Forensic Data Mining: Finding Intrusion Patterns in Evidentiary Data

In The extensive growth of computing networks and tools and tricks for intruding into and attacking networks has underscored the importance of intrusion detection in network security. Yet, contemporary intrusion detection systems (IDS) are limiting in that they typically employ a misuse detection strategy, with searches for patterns of program or user behavior that match known intrusion scenarios, or signatures. Accordingly, there is a need for more robust and adaptive methods for designing and updating intrusion detection systems. One promising approach is the use of data mining methods for discovering intrusion patterns. Discovered patterns and profiles can be translated into classifiers for detecting deviations from normal usage patterns. Among promising mining methods are association rules, link analysis, and rule-induction algorithms. Our particular contribution is a unique approach to combining association rules with link analysis and a rule-induction algorithm to augment intrusion detection systems

IDS by Using Data Mining Based on Class-Association-Rule Mining and Genetic Network Programming

Now a day’s security is considered as major topics in networks, since the network has increasing widely day by day. Therefore, intrusion detection systems have paid more awareness, as it has an ability to identify intrusion accesses effectively. All these systems can spot the attacks and behave by trigger different errors .The proposed system includes a data mining method with fuzzy logic and class-association rule mining method which is based on genetic algorithm [1]. As the use of fuzzy logic, the recommend system can able to show the different type of features and also able to keep away from the different problems that are arising in to the suggested system approach. By using Genetic algorithm it is possible to find many rules and regulations and that are use to anomaly detection systems an association-rule-mining is very important technique that is used to find valuable rules and these rules are used by different users, instead of to find all the rules meeting the criteria that are useful for detection. Different results that are experimented with KDD99 [9] Cup database realise that the proposed approach gives more detection rates as compared to crisp data mining. DOI: 10.17762/ijritcc2321-8169.15063

Analysis into developing accurate and efficient intrusion detection approaches

Cyber-security has become more prevalent as more organisations are relying on cyber-enabled infrastructures to conduct their daily actives. Subsequently cybercrime and cyber-attacks are increasing. An Intrusion Detection System (IDS) is a cyber-security tool that is used to mitigate cyber-attacks. An IDS is a system deployed to monitor network traffic and trigger an alert when unauthorised activity has been detected. It is important for IDSs to accurately identify cyber-attacks against assets on cyber-enabled infrastructures, while also being efficient at processing current and predicted network traffic flows. The purpose of the paper is to outline the importance of developing an accurate and effective intrusion detection approach that can be deployed on an IDS. Further research aims to develop a hybrid data mining intrusion detection approach that uses Decision Tree classifications and Association Rules to extract rules using the classified data

A Review on Various Methods of Intrusion Detection System

Detection of Intrusion is an essential expertise business segment as well as a dynamic area of study and expansion caused by its requirement. Modern day intrusion detection systems still have these limitations of time sensitivity. The main requirement is to develop a system which is able of handling large volume of network data to detect attacks more accurately and proactively. Research conducted by on the KDDCUP99 dataset resulted in a various set of attributes for each of the four major attack types. Without reducing the number of features, detecting attack patterns within the data is more difficult for rule generation, forecasting, or classification. The goal of this research is to present a new method that Compare results of appropriately categorized and inaccurately categorized as proportions and the features chosen. Data mining is used to clean, classify and examine large amount of network data. Since a large volume of network traffic that requires processing, we use data mining techniques. Different Data Mining techniques such as clustering, classification and association rules are proving to be useful for analyzing network traffic. This paper presents the survey on data mining techniques applied on intrusion detection systems for the effective identification of both known and unknown patterns of attacks, thereby helping the users to develop secure information systems. Keywords: IDS, Data Mining, Machine Learning, Clustering, Classification DOI: 10.7176/CEIS/11-1-02 Publication date: January 31st 2020

Improving Intrusion Detection System Based on Snort Rules for Network Probe Attacks Detection with Association Rules Technique of Data Mining

The intrusion detection system (IDS) is an important network security tool for securing computer and network systems. It is able to detect and monitor network traffic data. Snort IDS is an open-source network security tool. It can search and match rules with network traffic data in order to detect attacks, and generate an alert. However, the Snort IDS  can detect only known attacks. Therefore, we have proposed a procedure for improving Snort IDS rules, based on the association rules data mining technique for detection of network probe attacks.  We employed the MIT-DARPA 1999 data set for the experimental evaluation. Since behavior pattern traffic data are both normal and abnormal, the abnormal behavior data is detected by way of the Snort IDS. The experimental results showed that the proposed Snort IDS rules, based on data mining detection of network probe attacks, proved more efficient than the original Snort IDS rules, as well as icmp.rules and icmp-info.rules of Snort IDS.  The suitable parameters for the proposed Snort IDS rules are defined as follows: Min_sup set to 10%, and Min_conf set to 100%, and through the application of eight variable attributes. As more suitable parameters are applied, higher accuracy is achieved