Performance Study of Distributed Multi-Agent Intrusion Detection System

Traditional Intrusion Detection System (IDS) based on hosts or networks no longer meets the security requirements in today’s network environment due to the increasing complexity and distributivity. A multi-agent distributed IDS model, enhanced with a method of computing its statistical values of performance is presented. This model can accomplish not only distributed information collection, but also distributed intrusion detection and real-time reaction. Owing to prompt reaction and openness, it can detect intrusion behavior of both known and unknown sources. According to preliminary tests, the accuracy ratio of intrusion detection is higher than 92% on the average.     

Immune based computer virus detection approaches

The computer virus is considered one of the most horrifying threats to the security of computer systems worldwide.The rapid development of evasion techniques used in virus causes the signature based computer virus detection techniques to be ineffective.Many novel computer virus detection approaches have been proposed in the past to cope with the ineffectiveness,mainly classified into three categories: static,dynamic and heuristics techniques.As the natural similarities between the biological immune system(BIS),computer security system(CSS),and the artificial immune system(AIS) were all developed as a new prototype in the community of anti-virus research.The immune mechanisms in the BIS provide the opportunities to construct computer virus detection models that are robust and adaptive with the ability to detect unseen viruses.In this paper,a variety of classic computer virus detection approaches were introduced and reviewed based on the background knowledge of the computer virus history.Next,a variety of immune based computer virus detection approaches were also discussed in detail.Promising experimental results suggest that the immune based computer virus detection approaches were able to detect new variants and unseen viruses at lower false positive rates,which have paved a new way for the anti-virus research.     

A neuro-genetic based short-term forecasting framework for network intrusion prediction system

Information systems are one of the most rapidly changing and vulnerable systems, where security is a major issue. The number of security-breaking attempts originating inside organizations is increasing steadily. Attacks made in this way, usually done by ＂authorized＂ users of the system, cannot be immediately traced. Because the idea of filtering the traffic at the entrance door, by using firewalls and the like, is not completely successful, the use of intrusion detection systems should be considered to increase the defense capacity of an information system. An intrusion detection system （IDS） is usually working in a dynamically changing environment, which forces continuous tuning of the intrusion detection model, in order to maintain sufficient performance. The manual tuning process required by current IDS depends on the system operators in working out the tuning solution and in integrating it into the detection model. Furthermore, an extensive effort is required to tackle the newly evolving attacks and a deep study is necessary to categorize it into the respective classes. To reduce this dependence, an automatically evolving anomaly IDS using neuro-genetic algorithm is presented. The proposed system automatically tunes the detection model on the fly according to the feedback provided by the system operator when false predictions are encountered. The system has been evaluated using the Knowledge Discovery in Databases Conference （KDD 2009） intrusion detection dataset. Genetic paradigm is employed to choose the predominant features, which reveal the occurrence of intrusions. The neuro-genetic IDS （NGIDS） involves calculation of weightage value for each of the categorical attributes so that data of uniform representation can be processed by the neuro-genetic algorithm. In this system unauthorized invasion of a user are identified and newer types of attacks are sensed and classified respectively by the neuro-genetic algorithm. The experimental results obtained in this work show that the system achieves improvement in terms of misclassification cost when compared with conventional IDS. The results of the experiments show that this system can be deployed based on a real network or database environment for effective prediction of both normal attacks and new attacks.     

Research on an improved wireless network intrusion detection algorithm

Data mining technology is applied to the network intrusion detection, and precision of the detection will be improved by the superiority of data mining. This paper, first analyzes the method of wireless network intrusion detection, presents a wireless network intrusion detection algorithm based on association rule mining. The application of fuzzy association rules in the wireless network intrusion detection is mainly discussed; a comparative analysis with the classical algorithm Apriori is made by experiment. The result shows that the fuzzy rule mining algorithm is more convenient than Apriori algorithm.     

NRCE： A New Random Cluster Election Algorithm

The Intrusion Detection System （IDS） is a secure solution for Mobile Ad Hoc Networks （MANET）. Cluster management can improve cooperating detection ability of mobile nodes. In this paper, a New Random Cluster Election （NRCE） algorithm, which is applied to the intrusion detection system for MANET, is proposed based on analyzing some popular cluster algorithms. NREC considering security and fairness adequately is working in a distrustful mode where mobile nodes distrust each other and the process of electing clusterhead is random. It is a new method for cluster management of IDS for MANET.     

Probabilistic Neural Networks based network security management

Intrusion Detection System （IDS） is one of the main tools in computer and network management, we consider intrusion detection using probabilistic neural networks. An ensemble of Probabilistic Neural Networks （PNN） is trained with Adaptive Boost to classify the detected event as normal or intrusive. We use Hamming distance kernels for PNN and find them superior to Euclidean distance kernels for this kind of detected event.     

静态灰度图像中的人脸检测方法综述

In recent twenty years,the technique of face detection and face recognition,as one of the important research area of computer vision and image understanding,attracts more and more attenion.In general,face detection in graylevel still images is more difficualt than that in color images.Therefore this paper briefly surveys this raes and indicates some issues for exploration.     

A Simulation Environment for Intrusion Detection System in IEC 61850 Based Substation Automation System

Greater complexity and interconnectivity across systems embracing electrical power technologies has meant that cyber-security issues have attracted significant attention. In this paper a simulation environment for intrusion detection system in IEC 61850 standard-based substation automation system is provided to test simulated attacks on IEDs （intelligent electronic devices）. Intrusion detection is the process of detecting malicious attacker, so it is an effective and mature security mechanism to protect electrical facility. However, it is not harnessed when securing IEC 61850 automated substation. To prove the detection capability of the system testing environment was developed to analyze and test attacks simulated with different test cases. It shows that the simulation environment works accordingly to various network traffic scenarios and eventually proves the functionality of intrusion detection system to be later deployed in the real IEC 61850 based substation automation system site.     

安全操作系统中基于客体的保护机制

Security of operation system is the basis of protecting computer system against attack. To resolve more and more problem in security area ,we need an operation system of great security. That require we find an effective method to develop an security-enhanced operation system to meet these needs. Access control is often used in modern operation system. It is based on identity affirm and enforces control to the resources that are required by the identify. In this oaoer we mainly discuss security nolicies based nn nhiect (Mac and Dac).     

入侵检测技术的研究与进展

入侵检测系统(IDS)作为一门新兴的安全技术，是网络安全系统中的重要组成部分。该文阐述了入侵检测系统的基本原理和功能模块，从数据源、检测方法和检测定时三个方面描述了入侵检测系统的分类，并对目前国内外入侵检测技术的研究现状作了介绍和分析。随着计算机技术和网络技术的高速发展，海量存储和高带宽的传输技术，都使得集中式的入侵检测越来越不能满足系统需求。由此指出，分布式入侵检测(DID)必将逐渐成为入侵检测乃至整个网络安全领域的研究重点，为进行入侵检测技术的研究提供一定的技术和理论依据。     

A comparison of Intrusion Detection systems

A computer system intrusion is seen as any set of actions that attempt to compromise the integrity, confidentiality or availability of a resource.[1] The introduction of networks and the Internet caused great concern about the protection of sensitive information and have resulted in many computer security research efforts during the past few years. Although preventative techniques such as access control and authentication attempt to prevent intruders, these can fail, and as a second line of defence, intrusion detection has been introduced. Intrusion detection systems (IDS) are implemented to detect an intrusion as it occurs, and to execute countermeasures when detected.Usually, a security administrator has difficulty in selecting an IDS approach for his unique set-up. In this Report, different approaches to intrusion detection systems are compared, to supply a norm for the best-fit system. The results would assist in the selection of a single appropriate intrusion detection system or combine approaches that best fit any unique computer system.     

Wired and wireless intrusion detection system: Classifications, good characteristics and state-of-the-art

In computer and network security, standard approaches to intrusion detection and response attempt to detect and prevent individual attacks. Intrusion Detection System (IDS) and intrusion prevention systems (IPS) are real-time software for risk assessment by monitoring for suspicious activity at the network and system layer. Software scanner allows network administrator to audit the network for vulnerabilities and thus securing potential holes before attackers take advantage of them.

In this paper we try to define the intruder, types of intruders, detection behaviors, detection approaches and detection techniques. This paper presents a structural approach to the IDS by introducing a classification of IDS. It presents important features, advantages and disadvantages of each detection approach and the corresponding detection techniques. Furthermore, this paper introduces the wireless intrusion protection systems.

The goal of this paper is to place some characteristics of good IDS and examine the positioning of intrusion prevention as part of an overall layered security strategy and a review of evaluation criteria for identifying and selecting IDS and IPS. With this, we hope to introduce a good characteristic in order to improve the capabilities for early detection of distributed attacks in the preliminary phases against infrastructure and take a full spectrum of manual and automatic response actions against the source of attacks.     

一种入侵检测系统的规则描述语言

入侵检测系统(IDS)是保护信息系统安全的重要方式。而新一代IDS的一个最主要的特点就是IDS入侵检测规则描述语言。该文通过对Snort规则的分析与比较，提出了一种基于网络的IDS规则描述语言，它具有直观易定义、描述精确等特点。     

MOVIH-IDS: A mobile-visualization hybrid intrusion detection system

A novel hybrid artificial intelligent system for intrusion detection, called MObile-VIsualization Hybrid IDS (MOVIH-IDS), is presented in this study. A hybrid model built by means of a multiagent system that incorporates an unsupervised connectionist intrusion detection system (IDS) has been defined to guaranty an efficient computer network security architecture. This hybrid IDS facilitates the intrusion detection in dynamic networks, in a more flexible and adaptable manner. The proposed improvement of the system in this paper includes deliberative agents characterized by the use of an unsupervised connectionist model to identify intrusions in computer networks. This hybrid IDS has been probed through several real anomalous situations related to the simple network management protocol as it is potentially dangerous. Experimental results probed the successful detection of such attacks through MOVIH-IDS.     

入侵检测技术

入侵检测(Intrusion Detection),顾名思义,即是对入侵行为的发觉。它在计算机网络或计算机系统中的若干关键点收集信息,通过对这些信息的分析来发现网络或系统中是否有违反安全策略的行为和被攻击的迹象。进行入侵检测的软件与硬件的组合便是入侵检测系统(Intrusion Detection System,简称IDS)。与其他安全产品不同的是,入侵检测系统需要更多的智能,它必须能将得到的数据进行分析,并得出有用的结果。     

入侵检测技术

入侵检测（Intrusion Detection）,顾名思义,即是对入侵行为的发觉。它在计算机网络或计算机系统中的若干关键点收集信息,通过对这些信息的分析来发现网络或系统中是否有违反安全策略的行为和被攻击的迹象。进行入侵检测的软件与硬件的组合便是入侵检测系（Intrusion Detection System,简称IDS）。与其他安全产品不同的是,入侵检测系统需要更多的智能,它必须能将得到的数据进行分析,并得出有用的结果。     

面向滥用检测的反馈预测机制

在高速主干网络环境中的入侵检测系统应该满足两个要求：第一，需要尽早发现入侵企图；第二，要努力降低入侵检测的操作代价。两者的解决办法与入侵检测模型和测度密切相关。本文在一般的滥用检测系统中嵌入反馈预测机制，它不仅能预测用户当前行为是否入侵，而且能大幅度降低该入侵检测系统的操作代价，可适应在高速网络中的实时检测需要。实际测试结果表明反馈预测机制能比较精确地预测入侵，嵌入了反馈预测机制的滥用检测系统的数据处理能力有了较大的改善。     

入侵检测系统的研究

目前,网络安全是网络研究的热点,而随着对计算机系统弱点和入侵行为分析研究的深入,入侵检测系统在网络安全中发挥着越来越重要的作用,并成为处理网络安全问题的有效工具,对传统的安全防范技术起了重要的补充作用。该文介绍了研究入侵检测系统的意义,分析了入侵检测系统的一般工作流程,并给出了入侵检测系统的几种分类方法。入侵检测系统也有自身的局限性并且面临着诸多挑战。文章最后阐述入侵检测系统有待解决的关键问题以及入侵检测系统在我国网络安全中的重要地位。     

一种基于关联规则的分布式入侵检测模型

高虚警率和漏警率是当前入侵检测系统(IDS)的主要问题。采用基于CBW关联规则的数据挖掘算法,提出了一种新的分布式入侵检测模型,并分析了各模块的具体功能与实现。经实验分析,本模型可以有效降低虚警率和漏警率,同时在一定程度上实现各分节点间的快速协作检测能力。