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

A survey of intrusion detection techniques in Cloud

Cloud computing provides scalable, virtualized on-demand services to the end users with greater flexibility and lesser infrastructural investment. These services are provided over the Internet using known networking protocols, standards and formats under the supervision of different managements. Existing bugs and vulnerabilities in underlying technologies and legacy protocols tend to open doors for intrusion. This paper, surveys different intrusions affecting availability, confidentiality and integrity of Cloud resources and services. It examines proposals incorporating Intrusion Detection Systems (IDS) in Cloud and discusses various types and techniques of IDS and Intrusion Prevention Systems (IPS), and recommends IDS/IPS positioning in Cloud architecture to achieve desired security in the next generation networks

Statistical and fuzzy approach for database security

A new type of database anomaly is described by addressing the concept of Cumulated Anomaly in this paper. Dubiety-Determining Model (DDM), which is a detection model basing on statistical and fuzzy set theories for Cumulated Anomaly, is proposed. DDM can measure the dubiety degree of each database transaction quantitatively. Software system architecture to support the DDM for monitoring database transactions is designed. We also implemented the system and tested it. Our experimental results show that the DDM method is feasible and effective

A dubiety-determining based model for database cumulated anomaly intrusion

The concept of Cumulated Anomaly (CA), which describes a new type of database anomalies, is addressed. A typical CA intrusion is that when a user who is authorized to modify data records under certain constraints deliberately hides his/her intentions to change data beyond constraints in different operations and different transactions. It happens when some appearing to be authorized and normal transactions lead to certain accumulated results out of given thresholds. The existing intrusion techniques are unable to deal with CAs. This paper proposes a detection model, Dubiety-Determining Model (DDM), for Cumulated Anomaly. This model is mainly based on statistical theories and fuzzy set theories. It measures the dubiety degree, which is presented by a real number between 0 and 1, for each database transaction, to show the likelihood of a transaction to be intrusive. The algorithms used in the DDM are introduced. A DDM-based software architecture has been designed and implemented for monitoring database transactions. The experimental results show that the DDM method is feasible and effective

AI Solutions for MDS: Artificial Intelligence Techniques for Misuse Detection and Localisation in Telecommunication Environments

This report considers the application of Articial Intelligence (AI) techniques to the problem of misuse detection and misuse localisation within telecommunications environments. A broad survey of techniques is provided, that covers inter alia rule based systems, model-based systems, case based reasoning, pattern matching, clustering and feature extraction, articial neural networks, genetic algorithms, arti cial immune systems, agent based systems, data mining and a variety of hybrid approaches. The report then considers the central issue of event correlation, that is at the heart of many misuse detection and localisation systems. The notion of being able to infer misuse by the correlation of individual temporally distributed events within a multiple data stream environment is explored, and a range of techniques, covering model based approaches, `programmed' AI and machine learning paradigms. It is found that, in general, correlation is best achieved via rule based approaches, but that these suffer from a number of drawbacks, such as the difculty of developing and maintaining an appropriate knowledge base, and the lack of ability to generalise from known misuses to new unseen misuses. Two distinct approaches are evident. One attempts to encode knowledge of known misuses, typically within rules, and use this to screen events. This approach cannot generally detect misuses for which it has not been programmed, i.e. it is prone to issuing false negatives. The other attempts to `learn' the features of event patterns that constitute normal behaviour, and, by observing patterns that do not match expected behaviour, detect when a misuse has occurred. This approach is prone to issuing false positives, i.e. inferring misuse from innocent patterns of behaviour that the system was not trained to recognise. Contemporary approaches are seen to favour hybridisation, often combining detection or localisation mechanisms for both abnormal and normal behaviour, the former to capture known cases of misuse, the latter to capture unknown cases. In some systems, these mechanisms even work together to update each other to increase detection rates and lower false positive rates. It is concluded that hybridisation offers the most promising future direction, but that a rule or state based component is likely to remain, being the most natural approach to the correlation of complex events. The challenge, then, is to mitigate the weaknesses of canonical programmed systems such that learning, generalisation and adaptation are more readily facilitated