一种基于代理的分布式抗攻击的入侵检测体系结构

提出了一种基于代理(Agent)的入侵检测体系结构。该体系克服了当前入侵检测系统(IDS)的部分缺陷，具有分布式检测、响应入侵的能力，并能对单一主机、检测区域和整个网络进行多层次的检测。利用移动代理，整个检测体系可以灵活、动态地配置和方便地扩展。针对IDS日益成为攻击目标的现状，结合现有保护IDS的研究成果，给出了相应的方法，使该体系能有效地抵抗攻击，有更强的生存能力。     

免疫原理驱动的入侵检测系统框架

将人体免疫原理应用于计算机安全领域是安全技术研究的一次新的尝试。免疫系统捍卫着人体的生存,正如入侵检测系统(IDS,IntrusionDetectionSystem)保护计算机系统免受攻击的摧毁。当前大多数IDS系统仅采用基于特征的检测以减少误报,然而这会造成不可忍受的漏报问题。论文将免疫原理引入入侵检测领域,试图为当前入侵检测领域普遍存在的误报、漏报问题提出一种解决方案,并提出一种分布式、轻量级和自组织的入侵检测系统框架。     

基于联动机制的入侵防御系统

当前的IDS入侵检测系统对于较为复杂的攻击缺乏有效的应对和阻断能力，而防火墙则缺乏针对各种攻击的灵活的过滤策略。文章以snort入侵检测系统和iptables防火墙为实例，构架了一个基于联动机制的入侵防御系统。该系统能够使IDS入侵检测系统与防火墙联动运行，利用IDS的入侵检测能力，动态地为防火墙定制针对各种入侵的过滤规则，实现对入侵行为的实时阻断。     

融合漏洞扫描的入侵检测系统模型的研究

目前大部分入侵检测系统(IDS)采用基于模式匹配的入侵检测方法,该方法由于计算量大,因而在高速网络中检测效率较低。文章提出一种新的融合漏洞扫描功能的IDS模型,通过定期对系统进行漏洞扫描,及时修补系统安全漏洞,同时IDS根据漏洞扫描结果,对模式库进行动态更新,删除与得到修补的漏洞有关的攻击模式,缩减模式库的规模,提高检测效率。文章根据该模型提出一种基于多Agent的分布式IDS体系结构,提高了系统的可扩充性。     

融合漏洞扫描的入侵检测系统模型的研究

目前大部分入侵检测系统（IDS）采用基于模式匹配的入侵检测方法，该方法由于计算量大，因而在高速网络中检测效率较低。文章提出一种新的融合漏洞扫描功能的IDS模型，通过定期对系统进行漏洞扫描，及时修补系统安全漏洞，同时IDS根据漏洞扫描结果，对模式库进行动态更新，删除与得到修补的漏洞有关的攻击模式，缩减模式库的规模，提高检测效率。文章根据该模型提出一种基于多Agent的分布式IDS体系结构，提高了系统的可扩充性。     

基于本体的Web服务攻击检测技术研究

Web服务在给基于异构平台的应用集成带来极大便利的同时,各核心组件也面临着被恶意攻击的威胁。目前,主要依靠入侵检测系统(IDS)来检测这些攻击,但是分布在网络中的IDS往往是由不同的厂商或组织开发的,没有用于交换知识的可被共同理解的词汇集,难以交互和协作,工作效率低且很难抵御多层次、分布式攻击。提出了一种基于本体和Web本体标准语言（OWL）的Web服务攻击分类和描述方法,通过构建Web服务攻击本体以提供不同IDS共同理解的词汇集。在此基础上,设计了一种基于Web服务攻击本体库的入侵检测系统(O-IDS),能有效弥补现有IDS难以交互的不足,提高对多层次、分布式攻击的检测能力。     

拉紧IPS的两根弦

网络数据处理性能、攻击检测阻断准确性,这是IPS产品当前的突破所在。在网络安全领域,IDS(入侵检测系统)与IPS(入侵防御系统)都是热门的话题。如果说IDS主要是注重全面检测、有效呈现,那么IPS则更专长于深层防     

基于Jini的分布式入侵检测系统

文章设计了一个分布式自适应入侵检测系统(DAIDS)，它通过各个检测节点之间的协同工作，共同检测和防范对系统的入侵行为，并且有机结合了主机型IDS和网络型IDS，给出了该系统的分布式体系结构和基于Jini的实现，介绍了一些系统实现中的关键问题及其解决方法。     

基于Agent的分布式协作入侵检测系统

分析了当前入侵检测系统及其存在的问题,提出一个基于Agent的分布式协作检测模型(ADCM),给出其原型系统。该模型通过逻辑检测域(LDD)之间的协作通信,完成对新型分布式攻击的检测。实验证明ADCM可以有效地检测出具有一定隐蔽性的、分布式的协作攻击。     

网络安全战略预警系统的攻击检测技术研究

攻击检测系统是网络安全战略预警系统的重要组成部分，它从现有的入侵检测系统（IDS）出发，应用当前的民用技术来发展更先进的入侵检测系统（IDS），又将数据输入从逻辑入侵拓展到物理、心理和情报攻击，这些都是信息战进攻的一部分，本文主要探讨适合大范围协同攻击的检测技术。     

A hybrid intrusion detection system design for computer network security

Intrusions detection systems (IDSs) are systems that try to detect attacks as they occur or after the attacks took place. IDSs collect network traffic information from some point on the network or computer system and then use this information to secure the network. Intrusion detection systems can be misuse-detection or anomaly detection based. Misuse-detection based IDSs can only detect known attacks whereas anomaly detection based IDSs can also detect new attacks by using heuristic methods. In this paper we propose a hybrid IDS by combining the two approaches in one system. The hybrid IDS is obtained by combining packet header anomaly detection (PHAD) and network traffic anomaly detection (NETAD) which are anomaly-based IDSs with the misuse-based IDS Snort which is an open-source project.The hybrid IDS obtained is evaluated using the MIT Lincoln Laboratories network traffic data (IDEVAL) as a testbed. Evaluation compares the number of attacks detected by misuse-based IDS on its own, with the hybrid IDS obtained combining anomaly-based and misuse-based IDSs and shows that the hybrid IDS is a more powerful system.     

Mixing Wheat with the Chaff: Creating Useful Test Data for IDS Evaluation

As the use of intrusion detection systems (IDSs) continues to climb and as researchers find more ways to detect attacks amid a vast ocean of data. The problem of testing IDS solutions has reared its ugly bead. Showing that one technique is better than another or training an IDS about normal usage requires test data. As it turns out, collecting or creating such a data set is something of a catch-22. If the data already contains attacks, researchers will train the IDS to see the attacks as normal; the IDS could then fail to register them as malicious events in the future. The most efficient way, however, to determine whether a large data set contains malicious events is to scan it with existing IDS. Thus, any attacks that the existing IDS fails to find are presented to the new IDS as normal data leading to potential false negatives. Clearly, breaking this cycle requires an independent source of verifiable attack-free training data with which to train IDSs.     

一种基于移动代理的分布式IDS模型研究

传统的分布式层次型入侵检测系统也有难以克服的缺陷,在层出不穷的攻击方式面前容易崩溃。本文提出了一种基于移动代理的IDS模型,它综合了两个领域的研究成果：分布式IDS和移动代理(MA)。通过安全分析表明,该模型提供了较好的安全构架。     

Network Security Alerts Management Architecture for Signature-Based Intrusions Detection Systems within a NAT Environment

Internet is providing essential communication between an infinite number of people and is being increasingly used as a tool for commerce. At the same time, security is becoming a tremendously important issue to deal with. Different network security solutions exist and contribute to enhanced security. From these solutions, Intrusion detection systems (IDS) have become one of the most common countermeasures for monitoring safety in computer systems and networks. The purpose of IDSs is distinguishing between intruders and normal users. However, IDSs report a massive number of isolated alerts. These isolated alerts represent low-level security-related events. Many of these isolated alerts are logically involved in a single multi-stage intrusion incident and a security officer often wants to analyze the complete incident instead of each individual simple alert. Another problem is that IDSs cannot work correctly with an environment managed with a NAT technique (Network Address Translation) since the host information (IP address and port number) are affected by the NAT devices. In order to address these limitations, the paper proposes a well-structured model to manage the massive number of isolated alerts and includes the NAT information in the IDS analysis. In fact, our solution permits to determine the real identities of entities implicated in security issues and abstracts the logical relation between alerts in order to support automatic correlation of those alerts involved in the same intrusion and to construct comprehensible attacks scenarios.     

Alert correlation in collaborative intelligent intrusion detection systems—A survey

As complete prevention of computer attacks is not possible, intrusion detection systems (IDSs) play a very important role in minimizing the damage caused by different computer attacks. There are two intrusion detection methods: namely misuse- and anomaly-based. A collaborative, intelligent intrusion detection system (CIIDS) is proposed to include both methods, since it is concluded from recent research that the performance of an individual detection engine is rarely satisfactory. In particular, two main challenges in current collaborative intrusion detection systems (CIDSs) research are highlighted and reviewed: CIDSs system architectures and alert correlation algorithms. Different CIDSs system, architectures are explained and compared. The use of CIDSs together with other multiple security systems raise certain issues and challenges in, alert correlation. Several different techniques for alert correlation are discussed. The focus will be on correlation of CIIDS alerts. Computational, Intelligence approaches, together with their applications on IDSs, are reviewed. Methods in soft computing collectively provide understandable, and autonomous solutions to IDS problems. At the end of the review, the paper suggests fuzzy logic, soft computing and other AI techniques, to be exploited to reduce the rate of false alarms while keeping the detection rate high. In conclusion, the paper highlights opportunities for an integrated solution to large-scale CIIDS.     

Decentralized multi-dimensional alert correlation for collaborative intrusion detection

The growth in coordinated network attacks such as scans, worms and distributed denial-of-service (DDoS) attacks is a profound threat to the security of the Internet. Collaborative intrusion detection systems (CIDSs) have the potential to detect these attacks, by enabling all the participating intrusion detection systems (IDSs) to share suspicious intelligence with each other to form a global view of the current security threats. Current correlation algorithms in CIDSs are either too simple to capture the important characteristics of attacks, or too computationally expensive to detect attacks in a timely manner. We propose a decentralized, multi-dimensional alert correlation algorithm for CIDSs to address these challenges. A multi-dimensional alert clustering algorithm is used to extract the significant intrusion patterns from raw intrusion alerts. A two-stage correlation algorithm is used, which first clusters alerts locally at each IDS, before reporting significant alert patterns to a global correlation stage. We introduce a probabilistic approach to decide when a pattern at the local stage is sufficiently significant to warrant correlation at the global stage. We then implement the proposed two-stage correlation algorithm in a fully distributed CIDS. Our experiments on a large real-world intrusion data set show that our approach can achieve a significant reduction in the number of alert messages generated by the local correlation stage with negligible false negatives compared to a centralized scheme. The proposed probabilistic threshold approach gains a significant improvement in detection accuracy in a stealthy attack scenario, compared to a naive scheme that uses the same threshold at the local and global stages. A large scale experiment on PlanetLab shows that our decentralized architecture is significantly more efficient than a centralized approach in terms of the time required to correlate alerts.     

多代理分布式入侵检测系统在校园网中的应用

近年来，入侵检测系统(IDS)作为信息系统安全的重要组成部分，得到了广泛的重视。可以看到，仅仅采用防火墙技术来构造网络的安全体系是远远不够的，很多攻击可以绕过防火墙。入侵检测技术可以在网络系统受到损害前对入侵行为做出拦截和响应。基于代理的分布式入侵检测系统实现了基于主机和基于网络检测的结合，为网络系统提供更好的安全保护。文中针对防火墙技术的不足，在对入侵检测技术及其通用架构做出分析和研究后，设计了一种基于代理的分布式入侵检测系统，并给出了在某校园网中的实现。     

入侵检测系统发展的研究综述

With the fast development of Internet,more and more computer security affairs appear. Researchers have developed many security mechanisms to improve computer security ,including intrusion detection (ID). This paper re-views the history of intrusion detection systems (IDS)and mainstream techniques used in IDS,showing that IDS couldimprove security only provided that it is devised based on the architecture of the target system. From this, we could see the trend of integration of host-oriented ,network-oriented and application-oriented IDSs.     

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.