Results 1 - 10 of 939

Mining Concept-Drifting Data Streams Using Ensemble Classifiers

by Haixun Wang, Wei Fan, Philip S. Yu, Jiawei Han , 2003
"... Recently, mining data streams with concept drifts for actionable insights has become an important and challenging task for a wide range of applications including credit card fraud protection, target marketing, network intrusion detection, etc. Conventional knowledge discovery tools are facing two ch ..."
Abstract - Cited by 280 (37 self) - Add to MetaCart
sequential chunks of the data stream. The classifiers in the ensemble are judiciously weighted based on their expected classification accuracy on the test data under the time-evolving environment. Thus, the ensemble approach improves both the efficiency in learning the model and the accuracy in performing

Intrusion detection with unlabeled data using clustering

by Leonid Portnoy, Eleazar Eskin, Sal Stolfo - In Proceedings of ACM CSS Workshop on Data Mining Applied to Security (DMSA-2001 , 2001
"... Abstract Intrusions pose a serious security risk in a network environment. Although systems can be hardened against many types of intrusions, often intrusions are successful making systems for detecting these intrusions critical to the security of these system. New intrusion types, of which detectio ..."
Abstract - Cited by 191 (6 self) - Add to MetaCart
very expensive. We present a new type of clustering-based intrusion detection algorithm, unsupervised anomaly detection, which trains on unlabeled data in order to detect new intrusions. In our system, no manually or otherwise classified data is necessary for training. Our method is able to detect many

An Efficient Local Region and Clustering-Based Ensemble System for Intrusion Detection

by Huu Hoa Nguyen, Nouria Harbi, Jérôme Darmont
"... The dramatic proliferation of sophisticated cyber attacks, in conjunction with the ever growing use of Internet-based services and applications, is nowadays becoming a great concern in any organization. Among many efficient security solutions proposed in the literature to deal with this evolving thr ..."
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threat, ensemble approaches, a particular family of data mining, have proven very successful in designing high performance intrusion detection systems (IDSs) resting on the mutual combination of multiple classifiers. However, the strength of ensemble systems depends heavily on the methods to generate

Classification, Clustering And Intrusion Detection System

by Manish Joshi
"... Cluster analysis or clustering is the task of assigning a set of objects into groups (called clusters) so that the objects in the same cluster are more similar (in some sense or another) to each other than to those in other clusters. Classification and clustering techniques in data mining are useful ..."
Abstract - Cited by 2 (0 self) - Add to MetaCart
that attempt to compromise the confidentiality, integrity or availability of a resource. Intrusion detection systems are software systems for identifying the deviations from the normal behavior and usage of the system. They detect attacks using the data mining techniques-classification and clustering

A line in the sand: a wireless sensor network for target detection, classification, and tracking

by A. Arora , P. Dutta , S. Bapat , V. Kulathumani , H. Zhang , V. Naik, V. Mittal , H. Cao , M. Demirbas , M. Gouda , Y. Choi , T. Herman , et al. - COMPUTER NETWORKS , 2004
"... Intrusion detection is a surveillance problem of practical import that is well suited to wireless sensor networks. In this paper, we study the application of sensor networks to the intrusion detection problem and the related problems of classifying and tracking targets. Our approach is based on a de ..."
Abstract - Cited by 272 (41 self) - Add to MetaCart
Intrusion detection is a surveillance problem of practical import that is well suited to wireless sensor networks. In this paper, we study the application of sensor networks to the intrusion detection problem and the related problems of classifying and tracking targets. Our approach is based on a

ENSEMBLE DESIGN FOR INTRUSION DETECTION SYSTEMS

by T. Subbulakshmi, A. Ramamoorthi, Dr. S. Mercy Shalinie
"... Intrusion Detection problem is one of the most promising research issues of Information Security. The problem provides excellent opportunities in terms of providing host and network security. Intrusion detection is divided into two categories with respect to the type of detection. Misuse detection a ..."
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and Anomaly detection. Intrusion detection is done using rule based, Statistical, and Soft computing techniques. The rule based measures provides better results but the extensibility of the approach is still a question. The statistical measures are lagging in identifying the new types of attacks. Soft

A Cooperative Intrusion Detection System for Ad Hoc Networks

by Yi-an Huang , 2003
"... Mobile ad hoc networking (MANET) has become an exciting and important technology in recent years because of the rapid proliferation of wireless devices. MANETs are highly vulnerable to attacks due to the open medium, dynamically changing network topology, cooperative algorithms, lack of centralized ..."
Abstract - Cited by 193 (3 self) - Add to MetaCart
on attack types and sources. For several well-known attacks, we can apply a simple rule to identify the attack type when an anomaly is reported. In some cases, these rules can also help identify the attackers. We address the run-time resource constraint problem using a cluster-based detection scheme where

Design Hybrid method for intrusion detection using Ensemble cluster classification and SOM network

by Deepak Rathore, Anurag Jain
"... In current scenario of internet technology security is big challenge. Internet network threats by various cyber-attack and loss the system data and degrade the performance of host computer. In this sense intrusion detection are challenging field of research in concern of network security based on fi ..."
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on firewall and some rule based detection technique. In this paper we proposed an Ensemble Cluster Classification technique using som network for detection of mixed variable data generated by malicious software for attack purpose in host system. In our methodology SOM network control the iteration of distance

An Ensemble Classification Approach for Intrusion Detection

by Riyad. A. M
"... Increased cyber attacks in various forms compel everyone to implement effective intrusion detection systems for protecting their information wealth. From last two decades, there has been extensive research going on in intrusion detection system development using various techniques. But, designing de ..."
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detection systems producing maximum accuracy with minimum false positive is yet a challenging task for the research community. Ensemble method is one of the major developments in the field of machine learning. In this research work, new ensemble classification method is proposed from different classifiers

Intrusion Detection Based On Clustering Algorithm

by Nadya El Moussaid, Ahmed Toumanari, Maryam Elazhari
"... Abstract- The traditional Intrusion detection systems have been used long time ago, namely Anomaly-Based detection and Signature-based detection but have many drawbacks that limit their performance. Consequently the main goal of this paper is to use data mining techniques including classification u ..."
Abstract - Cited by 1 (1 self) - Add to MetaCart
Abstract- The traditional Intrusion detection systems have been used long time ago, namely Anomaly-Based detection and Signature-based detection but have many drawbacks that limit their performance. Consequently the main goal of this paper is to use data mining techniques including classification
Results 1 - 10 of 939