基于多挖掘方法和CPN的告警关联系统

网络攻击越来越复杂,入侵检测系统产生大量告警日志,误报率高,可用性低。为此,论文提出采用聚类分析、关联挖掘算法和基于彩色Petri网的联合挖掘多步骤攻击方法进行IDS告警关联,从宏观和微观攻击轨迹角度重建攻击场景,提高了入侵检测系统的可用性。     

基于因果关联的电力工控系统攻击场景还原

随着电力系统自动化程度的快速提升,电力工控系统已成为网络攻击的重要目标。文中将因果关联方法应用于电力工控系统攻击场景还原,并提出一个完整的场景还原框架。该框架在攻击取证阶段通过入侵检测和监测设备得到多源原始告警信息,然后采用入侵检测信息交换格式将多源告警信息标准化并去除冗余,获得有效攻击证据;在场景还原阶段,引入告警信息差异度计算方法,并结合因果关联方法推理还原攻击路径。配电网攻击还原案例分析验证了所提框架的可行性。     

电信网络海量安全事件关联分析引擎技术研究

关联分析引擎通过把孤立的安全事件集合关联为一个安全事件链,从大量误报告警与低级别告警中找出真正威胁告警,是安全运营中心分析安全态势、做出及时响应的关键技术模块。提出了一种结合状态机与推理机两类关联分析机制优点的关联分析引擎技术——基于序列状态推导的关联分析引擎,通过自动序列控制攻击场景规则的匹配,实现高效准确的海量安全事件的关联分析,能够较好地解决传统关联分析引擎在电信级网络应用中的问题。     

基于模糊场景关联分析的技术研究与实践

文章针对海量多源异构安全日志分析问题,提出了一种基于模糊场景的关联分析方法。这种方法打破传统安全告警日志关联分析技术中常采用的构建固定攻击场景的方式。它采用聚类算法对多源异构告警日志进行聚合,综合考虑告警事件的数据来源、数量和事件等级,对每部分进行权重的累加,计算出与源IP对应的事件疑似度。文章介绍了模糊场景关联分析方法的架构原理、技术实现,并通过实例加以说明,对所提出的方法进行验证。结果表明该方法和应用是可行和有效的。     

基于攻击图模型的网络安全态势评估方法

针对网络攻击出现的大规模、协同、多阶段的特点,提出一种基于攻击图模型的网络安全态势评估方法。首先,结合攻击事件的时空特征融合多源告警数据构建网络攻击行为特征;其次,基于告警信息映射攻击节点,关联多步攻击的路径;再次,在构建攻击图的基础上,结合转移序列构建攻击节点转移概率表,将转移概率引入攻击图中,推断攻击者的攻击意图;最后,针对最大可能的攻击路径,对大概率的攻击节点进行安全态势评估,科学量化网络攻击后潜在攻击节点的安全态势,为网络安全管理人员提前做好防护提供理论支撑和科学依据。     

面向故障联合定位的告警数据信息填充算法

数据完整性是网络告警信息的基本质量属性,是进一步进行网络告警故障分析的基础。然而在现实中,网管系统可能接收到信息缺失的网元告警信息,从而影响网络故障定位的准确性。分别使用决策树算法、窗关联规则挖掘(WARM)算法和相似度算法对网络告警信息中缺失的属性进行数据填充,并在国家电网信息网络和通信网络共存的场景下研究分析上述算法对联合故障定位性能的提升。实验结果表明,在该场景下决策树算法有更高的联合故障定位准确率。     

新的入侵检测数据融合模型——IDSFP

以多传感器数据融合技术为基础,提出了新的入侵检测融合模型——IDSFP。其具有对多个IDS入侵检测系统的警报进行关联、聚合,产生对安全态势判断的度量,从而构成证据的特点。IDSFP应用D-S证据理论来形成对当前安全态势进行评估的信息,并动态地反馈、调整网络中各个IDS(intrusiondetectionsystem),加强对与攻击意图有关的数据的检测,进而提高IDS检测效率,降低系统的误报率和漏报率。     

基于告警关联的“互联网”多步攻击意图识别方法

针对现有静态评估的漏洞威胁技术不能有效量化网络攻击危害的问题，提出一种基于告警关联的多步攻击意图识别方法。该方法通过告警数据的关联特点挖掘并还原攻击者的多步攻击序列，围绕攻击过程评估基础设施重要性和漏洞威胁探测攻击者意图，从而实现还原攻击场景、刻画攻击行为的目的。实验表明，与传统算法进行对比分析，在DARPA2000上验证了该算法对特定网络攻击场景的识别能力，且百分误差绝对值和均方误差绝对值均低于传统算法。由此可知，文中所述的结合漏洞威胁和基础设施重要性来关联攻击步骤能够有效解决攻击过程出现的虚假攻击问题，提升了网络多步攻击意图识别的准确性。     

基于小波神经网络的告警信息相关性挖掘策略

针对现有告警信息相关性分析方法没有客观全面考虑各告警的重要程度,无法体现告警之间个体差异性等问题,该文提出一种基于小波神经网络的加权关联规则告警挖掘算法。综合告警级别、告警类型以及告警设备类型3个主要告警属性,将其作为小波神经网络的输入,通过对历史样本数据的学习确定连接权值,合理地评估各个告警属性重要程度,利用所得权值向量进一步挖掘告警加权关联规则。结果表明所提算法在权值确定时能够综合考虑告警信息的多个属性及历史经验,得到的权值更能合理地反映告警重要度,所得关联规则能够更加准确地反映告警之间的相关性。     

基于XML的分布式多步骤入侵特征语言

分布式多步骤入侵的证据可能分散在不同的节点中,很多告警是多步骤攻击中的一个步骤,因此IDS有必要收集并关联不同来源的信息.本文提出了一种完全非集中方式,将分布式多步骤入侵场景建模为分布于被保护网络系统中多个节点上的检测子任务序列.文章为这种模式提出了一种基于XML的分布式多步骤入侵场景描述语言.     

考虑置信度的告警因果关联的研究

一个成功的网络攻击往往由若干个处于不同阶段的入侵行为组成,较早发生的入侵行为为下一阶段的攻击做好准备。在因果关联方法中,可以利用入侵行为所需的攻击前提和造成的攻击结果,重构攻击者的攻击场景。论文引入了告警关联置信度的属性描述,用于分析因果关联结果的可信度,进而能够进一步消除虚假关联关系。通过DARPA标准数据集分析,该方法取得了较好的实验结果。     

Visualization of big data security: a case study on the KDD99 cup data set

Cyber security has been thrust into the limelight in the modern technological era because of an array of attacks often bypassing untrained intrusion detection systems (IDSs). Therefore, greater attention has been directed on being able deciphering better methods for identifying attack types to train IDSs more effectively. Keycyber-attack insights exist in big data; however, an efficient approach is required to determine strong attack types to train IDSs to become more effective in key areas. Despite the rising growth in IDS research, there is a lack of studies involving big data visualization, which is key. The KDD99 data set has served as a strong benchmark since 1999; therefore, we utilized this data set in our experiment. In this study, we utilized hash algorithm, a weight table, and sampling method to deal with the inherent problems caused by analyzing big data; volume, variety, and velocity. By utilizing a visualization algorithm, we were able to gain insights into the KDD99 data set with a clear identification of “normal” clusters and described distinct clusters of effective attacks.     

FuzMet: a fuzzy‐logic based alert prioritization engine for intrusion detection systems

Intrusion detection systems (IDSs) are designed to monitor a networked environment and generate alerts whenever abnormal activities are detected. The number of these alerts can be very large, making their evaluation by security analysts a difficult task. Management is complicated by the need to configure the different components of alert evaluation systems. In addition, IDS alert management techniques, such as clustering and correlation, suffer from involving unrelated alerts in their processes and consequently provide results that are inaccurate and difficult to manage. Thus the tuning of an IDS alert management system in order to provide optimal results remains a major challenge, which is further complicated by the large spectrum of potential attacks the system can be subject to. This paper considers the specification and configuration issues of FuzMet, a novel IDS alert management system which employs several metrics and a fuzzy‐logic based approach for scoring and prioritizing alerts. In addition, it features an alert rescoring technique that leads to a further reduction in the number of alerts. Comparative results between SNORT scores and FuzMet alert prioritization onto a real attack dataset are presented, along with a simulation‐based investigation of the optimal configuration of FuzMet. The results prove the enhanced intrusion detection accuracy brought by our system. Copyright © 2011 John Wiley & Sons, Ltd.     

An efficient approach to reduce alerts generated by multiple IDS products

Intrusion detection systems (IDSs) often trigger a huge number of unnecessary alerts. Managing the overwhelming number of alerts, especially from multiple IDS products, is a concern to every security analyst. Analyzing and evaluating these alerts is a difficult task that frustrates the effort of analysts. In fact, true alerts are usually buried under heaps of false alerts. We have identified several research gaps in the existing alert management approaches that need to be addressed, especially when handling alerts from different IDS products. In this work, we present an efficient alert management approach that reduces the unnecessary alerts produced by different IDS products using two main modules: an enhanced alert verification module that validates alerts with vulnerability assessment data; and an enhanced alert aggregator module that reduces redundant alerts and presents them in the form of meta alerts. Finally, we have carried out experiments in our test bed and recorded impressive results in terms of high accuracy and low false positive rate for multiple IDS products. Copyright © 2014 John Wiley & Sons, Ltd.     

Counselors network for intrusion detection

Intrusion detection systems (IDSs) are a fundamental component of defense solutions. In particular, IDSs aim to detect malicious activities on computer systems and networks by relying on data classification models built from a training dataset. However, classifiers' performance can vary for each attack pattern. A common technique to overcome this issue is to use ensemble methods, where multiple classifiers are employed and a final decision is taken combining their outputs. Despite the potential advantages of such an approach, its usefulness is limited in scenarios where (i) multiple expert classifiers present divergent results, (ii) all classifiers present poor results due to lack of representative features, or (iii) detectors have insufficient labeled signatures to train their classifiers for a specific attack pattern. In this work, we introduce the concept of a counselors network to deal with conflicts from different classifiers by exploiting the collaboration among IDSs that analyze multiple and heterogeneous data sources. Empirical results demonstrate the feasibility of the proposed architecture in improving the accuracy of the intrusion detection process.     

Design and analysis of intrusion detection systems for wireless mesh networks

Intrusion is any unwanted activity that can disrupt the normal functions of wired or wireless networks. Wireless mesh networking technology has been pivotal in providing an affordable means to deploy a network and allow omnipresent access to users on the Internet. A multitude of emerging public services rely on the widespread, high-speed, and inexpensive connectivity provided by such networks. The absence of a centralized network infrastructure and open shared medium makes WMNs particularly susceptible to malevolent attacks, especially in multihop networks. Hence, it is becoming increasingly important to ensure privacy, security, and resilience when designing such networks. An effective method to detect possible internal and external attack vectors is to use an intrusion detection system. Although many Intrusion Detection Systems (IDSs) were proposed for Wireless Mesh Networks (WMNs), they can only detect intrusions in a particular layer. Because WMNs are vulnerable to multilayer security attacks, a cross-layer IDS are required to detect and respond to such attacks. In this study, we analyzed cross-layer IDS options in WMN environments. The main objective was to understand how such schemes detect security attacks at several OSI layers. The suggested IDS is verified in many scenarios, and the experimental results show its efficiency.     

A ranging based scheme for detecting the wormhole attack in wireless sensor networks

Wireless sensor networks have been widely used in general and military scenarios. And this leads to a need for more security. Wireless sensor network are easy vulnerable to attack and compromise. Wormhole attack is a harmful against routing protocol which can drop data randomly or disturbing routing path. In this paper, we proposed a novel method to detect the wormhole attack based on statistical analysis. In the proposed method, a sensor can detect the fake neighbors which are caused by wormhole through the neighbor discovery process, and then a k-means clustering based method is used to detect wormhole attack according to the neighbor information. That is, by using this proposed method, we can detect the wormhole only by the neighbor information without any special requirement. We did some experiments to evaluate the performance of this method, and the experimental results show that our method can achieve satisfying results.     

识别IDS躲避技术-网络安全策略的关键

随着网络技术的发展、网络规模的扩大 ,针对网络和计算机系统的非法入侵水平的不断提高 ,其入侵手段的自动化水平和隐蔽性也越来越高。对入侵攻击的检测与防范 ,保障计算机系统、网络系统及整个信息基础设施的安全已经成为刻不容缓的重要课题。单纯引入某些设备已不能满足当今网络安全的需要。为此本文以网络入侵检测系统为例 ,讨论了一些常用的IDS躲避技术 ,以及如何识破这些技术 ,制定更加细致的安全策略 ,以构造出更加强壮的防护体系     

基于SAT的安全协议惰性形式化分析方法

提出了一种基于布尔可满足性问题的安全协议形式化分析方法SAT-LMC,通过引入惰性分析的思想优化初始状态与转换规则,提高了安全性的检测效率。另一方面,通过在消息类型上定义偏序关系,SAT-LMC能够检测出更丰富的类型缺陷攻击。基于此方法实现了一个安全协议分析工具,针对Otway-Rees协议检测出了一种类型缺陷攻击;针对OAuth2.0协议,检测结果显示对现实中存在的一些应用场景,存在一种利用授权码截取的中间人攻击。     

基于数据挖掘的入侵检测体系

入侵检测技术是一种主动保护网络免受黑客攻击的安全技术,它是继防火墙、数据加密等传统安全保护措施后的新一代的网络安全保障技术。当前的入侵检测系统在网络的变化或升级上缺乏扩展性,对新的攻击模式缺乏自适应性。提出了一种基于数据挖掘的入侵检测模型,该模型具有一定的自学习性和自完善性,可以检测已知或未知的入侵行为。