MFIRE-2: A Multi Agent System for Flow-based Intrusion Detection Using Stochastic Search

Detecting attacks targeted against military and commercial computer networks is a crucial element in the domain of cyberwarfare. The traditional method of signature-based intrusion detection is a primary mechanism to alert administrators to malicious activity. However, signature-based methods are not capable of detecting new or novel attacks. This research continues the development of a novel simulated, multiagent, flow-based intrusion detection system called MFIRE. Agents in the network are trained to recognize common attacks, and they share data with other agents to improve the overall effectiveness of the system. A Support Vector Machine (SVM) is the primary classifier with which agents determine an attack is occurring. Agents are prompted to move to different locations within the network to find better vantage points, and two methods for achieving this are developed. One uses a centralized reputation-based model, and the other uses a decentralized model optimized with stochastic search. The latter is tested for basic functionality. The reputation model is extensively tested in two configurations and results show that it is significantly superior to a system with non-moving agents. The resulting system, MFIRE-2, demonstrates exciting new network defense capabilities, and should be considered for implementation in future cyberwarfare applications

A Multi Agent System for Flow-Based Intrusion Detection

The detection and elimination of threats to cyber security is essential for system functionality, protection of valuable information, and preventing costly destruction of assets. This thesis presents a Mobile Multi-Agent Flow-Based IDS called MFIREv3 that provides network anomaly detection of intrusions and automated defense. This version of the MFIRE system includes the development and testing of a Multi-Objective Evolutionary Algorithm (MOEA) for feature selection that provides agents with the optimal set of features for classifying the state of the network. Feature selection provides separable data points for the selected attacks: Worm, Distributed Denial of Service, Man-in-the-Middle, Scan, and Trojan. This investigation develops three techniques of self-organization for multiple distributed agents in an intrusion detection system: Reputation, Stochastic, and Maximum Cover. These three movement models are tested for effectiveness in locating good agent vantage points within the network to classify the state of the network. MFIREv3 also introduces the design of defensive measures to limit the effects of network attacks. Defensive measures included in this research are rate-limiting and elimination of infected nodes. The results of this research provide an optimistic outlook for flow-based multi-agent systems for cyber security. The impact of this research illustrates how feature selection in cooperation with movement models for multi agent systems provides excellent attack detection and classification

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Optimizing cybersecurity incident response decisions using deep reinforcement learning

The main purpose of this paper is to explore and investigate the role of deep reinforcement learning (DRL) in optimizing the post-alert incident response process in security incident and event management (SIEM) systems. Although machine learning is used at multiple levels of SIEM systems, the last mile decision process is often ignored. Few papers reported efforts regarding the use of DRL to improve the post-alert decision and incident response processes. All the reported efforts applied only shallow (traditional) machine learning approaches to solve the problem. This paper explores the possibility of solving the problem using DRL approaches. The main attraction of DRL models is their ability to make accurate decisions based on live streams of data without the need for prior training, and they proved to be very successful in other fields of applications. Using standard datasets, a number of experiments have been conducted using different DRL configurations The results showed that DRL models can provide highly accurate decisions without the need for prior training

Advances in Data Mining Knowledge Discovery and Applications

Advances in Data Mining Knowledge Discovery and Applications aims to help data miners, researchers, scholars, and PhD students who wish to apply data mining techniques. The primary contribution of this book is highlighting frontier fields and implementations of the knowledge discovery and data mining. It seems to be same things are repeated again. But in general, same approach and techniques may help us in different fields and expertise areas. This book presents knowledge discovery and data mining applications in two different sections. As known that, data mining covers areas of statistics, machine learning, data management and databases, pattern recognition, artificial intelligence, and other areas. In this book, most of the areas are covered with different data mining applications. The eighteen chapters have been classified in two parts: Knowledge Discovery and Data Mining Applications

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

Autonomous decision on intrusion detection with trained BDI agents

In the context of computer security, the first step to respond to an intrusive incident is the detection of such activity in the monitored system. In recent years, research in intrusion detection has evolved to become a multi-discipline task that involves areas such as data mining, decision analysis, agent-based systems or cost–benefit analysis among others. We propose a multiagent IDS that considers decision analysis techniques in order to configure itself optimally according to the conditions faced. This IDS also provides a quantitative measure of the value of the response decision it can autonomously take. Results regarding the well-known 1999 KDD dataset are shown.Publicad

Global and local clustering soft assignment for intrusion detection system: a comparative study

Intrusion Detection System (IDS) plays an important role in computer network defence mechanism against malicious objects. The ability of IDS to detect new sophisticated attacks compared to traditional method such as firewall is important to secure the network. Machine Learning algorithm such as unsupervised learning and supervised learning is capable to solve the problem of classification in IDS. To achieve that, KDD Cup 99 dataset is used in experiments. This dataset contains 5 million instances with 5 different categories which are Normal, DOS, U2R, R2L and Probe. With such a large dataset, the learning process consumes a lot of processing times and resources. Clustering is unsupervised learning method that can be used for organizing data by grouping similar features into same group. In literature, many researchers used global clustering approach whereby all input will be combined and clustered to construct a codebook. However, there is an alternative technique namely local clustering approach whereby the input will be split into 5 different categories and clustered independently to construct 5 different codebooks. The main objective of this research is to compare the classification performance between the global and local clustering approaches. For this purpose, the soft assignment approach is used for indexing on KDD input and SVM for classification. In the soft assignment approach, the smallest distance values are used for attack description and RBF kernel for SVM to classify attack. The results show that the global clustering approach outperforms the local clustering approach for binary classification. It gives 83.0% of the KDD Cup 99 dataset. However, the local clustering approach outperforms the global clustering approach on multi-class classification problem. It gives 60.6% of the KDD Cup 99 dataset