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A hybrid intrusion detection system for cloud computing environments

The goal of intrusion detection system (IDS) is to provide another layer of defense against malicious (or unauthorized) uses of computer systems by sensing a misuse or a breach of a security policy and alerting operators to an ongoing attack.

Key words: Intrusion Detection System, Anomaly Detection, Web Server, Attacks, SQLIA, Classification of SQLIA.

[1] K.

Sensor networks have different characteristics and hence security solutions have to be designed with limited usage of computation and resources.

Key words: Wireless Sensor Network, Rule-based & cluster-based intrusion detection, Hybrid, Anomaly detection.

[1] I.

A Data Mining Framework for Constructing Feature and Model for Intrusion Detection System.

A cooperative intrusion detection system for ad hoc networks.

Intrusion detection is the art of detecting computer abuse and any attempt to break the networks.

Simulation results show that the proposed simple analytical forms are quite accurate for different modulation techniques , which lead to the conclusion that BPSK gives the best and ideal performance as compared to other PSK in wireless communications

Key words: AWGN, BPSK, QPSK, 8-PSK, 16-PSK, 32-PSK, BER, OFDM

[1] IEEE Std 802.11a-1999, Supplement to IEEE standard for information technology - telecommunications and information exchange between systems - local and metroplitan area networks - specific requirements.
[2] T.

Patil (2008), "Adaptive Neuro Fuzzy Controller for Process Control System", IEEE Region 10 Colloquium and the Third International Conference on Industrial and Information Systems, December 8 -10.
[2] Jafar Tavoosi, Majid Alaei, Behrouz Jahani, "Temperature Control of Water Bath by using Neuro- Fuzzy Controller", 5thSASTech 2011, Khavaran Higher-education Institute, Mashhad, Iran.

Research in intrusion detection and response - a survey".

Tsai,A Framework of Machine Learning Based Intrusion Detection for Wireless Sensor Networks, IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing,2008.
[2] W.

Vign a, "Intrusion Detection: A Brief History and Overview," Comp uter Society, Vol.

In this paper, we study fuzzy type-ahead search in XML data, a new information-access paradigm in which the system searches XML data on the fly as the user types in query keywords.

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I need good research papers in intrusion detection system

intrusion detection system in manet

I discuss the impact of the misuse on the system and the provide security for each user.

Key words: Global list, Local list, KDD, Distribution Detection, Node Monitor, Intrusion Detection System

[1] Ethereal: a network protocol analyzer.

Quantitative Behavior Based Intrusion Detection System …

Intrusion detection system is an effective security tool that helps to prevent unauthorized access to network resources by analyzing the network traffic.

Quantitative Behavior Based Intrusion Detection System ..

I discuss intrusion detection system for wireless network in which each node monitors the traffic flow on the network and collects relevant statistics about it.

Modeling and simulation of intrusion detection system …

Different algorithms, methods and applications are created and implemented to solve the problem of detecting the attacks in intrusion detection systems.

Intrusion detection and Big Heterogeneous Data: a …

The experimental results demonstrate the image retrieval performance of the proposed method is superior to other methods.

Key words: Image Retrieval, CBIR, Multi-Feature

[1] Barbeau Jerome, Vignes-Lebbe Regine, and Stamon Georges, "A Signature based on Delaunay Graph and Co-occurrence Matrix," Laboratoire Informatique et Systematique, University of Paris, Paris, France, July 2002,.
[2] Sharmin Siddique, "A Wavelet Based Technique for Analysis and Classification of Texture Images," Carleton University, Ottawa, Canada, Proj.

EAACK—A Secure Intrusion-Detection System for …

Multi Layer Percepron (MLP) architecture is used for Intrusion Detection System.

Key words: Artificial Neural Network, Multilayer Perceptron, KDD, Intrusion Detection System, Network Security

[1] James Cannady, "Artificial Neural Networks for Misuse Detection," Proceedings of the 1998 National Information Systems Security Conference (NISSC'98), Arlington, VA, 1998.

EAACK—A Secure Intrusion-Detection System for MANETs ..

Security is a primary concerns when protected communication between mobile nodes in a averse environment is the requirement. MANETs are more susceptible to be attacked as compared to the wired networks. These vulnerabilities are due to the operating principles of the MANET which can not be changed. Securing MANET is equally important as securing fixed wired networks. Certain level of security can be obtained form the existing solutions. However, these solutions are not always necessarily suitable for wireless networks. Several intrusion detection techniques that have been developed for fixed wired network cannot be applied in this new environment. Further, energy is also one of the major issues in MANET as the battery life of the network nodes is limited and once discharged these nodes can not be charged again. Energy plays a vital role in determining the reliability of the network as the lifetime of the network highly depends on the energy status of the nodes in the network. Here we have designed a method to incorporate both these factors i.e., security and energy in order to make the network more reliable and secure. We have merged the IDS with the AODV routing protocol such that there is not need to deploy a separate IDS part on each node. Using a separate IDS take considerable amount of processing power of the nodes as well as consumes energy. Thus, by merging the IDS with the routing algorithm itself we are able to decrease the energy consumption at each node which enhances the lifetime of the entire network. Finally we have applied clustering in order to provide stability to out ad hoc network so that the path determination can be made faster and effective

Fuzzy Based IDS for Black Hole Detection and Prevention …

Miikkulainen, "Intrusion Detection with Neural Networks," AI Approaches to Fraud Detection and Risk Management: Papers from the 1997 AAAI Workshop, Providence, RI, pp.

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