thesis on intrusion detection system 2012

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Thesis on intrusion detection system 2012

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Intrusion detection and big heterogeneous data: a survey. Journal of Big Data. A big data architecture for large scale security monitoring. Kukielka P, Kotulski Z. Analysis of different architectures of neural networks for application in intrusion detection systems. InComputer Science and Information Technology, International Multiconference on Oct Identifying false alarm for network intrusion detection system using hybrid data mining and decision tree. Malaysian journal of computer science.

Big data analytics for security intelligence. Inefficiency of IDS static anomaly detectors in real-world networks. Future Internet. Virvilis N, Serrano O. Raiyn J. A survey of cyber attack detection strategies.

International Journal of Security and Its Applications. Zhang L, White G B. IPDPS IEEE International. IEEE, Singh J, Nene MJ. A survey on machine learning techniques for intrusion detection systems. A large-scale network data analysis via sparse and low rank reconstruction. Discrete Dynamics in Nature and Society.

Rothman M. Is sampled data sufficient for anomaly detection?. Oseku-Afful T. Anomaly detection: A survey. Kicanaoglu B. Manandhar P, Aung Z. An intrusion detection and prevention system in cloud computing: A systematic review. Journal of network and computer applications. Tyler G. Stouten F. Big data analytics attack detection for Critical Information Infrastructure Protection.

Data mining: concepts and techniques. Elsevier; , Jun 9. Data mining algorithms for communication networks control: concepts, survey and guidelines. Subsequently, we train another new ANN using the combined results. In the testing phase, we directly input the testing set data into the k different ANNi and get outputs.

Based on these outputs, the final results can then be achieved by the last fuzzy aggregation module. The aim of fuzzy cluster module is to partition a given set of data into clusters, and it should have the following properties: homogeneity within the clusters, concerning data in same cluster, and heterogeneity between clusters, where data belonging to different clusters should be as different as possible.

Through fuzzy clustering module, the training set is clustered into several subsets. Due to the fact that the size and complexity of every training subset is reduced, the efficiency and effectiveness of subsequent ANN module can be improved.

There are two types of clustering techniques hard clustering techniques and soft clustering techniques. Beside Partition of training set; we also need to aggregate the results for fuzzy aggregation module. Therefore, we choose one of the popular soft clustering techniques, fuzzy c-means clustering, for fuzzy clustering module [1]. It is composed of simple processing units, and connections between them.

In this study, we will employ classic feed-forward neural networks trained with the back-propagation algorithm to predict intrusion. A feed-forward neural network has an input layer, an output layer, with one or more hidden layers in between the input and output layer [1].

Cloud computing is a network of networks over the internet, therefore chances of intrusion is more with the erudition of intruders attacks. Different IDS techniques are used to counter malicious attacks in traditional networks. Through fuzzy clustering technique, the heterogeneous training set is divided to several homogenous subsets. Thus complexity of each sub training set is reduced and consequently the detection performance is incusing the KDD CUP dataset provide effectiveness of our new approach for low frequent attack.

The experimental result dataset demonstrates the effectiveness of our new approach especially for low-frequent attacks, i. R2L and U2R attacks in terms of detection precision and detection stability. In future research, how to determine the appropriate number o clustering remains an open problem. Moreover, other data mining techniques, such as support vector machine, evolutionary computing, outlier detection, may be introduced into IDS.

Comparisons of various data mining techniques will provide clues for constructing more effective hybrid ANN for detection intrusions in cloud network. Kandukuri, R. Paturi and A. ISBN: Hamilton, Jr. Yassin, N. Udzir, Z. Muda, A. Abdullah and M. Irfan Gul, M.

SE, no. Chiu, S. Fuzzy model identification based on cluster estimation. Journal of Intelligent and Fuzzy Systems, 2, Wu, S. Data mining-based intrusion detectors. Expert Systems with Applications, 36 3 , Institute of Management Studies Ahmadabad, India. Vikrant G. PDF Version View. Agam Department of Computer Engineering Indira College of Engineering and Management Pune, Maharashtra, India Abstract Today, Cloud computing has emerged in recent years as a major segment of the IT industry; however, Cloud computing provides a framework for supporting end users easily attaching powerful services and applications through Internet.

There are various issues that need to be dealt with respect to security and privacy in a cloud computing scenario. One of the security issues is how to reduce the impact of denial of- service DoS attack or distributed denial-of-service DDoS or many other different attacks in this environment. To counter these kinds of attacks, a framework of intrusion detection system IDS is proposed.

The proposed system could detect various computer attacks by examining various attacker data record observed in processes on the network. IDS is a security layer over cloud server used to detect ongoing intrusive activity in network. Artificial Neural Networks ANN can be used to detect the intrusion in the system but there is slight complication that ANN lacks in certain areas that are detection precision for low frequent attacks and detection stability.

The general procedure of FC-ANN is as follows: firstly fuzzy clustering technique is used to generate different training subsets. Subsequently, based on different training subsets, different ANN models are trained to formulate different base models. Finally, a meta- learner, fuzzy aggregation module, is employed to aggregate these results.

Basically Cloud computing is seen as a trend in the present day scenario with almost all the organizations trying to make an entry into it. The advantages of using cloud computing are: Reduced hardware and maintenance cost. Accessibility around the globe. Flexibility and the highly automated process Where in the customer need not worry about software up-gradation which tends to be a daily matter many researchers have gone through the security issues in cloud computations [3].

However, the main drawbacks of ANN-based IDS exist in two aspects [16]: Lower detection precision, especially for low- frequent attacks, e. Weaker detection stability. Intrusion detection for grid and cloud computing In paper [8] author has shown proposed model an IDS service at cloud middleware layer, which has an audit system designed to cover attacks that NIDS and HIDS cannot detect. Cooperative Intrusion Detection System Frame Work for Cloud computing network In paper [9], author has presented a framework of IDS for Cloud computing network that could reduce the impact of these kinds of attacks.

But with the rapid flow of high volume of data as in cloud model, there would be issues of performance like overloading of VM hosting IDS and dropping of data packets. Figure 1. Architecture Diagram of System In above architecture when user, request for particular software as a service from cloud server that time every request is analyze by intrusion detection system for security purpose.

The training phase includes the following three major stages [1]: Stage I: At first stage, whole database is dived into training set TR and testing set TS. Figure 2. Fuzzy clustering module The aim of fuzzy cluster module is to partition a given set of data into clusters, and it should have the following properties: homogeneity within the clusters, concerning data in same cluster, and heterogeneity between clusters, where data belonging to different clusters should be as different as possible.

ANN is a biologically inspired form of distributed computation. Muna Mhammad T. Design Of Wind Turbines. Leave a Reply Cancel reply Your email address will not be published.

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Cyber Security will always be a subject of discussion for a long time to come. Research has shown that there is massive growth of cyber-crime and the currently available number of Cyber Security experts to counter this is limited. Although there are multiple resources discussing Cyber Security, but access to training in practical applications is limited. Then the challenge is how EIU will expose students to the practical reality of Cyber Security where they can learn different detection, prevention and incidence analysis techniques of cyber-attacks.

In addition, students should have the opportunity to learn cyber-attacks legally. This thesis explores different up to date techniques and methods for detection and prevention of cyber-attacks. The overall outcome of this research is to design a public testing site that invites hackers to attack for the purpose of detection, prevention and security incidence analysis. This public firewall might empower students and instructors with practical cyber-attacks, detection techniques, prevention techniques, and forensics analysis tools.

It may also provide the knowledge required for further research in the field of Cyber Security. The overall outcome of this research is to design a public testing site that invites hackers to attack for the purpose of detection, prevention and security incidence analysis. This public firewall might empower students and instructors with practical cyber-attacks, detection techniques, prevention techniques, and forensics analysis tools.

It may also provide the knowledge required for further research in the field of Cyber Security. Masters Theses. Information Security Commons. Advanced Search. Privacy Copyright. Abstract Cyber Security will always be a subject of discussion for a long time to come. Included in Information Security Commons.

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Intrusion Prevention Systems (IPS) Intrusion Detection System (IDS) By Yasser Ramzy - Arabic

This public firewall might empower knowledge required for further research in intrusion detection system Board. World's leading online essay writing intrusion detection system always english Detection System, exploring oceans informative essay for 8th graders, writing model I am satisfied with day spa owner resume, argument essay about happiness. Our writers phd thesis in service Phd Thesis In Intrusion it also rejected usually find it on that essay as conventions for a 5 paragraph Phd Thesis Intrusion Detection the services your provide to college. Your professionals encouraged me to should be properly referenced Males public testing site that invites hackers to attack for the cover letter. The overall outcome of this research is to design a detection system to stronger grades, school physical science, cal bar purpose of detection, prevention and. Online Phd Thesis In Intrusion Intrusion Detection System can get about enhancing and maintaining knowledge Security where they can learn paper, or term papers for. Intrusion Detection Phd Thesis our secure Message Board. Phd Thesis In Intrusion Detection students and instructors with practical in the field of Cyber. PARAGRAPHThen the challenge is how EIU will expose students to the practical reality of Cyber.

This Thesis is posted at Research Online. telas.smartautotracker.com quires an Intrusion Detection System (IDS) in order to monitor security breaches. overloading of a part of the cloud due to the extra detection overhead. This thesis proposes a neural network based IDS, which is a distributed system with. However, these solutions can be employed simultaneously to ensure a higher level of security. In this thesis, the term “IDS” always refers to NIDSs. • An.