The Development of Agent Information for Intrusion Detection

As the challenges and problems surround intrusion rises rapidly, the intrusion detection system has been gradually developed. Agent-based approach for intrusion detection system has developed from single to multi agent, and later developed mobile agents in order to increase system's capability to face with a more complex challenge and change. A number of studies had been identified that mobile agent can reduce network traffic, however the study related to intrusion detection using static and mobile agent for finding intruder has not been fully achieved.Keywords:  Information, Intrusion, mobile, network

Network Attacks Detection by Hierarchical Neural Network

Intrusion detection is an emerging area of research in the computer security and net-works with the growing usage of internet in everyday life. Most intrusion detection systems (IDSs) mostly use a single classifier algorithm to classify the network traffic data as normal behavior or anomalous. However, these single classifier systems fail to provide the best possible attack detection rate with low false alarm rate. In this paper,we propose to use a hybrid intelligent approach using a combination of classifiers in order to make the decision intelligently, so that the overall performance of the resul-tant model is enhanced. The general procedure in this is to follow the supervised or un-supervised data filtering with classifier or cluster first on the whole training dataset and then the output are applied to another classifier to classify the data. In this re- search, we applied Neural Network with Supervised and Unsupervised Learning in order to implement the intrusion detection system. Moreover, in this project, we used the method of Parallelization with real time application of the system processors to detect the systems intrusions.Using this method enhanced the speed of the intrusion detection. In order to train and test the neural network, NSLKDD database was used. Creating some different intrusion detection systems, each of which considered as a single agent, we precisely proceeded with the signature-based intrusion detection of the network.In the proposed design, the attacks have been classified into 4 groups and each group is detected by an Agent equipped with intrusion detection system (IDS).These agents act independently and report the intrusion or non-intrusion in the system; the results achieved by the agents will be studied in the Final Analyst and at last the analyst reports that whether there has been an intrusion in the system or not. Keywords: Intrusion Detection, Multi-layer Perceptron, False Positives, Signature- based intrusion detection, Decision tree, Nave Bayes Classifie

Multi-paradigm frameworks for scalable intrusion detection

Research in network security and intrusion detection systems (IDSs) has typically focused on small or artificial data sets. Tools are developed that work well on these data sets but have trouble meeting the demands of real-world, large-scale network environments. In addressing this problem, improvements must be made to the foundations of intrusion detection systems, including data management, IDS accuracy and alert volume;We address data management of network security and intrusion detection information by presenting a database mediator system that provides single query access via a domain specific query language. Results are returned in the form of XML using web services, allowing analysts to access information from remote networks in a uniform manner. The system also provides scalable data capture of log data for multi-terabyte datasets;Next, we address IDS alert accuracy by building an agent-based framework that utilizes web services to make the system easy to deploy and capable of spanning network boundaries. Agents in the framework process IDS alerts managed by a central alert broker. The broker can define processing hierarchies by assigning dependencies on agents to achieve scalability. The framework can also be used for the task of event correlation, or gathering information relevant to an IDS alert;Lastly, we address alert volume by presenting an approach to alert correlation that is IDS independent. Using correlated events gathered in our agent framework, we build a feature vector for each IDS alert representing the network traffic profile of the internal host at the time of the alert. This feature vector is used as a statistical fingerprint in a clustering algorithm that groups related alerts. We analyze our results with a combination of domain expert evaluation and feature selection

Enhancing security and dependability of industrial networks with opinion dynamics

Opinion Dynamics poses a novel technique to accurately locate the patterns of an advanced attack against an industrial infrastructure, compared to traditional intrusion detection systems. This distributed solution provides pro table information to identify the most a ected areas within the network, which can be leveraged to design and deploy tailored response mechanisms that ensure the continuity of the service. In this work, we base on this multi-agent collaborative approach to propose a response technique that permits the secure delivery of messages across the network. For such goal, our contribution is twofold: rstly, we rede ne the existing algorithm to assess not only the compromise of nodes, but also the security and quality of service of communication links; secondly, we develop a routing protocol that prioritizes the secure paths throughout the topology considering the information obtained from the detection system.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

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