day’s information of an organization floating over the internet that increases the traffic on the network as well as threats from attackers. To protect these sensitive material Intrusion Detection System (IDS) is situated in the scheme. It is an application software program or hardware mechanism that compacts with assaults by assembling information from a mixture of systems and network resources, then analyzing indications of defense dilemmas. Network Intrusion Detection (NID) is a method that efforts to determine unauthorized entrance to a network through analyzing traffic on the network. There are a variety of advances of intrusion detection, for instance Data Mining, Pattern Matching, Machine Learning and Measure Based Methods. This survey paper aims towards the proper learning of intrusion detection system with the intention that researchers could create employ of it and discover the new methods towards intrusions. Keywords: Intrusion Detection System, Data Mining, Pattern Matching, Anomaly detection, misuse detection, Machine Learning

Cost-based Modeling for Fraud and Intrusion Detection: Results from the JAM Project

We describe the results achieved using the JAM distributed data mining system for the real world problem of fraud detection in financial information systems. For this domain we provide clear evidence that state-of-the-art commercial fraud detection systems can be substantially improved in stopping losses due to fraud by combining multiple models of fraudulent transaction shared among banks. We demonstrate that the traditional statistical metrics used to train and evaluate the performance of learning systems (i.e. statistical accuracy or ROC analysis) are misleading and perhaps inappropriate for this application. Cost-based metrics are more relevant in certain domains, and defining such metrics poses significant and interesting research questions both in evaluating systems and alternative models, and in formalizing the problems to which one may wish to apply data mining technologies. This paper also demonstrates how the techniques developed for fraud detection can be generalized and applied to the important area of intrusion detection in networked information systems. We report the outcome of recent evaluations of our system applied to tcpdump network intrusion data specifically with respect to statistical accuracy. This work involved building additional components of JAM that we have come to call, MADAM ID (Mining Audit Data for Automated Models for Intrusion Detection). However, taking the next step to define cost-based models for intrusion detection poses interesting new research questions. We describe our initial ideas about how to evaluate intrusion detection systems using cost models learned during our work on fraud detection

An Efficient Fuzzy Clustering-Based Approach for Intrusion Detection

The need to increase accuracy in detecting sophisticated cyber attacks poses a great challenge not only to the research community but also to corporations. So far, many approaches have been proposed to cope with this threat. Among them, data mining has brought on remarkable contributions to the intrusion detection problem. However, the generalization ability of data mining-based methods remains limited, and hence detecting sophisticated attacks remains a tough task. In this thread, we present a novel method based on both clustering and classification for developing an efficient intrusion detection system (IDS). The key idea is to take useful information exploited from fuzzy clustering into account for the process of building an IDS. To this aim, we first present cornerstones to construct additional cluster features for a training set. Then, we come up with an algorithm to generate an IDS based on such cluster features and the original input features. Finally, we experimentally prove that our method outperforms several well-known methods.Comment: 15th East-European Conference on Advances and Databases and Information Systems (ADBIS 11), Vienna : Austria (2011

Wireless Intrusion Detection System Based on Data Mining

S rozšířením bezdrátových sítí se bezpečnost v těchto sítích stává vážným problémem. Tato práce proto představuje detekční systém pro bezdrátové sítě, který využívá dvě neuronové sítě k rozeznávání vzorů útoků v rámci zachycené komunikace. Jako řešení problému vysoké míry falešných poplachů představuje tato práce právě metodu využití těchto dvou neuronových sítí.Widespread use of wireless networks has made security a serious issue. This thesis proposes misuse based intrusion detection system for wireless networks, which applies artificial neural network to captured frames for purpose of anomalous patterns recognition. To address the problem of high positive alarm rate, this thesis presents a method of applying two artificial neural networks.

Intrusion Detection Using Self-Training Support Vector Machines

Intrusion is broadly defined as a successful attack on a network. Intrusion Detection System (IDS) is a software tool used to detect unauthorized access to a computer system or network. It is a dynamic monitoring entity that complements the static monitoring abilities of a firewall. Data Mining techniques provide efficient methods for the development of IDS. The idea behind using data mining techniques is that they can automate the process of creating traffic models from some reference data and thereby eliminate the need of laborious manual intervention. Such systems are capable of detecting not only known attacks but also their variations.Existing IDS technologies, on the basis of detection methodology are broadly classified as Misuse or Signature Based Detection and Anomaly Detection Based System. The idea behind misuse detection consists of comparing network traffic against a Model describing known intrusion. The anomaly detection method is based on the analysis of the profiles that represent normal traffic behavior. Semi-Supervised systems for anomaly detection would reduce the demands of the training process by reducing the requirement of training labeled data. A Self Training Support Vector Machine based detection algorithm is presented in this thesis. In the past, Self-Training of SVM has been successfully used for reducing the size of labeled training set in other domains. A similar method was implemented and results of the simulation performed on the KDD Cup 99 dataset for intrusion detection show a reduction of upto 90% in the size of labeled training set required as compared to the supervised learning techniques

Anomaly Intrusion Detection based on Concept Drift

Nowadays, security on the internet is a vital issue and therefore, intrusion detection is one of the major research problems for networks that defend external attacks. Intrusion detection is a new approach for providing security in existing computers and data networks. An Intrusion Detection System is a software application that monitors the system for malicious activities and unauthorized access to the system. An easy accessibility condition causes computer networks vulnerable against the attack and several threats from attackers. Intrusion Detection System is used to analyze a network of interconnected systems for avoiding uncommon intrusion or chaos. The intrusion detection problem is becoming a challenging task due to the increase in computer networks since the increased connectivity of computer systems gives access to all and makes it easier for hackers to avoid their traces and identification. The goal of intrusion detection is to identify unauthorized use, misuse and abuse of computer systems. This project focuses on algorithms: (i) Concept Drift based ensemble Incremental Learning approach for anomaly intrusion detection, and (ii) Diversity and Transfer-based Ensemble Learning. These are highly ranked anomaly detection models. We study and compare both learning models. The Network Security Laboratory-Knowledge Discovery and Data Mining (NSL-KDD99) dataset have been used for training and to detect the misuse activities