利用蜜罐提高NIDS的检测性能

目前的入侵检测技术本身存在着缺陷,比如特征检测中规则库不完备.由这些缺陷而导致的误报和漏报是制约其发展的重要瓶颈.Honeypot/net是一种新的安全技术,通过部署蜜罐收集攻击信息,再把这些信息加以整理传送给IDS,可以弥补入侵检测技术的一些缺点,从而降低IDS的误报率和漏报率.本文分析了这一设想的可行性,并提出了设计方案.此方案中包括一个Honeynet Software,它联系Honeynet 控制台和NIDS控制台,完成其中提取新模式、传递攻击信息等功能.     

Honeynet新技术及其应用

伴随着网络的普及与发展,越来越频繁的黑客入侵已经对网络安全构成了严重的威胁,然而当前的大部分系统采用的是被动防御技术,有防火墙技术和入侵检测技术等,而随着网络技术的发展,逐渐暴露出其缺陷。防火墙在保障网络安全方面,对病毒、访问限制、后门威胁和对于内部的黑客攻击等都无法起到作用。入侵检测则存在着难以检测新类型黑客攻击方法,并可能漏报和误报的问题,这些都必须要求有更高的技术手段来防范黑客攻击与病毒入侵。Honeypot技术使这些问题有望得到进一步的解决,通过观察和记录黑客在Honeypot上的活动,人们可以了解黑客的动向、黑客使用的攻击方法等有用信息。如果将Honeypot采集的信息与IDS采集的信息联系起来,则有可能减少IDS的漏报和误报,并能用于进一步改进IDS的设计,增强IDS的检测能力。Spitzner认为Honeypot的概念非常简单,就是一个专门让黑客攻击的系统,而作为欺骗系统的Honeypot所发挥的作用是极其有限的,Honeypot主要作用是提供了一条获取黑客信息的途径;基于Honeypot理论发展而来的Honeynet在理论技术方面已经比较成熟,Honeynet主张使用真实的系统。这样既可以观察黑客是如何侵入系统的,又可以深人观察黑客进入系统之后的一些活动,Honeynet方案提供给黑客的活动环境是一个网络,该网络一般有多台作为“诱饵”的Honeypot机,连到Internet上,吸引黑客攻击。Honeynet利用人侵检测系统捕获网络上传输的数据包,记录黑客活动信息。另外,Honeynet利用防火墙控制黑客不能以Honeypot机为跳板攻击该网络外的系统。     

探索构造一个比较实用的分布式入侵检测系统

一、网络技术的发展孕育出分布式入侵检测系统 传统的集中式入侵检测技术的基本模型是在网络的不同网段中放置传感器或嗅探器来收集网络状态信息,并将这些信息传送至中央控制台进行分析和处理。这种集中式的入侵检测模型存在着明显的缺陷。首先,在面对大规模、异质网络基础上进行的复杂攻击,中央控制台     

入侵检测协作检测模型的分析与评估

目前,入侵检测系统(IDS)存在较高的误报率,这一直是困扰IDS用户的主要问题,而入侵检测系统主要有误用型和异常型两种检测技术,根据这两种检测技术各自的优点,以及它们的互补性,将两种检测技术结合起来的方案越来越多地应用于IDS.通过引入入侵检测能力,从理论上深刻解释了系统协作的必然性,提出了异常检测技术和误用检测技术相结合的IDS模型及其评估方法,降低了单纯使用某种入侵检测技术时产生的误报率,从而提高系统的安全性.     

MAIDS-多检测技术的IDS模型

目前,入侵检测系统的漏报率和误报率高一直是困扰IDS用户的主要问题,而入侵检测系统主要有误用型和异常型两种检测技术。针对这一问题,根据这两种检测技术各自的优点,以及它们的互补性,将两种检测技术结合起来的方案越来越多地应用于IDS中。论文提出了基于统计的异常检测技术和基于模式匹配的误用检测技术及其它检测技术相结合的IDS模型-MAIDS,以期达到减少入侵检测系统的漏报率和误报率的目的,从而提高系统的安全性。     

方通Sniper打破交换限制

国内入侵检测系统的市场和产品都在不断成熟,现在,入侵检测系统和防火墙、防病毒一起已经成为保护网络安全的三剑客。 但是这两年,厂商、媒体一直是从正面宣传IDS的功能,却回避IDS的缺陷。在众多缺陷中,交换机的数据镜像、VLAN给网络入侵检测系统(NIDS)的运用带来很大的麻烦。而众多IDS厂商却避而不谈,这必然会误     

安全事件关联分析引擎的研究与设计

入侵检测系统是动态安全防御里的重要环节，现有的入侵检测系统（IDS）存在一个致命的缺陷：误报率高居不下，IDS无法展现事件之间的逻辑关系，结果用户很难了解事件背后隐藏的攻击策略或逻辑步骤。为了解决IDS存在的上述问题，在深入分析入侵技术的基础上提出了基于入侵序列的启发式关联方法，设计并实现了一个事件关联分析引擎，最后验证了有效性。     

基于信息反馈的入侵检测模型研究

针对目前入侵检测系统（Intrusion Detection System,IDS）对未知异常检测误报率率比较高的问题,提出了一种基于信息反馈的入侵检测方法。首先设计了一个IDS与主机协作检测的模型,然后详细介绍了IDS根据反馈信息利用行为分析技术对未知异常的检测过程。最终实现了高效的入侵检测系统。     

基于粒子群和人工免疫的混合入侵检测系统研究

目前,漏报率和误报率高一直是入侵检测系统（IDS）的主要问题,而IDS主要有误用型和异常型两种检测技术。根据这两种检测技术各自的优点以及它们的互补性,本文给出一种基于人工免疫的异常检测技术和基于粒子群优化（PSO）的误用检测技术相结合的IDS模型;同时,该系统还结合特征选择技术降低数据维度,提高系统检测性能。实验表明,该
系统具有较高的检测率和较低的误报率,可以自动更新规则库,并且记忆未知类型的攻击,是一种有效的检测方法。     

针对网络入侵检测系统的攻击及防御

Internet的使用越来越广泛,随之而来的网络安全已成为人们关注的焦点。入侵检测系统作为一种对付攻击的有效手段,已为越来越多的单位所采用。然而一旦攻击者发现目标网络中部署有入侵检测系统IDS,那么IDS往往成为他们首选的攻击目标。该文详细分析了针对网络IDS的几种攻击类型,即过载攻击、崩溃攻击和欺骗攻击,以及如何防御这些攻击,这对于IDS的设计具有一定的借鉴意义。     

异常入侵检测系统虚警率问题研究

入侵检测系统的虚警率影响检测结果的可信性.通过分析入侵检测系统的可信问题及异常入侵检测系统的虚警率问题,提出了降低虚警率的方法:基于进程检测行为的入侵检测方法、多检测系统协作工作模式.重点描述了基于人工免疫思想,动态构建正常系统轮廓,抑制虚警率的方法,并对其进行了仿真实验.实验表明,本方法可以提高检测效率,有效降低系统虚警率.     

一种IDS报警可信性增强方案*

提高IDS（入侵检测系统）报警的可信性是IDS的根本目标。从理论上分析了可信问题产生的原因,给出了其形式化描述,提出了一种多IDS协同工作提高检测可信度的方法,并证明了该方法可以应用于各种不同IDS的协同工作中（基于误用、异常及异常与误用相结合的IDS）。多检测系统结果融合时采用推进Bayesian分类方法,给出了其模型和具体算法。实验分析表明,该方法与其他同类算法相比,降低了系统的漏报率和误报率,增强了报警的可信度。     

A lightweight distributed detection algorithm for DDAO attack on RPL routing protocol in Internet of Things

A significant increase in the number of connected devices in the Internet of Things poses a key challenge to efficiently handling the attacks in routing protocols such as Routing Protocol for Low Power and Lossy Networks (RPL). The attacks on RPL are partly studied in the literature, and the proposed solutions typically overlook the appropriate trade-off among the detection rate and communication and computational overhead. This study aimed at introducing a new attack called Dropped Destination Advertisement Object (DDAO) and a new Intrusion Detection System (IDS) to counter this attack in RPL protocol. DDAO attack adversely affects the network by preventing the creation of the downward routes through not forwarding Destination Advertisement Object (DAO) messages and sending fake Destination Advertisement Object Acknowledgment (DAO-ACK) messages to the DAO source. A distributed lightweight IDS is proposed in this study to detect and counter DDAO attacks by monitoring the behavior of parents against forwarded DAO messages. According to the evaluations conducted on the Cooja simulator under different real-world conditions, the proposed IDS can detect DDAO attacks with high accuracy, precision, and True Positive Rate (TPR) and low False Positive Rate (i.e., close to zero). Additionally, compared to RPL, the proposed IDS improves Packet Delivery Rate (PDR) by 158 percent when countering attacks.     

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.     

入侵检测报警信息管理系统设计与实现

入侵检测系统的高误警率成为制约其发展的瓶颈之一。本文首先分析了目前入侵检测系统存在误警的原因,分析了进行报警信息管理的必要性,提出并设计了一个入侵检测系统报警信息管理的模型,最后对系统进行了实验验证,结果表明该系统能有效地减少报警数量。     

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.     

Identification of vulnerable node clusters against false data injection attack in an AMI based Smart Grid

In today׳s Smart Grid, the power Distribution System Operator (DSO) uses real-time measurement data from the Advanced Metering Infrastructure (AMI) for efficient, accurate and advanced monitoring and control. Smart Grids are vulnerable to sophisticated data integrity attacks like the False Data Injection (FDI) attack on the AMI sensors that produce misleading operational decision of the power system (Liu et al., 2011 [1]). Presently, there is a lack of research in the area of power system analysis that relates the FDI attacks with system stability that is important for both analysis of the effect of cyber-attack and for taking preventive measures of protection.In this paper, we study the physical characteristics of the power system, and draw a relationship between the system stability indices and the FDI attacks. We identify the level of vulnerabilities of each AMI node in terms of different degrees of FDI attacks. In order to obtain the interdependent relationship of different nodes, we implement an improved Constriction Factor Particle Swarm Optimization (CF-PSO) based hybrid clustering technique to group the nodes into the most, the moderate and the least vulnerable clusters. With extensive experiments and analysis using two benchmark test systems, we show that the nodes in the most vulnerable cluster exhibit higher likelihood of de-stabilizing system operation compared to other nodes. Complementing research is the construction of FDI attacks and their countermeasures, this paper focuses on the understanding of characteristics and practical effect of FDI attacks on the operation of the Smart Grid by analysing the interdependent nature of its physical properties.     

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.     

一种新的基于协议树的入侵检测系统的设计

基于协议分析的入侵检测系统避免了传统入侵检测系统的计算量大、准确率低的缺陷。在协议分析的基础上,提出了一种基于带权重协议树的入侵检测系统,给出了其设计方案,该方案进一步提高了检测的准确性和效率,并且可以检测变体攻击、拒绝服务攻击等较难检测的攻击。