Master thesis intrusion detection system

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. Hence, classical detection systems show poor performance in detecting novel attacks.

Although much research has been devoted to improving the performance of intrusion detection systems, few methods can achieve consistently efficient results with the constant changes in network communications.

Master thesis intrusion detection system

Issues arise when attacks are not noticed by an existing IDS because the attack does not fit the pre-defined attack signatures the IDS is implemented to discover. Rigoberto Chinchilla Abstract Cyber Security will always be a subject of discussion for a long time to come.

What is hybrid intrusion detection system

This thesis proposes an intrusion detection system based on modeling distributions of network flow statistics in order to achieve a high detection rate for known and stealthy attacks. This public firewall might empower students and instructors with practical cyber-attacks, detection techniques, prevention techniques, and forensics analysis tools. This thesis explores different up to date techniques and methods for detection and prevention of cyber-attacks. These types of attacks are able to bypass existing IDSs, increase the potential for a web application security breach to occur and not be detected. It may also provide the knowledge required for further research in the field of Cyber Security. This thesis intends to address this problem. Although much research has been devoted to improving the performance of intrusion detection systems, few methods can achieve consistently efficient results with the constant changes in network communications. This aggregated traffic is used to build the distribution of network statistics for the most frequent IPv4 addresses encountered as destination. The obtained probability density functions are learned by the Extreme Learning Machine method which is a single-hidden layer feedforward neural network. The proposed hybrid model for data breach detection benefits organizations by increasing security measures and allowing attacks to be identified in less time and more efficiently. The proposed model aggregates the traffic at the IP subnetwork level using a hierarchical heavy hitters algorithm. Master of Science in Information Technology Theses. In particular, the attacks under study are all web application layer attacks.

The obtained probability density functions are learned by the Extreme Learning Machine method which is a single-hidden layer feedforward neural network. Despite current IDSs capabilities, little research has identified a method to detect all potential attacks on a system.

In this thesis, different sequential and batch learning strategies are proposed in order to analyze the efficiency of this proposed approach. This thesis proposes an intrusion detection system based on modeling distributions of network flow statistics in order to achieve a high detection rate for known and stealthy attacks.

hybrid intrusion detection system: technology and development

These types of attacks are able to bypass existing IDSs, increase the potential for a web application security breach to occur and not be detected. This thesis intends to address this problem.

Hybrid detection

The Open Web Application Security Project OWASP , generally defines web application security violations as unauthorized or unintentional exposure, disclosure, or loss of personal information. Such systems currently rely on either signatures of the attack used for the data breach or changes in the behavior patterns of the system to identify an intruder. Despite current IDSs capabilities, little research has identified a method to detect all potential attacks on a system. The obtained probability density functions are learned by the Extreme Learning Machine method which is a single-hidden layer feedforward neural network. This aggregated traffic is used to build the distribution of network statistics for the most frequent IPv4 addresses encountered as destination. This thesis explores different up to date techniques and methods for detection and prevention of cyber-attacks. Master of Science in Information Technology Theses. 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. Issues arise when attacks are not noticed by an existing IDS because the attack does not fit the pre-defined attack signatures the IDS is implemented to discover. This thesis intends to address this problem. It may also provide the knowledge required for further research in the field of Cyber Security. Recommended Citation Bronte, Robert N.

The proposed model aggregates the traffic at the IP subnetwork level using a hierarchical heavy hitters algorithm.

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"A Framework for Hybrid Intrusion Detection Systems" by Robert N. Bronte