Lessons Learned from Teaching Intrusion Detection and Intrusion Prevention with Snort

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CSMN683: Intrusion Detection, Incident Response, and Computer Forensics: Course Description ,[object Object]

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Intrusion Detection System (IDS) has been an effective way to achieve higher security in detecting malicious activities for the past couple of years. Anomaly detection is an intrusion detection system. Current anomaly detection is often associated with high false alarm rates and only moderate accuracy and detection rates because it’s unable to detect all types of attacks correctly. An experiment is carried out to evaluate the performance of the different machine learning algorithms using KDD-99 Cup and NSL-KDD datasets. Results show which approach has performed better in term of accuracy, detection rate with reasonable false alarm rate.

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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 techniques

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The internet and different computing devices from desktop computers to smartphones have raised many security and privacy concerns, and the need to automate systems that detect attacks on these networks has emerged in order to be able to protect these networks with scale. And while traditional intrusion detection methods may be able to detect previously known attacks, the issue of dealing with new unknown attacks arises and that brings machine learning as a strong candidate to solve these challenges. In this report, we investigate the use of machine learning in detecting network attacks, intrusion detection, by looking at work that has been done in this field. Particularly we look at the work that has been done by Pasocal et al.

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A method and a system for the detection of an intrusion in a computer network compare the network traffic of the computer network at multiple different points in the network. In an uncompromised network the network traffic monitored at these two different points in the network should be identical. A network intrusion detection system is mostly place at strategic points in a network, so that it can monitor the traffic traveling to or from different devices on that network. The existing Software Defined Network SDN proposes the separation of forward and control planes by introducing a new independent plane called network controller. Machine learning is an artificial intelligence approach that focuses on acquiring knowledge from raw data and, based at least in part on the identified flow, selectively causing the packet, or a packet descriptor associated with the packet. The performance is evaluated using the network analysis metrics such as key generation delay, key sharing delay and the hash code generation time for both SDN and the proposed machine learning SDN. Prof P. Damodharan | K. Veena | Dr N. Suguna "Optimized Intrusion Detection System using Deep Learning Algorithm" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-2 , February 2019, URL: https://www.ijtsrd.com/papers/ijtsrd21447.pdf Paper URL: https://www.ijtsrd.com/engineering/other/21447/optimized-intrusion-detection-system-using-deep-learning-algorithm/prof-p-damodharan

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Italo Calvino nelle "Lezioni americane" scrisse: “Se ho incluso la Visibilità nel mio elenco di valori da salvare è per avvertire del pericolo che stiamo correndo di perdere una facoltà umana fondamentale: il potere di mettere a fuoco visioni a occhi chiusi, di pensare per immagini. Penso una possibile pedagogia dell’immaginazione, che si può esercitare solo su se stessi, con metodi inventati volta per volta”. Questo è il metodo Woodys®

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Deep Packet Inspection (DPI) is becoming more widely used in virtually all applications or services like Intrusion Detection System (IDS), which operate with or within a network. DPI analyzes all data present in the packet as it passes an inspection to determine the application transported and protocol. Deep packet inspection typically uses regular expression matching as a core operator. Regular expressions (RegExes) are used to flexibly represent complex string patterns in many applications ranging from network intrusion detection and prevention systems (NIDPSs). Regular expressions represent complex string pattern as attack signatures in DPI. It examine whether a packetÃ¢â‚¬â„¢s payload matches any of a set of predefined regular expressions. There are various techniques developed in DPI for deep packet inspection for regular expression. We survey on these techniques for further improvement in regular expression detection in this paper. In the result we found that it is possible to reduce RegEx transaction memory required in network intrusion detection. We made this survey with possible use of DPI techniques in the wireless network.

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