Analysis of a scanning model of worm propagation

The traditional approach to modeling of internet worm propagation is to adopt a mathematical model, usually inspired by modeling of the spread of infectious diseases, describing the expected number of hosts infected as a function of the time since the start of infection. The predictions of such a model are then used to evaluate, improve, or develop defense and containment strategies against worms. However, a proper and complete understanding of worm propagation goes well beyond the mathematical formula given by the chosen model for the expected number of hosts infected at a given time. Thus, questions such as fitting the model, assessing the extent to which a specific realization of a worm spread may differ from the model’s predictions, behavior of the time points at which infections occur, and the estimation and effects of misspecification of model’s parameters must also be considered. In this paper, we address such questions for the well-known random constant spread (RCS) model of worm propagation. We first generalize the RCS model to our nonhomogeneous random scanning (NHRS) model. The NHRS model allows the worm’s contact rate to vary during worm propagation and it thus captures far more situations of interest than the RCS model which assumes a scanning rate constant in time. We consider the problem of fitting these models to empirical data and give a simulation procedure for a RCS epidemic. We also show how to obtain a confidence interval for the unknown contact rate in the RCS model. In addition, the use of prior information about the contact rate is discussed. The results and methodologies of this paper illuminate the structure and application of NHRS and RCS models of worm propagation.     

vEye: behavioral footprinting for self-propagating worm detection and profiling

With unprecedented speed, virulence, and sophistication, self-propagating worms remain as one of the most severe threats to information systems and Internet in general. In order to mitigate the threat, efficient mechanisms are needed to accurately profile and detect the worms before or during their outbreaks. Particularly, deriving a worm’s unique signatures, or fingerprints, is of the first priority to achieve this goal. One of the most popular approaches is to use content-based signatures, which characterize a worm by extracting its unique information payload. In practice, such content-based signatures, unfortunately, suffer from numerous disadvantages, such as vulnerable to content mutation attacks or not applicable for polymorphic worms. In this paper, we propose a new behavioral footprinting (BF) approach that nicely complements the state-of-the-art content-based signature approaches and allows users to detect and profile self-propagating worms from the unique worm behavioral perspective. More specifically, our behavioral footprinting method uniquely captures a worm’s dynamic infection sequences (e.g., probing, exploitation, and replication) by modeling each interaction step as a behavior phenotype and denoting a complete infection process as a chained sequence. We argue that a self-propagating worm’s inherent behaviors or infection patterns can be detected and characterized by using sequence alignment tools, where patterns shared by the infection sequences will imply the behavioral footprints of the worm. A systematic platform called vEye has been built to validate the proposed design with either “live” or historical worms, where a number of real-world infection sequences are used to build worm behavioral footprints. Experimental comparisons with existing content-based fingerprints will demonstrate the uniqueness and effectiveness of the proposed behavior footprints in self-propagating worm detection and profiling.

Research of internet worm warning system based on system identification

In this paper, by analyzing the worm’s propagation model, we propose a new worm warning system based on the method of system identification, and use recursive least squares algorithm to estimate the worm’s infection rate. The simulation result shows the method we adopted is an efficient way to conduct Internet worm warning.     

分而治之的混合型良性蠕虫的建模与分析

由于良性蠕虫可以主动地防御蠕虫的传播,因此引起了蠕虫研究领域专家的广泛关注.通过分析分而治之的混合型良性蠕虫的特点,将其划分为3个子类.在有延迟以及无延迟的情况下.推导了分而治之的混合型良性蠕虫的3个子类的数学传播模型,这些传播模型描述了分而治之的混合型良性蠕虫对抗蠕虫传播的过程.最后,仿真实验验证了传播模型,并且得到如下结论:在相同的感染条件下,复合的分而治之的混合型良性蠕虫抑制蠕虫传播的效果最好.     

网络蠕虫目标发现策略研究

要有效地防止蠕虫攻击,了解它的传播是很关键的.主要对蠕虫的目标发现策略进行研究,分析目标发现策略在蠕虫传播中的作用,给出了一种蠕虫目标发现策略的分类并对每种策略进行探讨.     

Characterizing and defending against divide-conquer-scanning worms

Internet worms are a significant security threat. Divide-conquer scanning is a simple yet effective technique that can potentially be exploited for future Internet epidemics. Therefore, it is imperative that defenders understand the characteristics of divide-conquer-scanning worms and study the effective countermeasures. In this work, we first examine the divide-conquer-scanning worm and its potential to spread faster and stealthier than a traditional random-scanning worm. We then characterize the relationship between the propagation speed of divide-conquer-scanning worms and the distribution of vulnerable hosts through mathematical analysis and simulations. Specifically, we find that if vulnerable hosts follow a non-uniform distribution such as the Witty-worm victim distribution, divide-conquer scanning can spread a worm much faster than random scanning. We also empirically study the effect of important parameters on the spread of divide-conquer-scanning worms and a worm variant that can potentially enhance the infection ability at the late stage of worm propagation. Furthermore, to counteract such attacks, we discuss the weaknesses of divide-conquer scanning and study two defense mechanisms: infected-host removal and active honeynets. We find that although the infected-host removal strategy can greatly reduce the number of final infected hosts, active honeynets (especially uniformly distributed active honeynets) are more practical and effective to defend against divide-conquer-scanning worms.     

DAW: A Distributed Antiworm System

A worm automatically replicates itself across networks and may infect millions of servers in a short period of time. It is conceivable that the cyberterrorists may use a widespread worm to cause major disruption to the Internet economy. Much recent research concentrates on propagation models and early warning, but the defense against worms is largely an open problem. We propose a distributed antiworm architecture (DAW) that automatically slows down or even halts the worm propagation within an Internet service provider (ISP) network. New defense techniques are developed based on the behavioral difference between normal hosts and worm-infected hosts. Particularly, a worm-infected host has a much higher connection-failure rate when it randomly scans the Internet. This property allows DAW to set the worms apart from the normal hosts. We propose a temporal rate-limit algorithm and a spatial rate-limit algorithm, which makes the speed of worm propagation configurable by the parameters of the defense system. The effectiveness of the new techniques is evaluated analytically and by simulations.     

Strategy of fast and light-load cloud-based proactive benign worm countermeasure technology to contain worm propagation

Benign worms have been attracting wide attention in the field of worm research due to the proactive defense against the worm propagation and patch for the susceptible hosts. In this paper, two revised Worm?CAnti-Worm (WAW) models are proposed for cloud-based benign worm countermeasure. These Re-WAW models are based on the law of worm propagation and the two-factor model. One is the cloud-based benign Re-WAW model to achieve effective worm containment. Another is the two-stage Re-WAW propagation model, which uses proactive and passive switching defending strategy based on the ratio of benign worms to malicious worms. This model intends to avoid the network congestion and other potential risks caused by the proactive scan of benign worms. Simulation results show that the cloud-based Re-WAW model significantly improves the worm propagation containment effect. The cloud computing technology enables rapid delivery of massive initial benign worms, and the two stage Re-WAW model gradually clears off the benign worms with the containment of the malicious worms.     

基于复杂网络理论的即时通讯病毒研究

通过仿真分析了即时通讯病毒的传播特性,其传播速度非常迅速,并且在无标度网络上比在其他网络拓扑结构下传播得更快。单独增强用户的安全意识只能减少病毒的感染范围,不能减缓病毒的传播速度。结合复杂网络理论提出了监控和防御即时通讯病毒的一条思路,引入杀毒软件的病毒库的概念,使即时通讯软件能对已知病毒免疫。根据病毒的传播特点和即时通讯网络的无标度特性分别提出了基于IM客户端的监控方案和基于服务器端的病毒监控方案。     

Using artificial neural networks to detect unknown computer worms

Detecting computer worms is a highly challenging task. We present a new approach that uses artificial neural networks (ANN) to detect the presence of computer worms based on measurements of computer behavior. We compare ANN to three other classification methods and show the advantages of ANN for detection of known worms. We then proceed to evaluate ANN’s ability to detect the presence of an unknown worm. As the measurement of a large number of system features may require significant computational resources, we evaluate three feature selection techniques. We show that, using only five features, one can detect an unknown worm with an average accuracy of 90%. We use a causal index analysis of our trained ANN to identify rules that explain the relationships between the selected features and the identity of each worm. Finally, we discuss the possible application of our approach to host-based intrusion detection systems.     

基于生物免疫原理的网络蠕虫免疫模型

提出一种新的网络蠕虫传播模型,并基于生物免疫原理提出了成熟良性蠕虫、记忆良性蠕虫和疫苗良性蠕虫新概念,建立了新的主机状态转移关系,运用系统动力学理论和方法,建立了一种新的网络蠕虫免疫模型,它能够从定性和定量两方面分析和预测网络蠕虫免疫过程,并能够深入刻画恶性蠕虫和良性蠕虫交互过程中的网络特性,为动态防治网络蠕虫提供了新的理论依据。模拟实验结果表明,引入的三种良性蠕虫是动态防御恶性网络蠕虫传播的重要因素。     

Modeling and Simulation Study of the Propagation and Defense of Internet E-mail Worms

As many people rely on e-mail communications for business and everyday life, Internet e-mail worms constitute one of the major security threats for our society. Unlike scanning worms such as Code Red or Slammer, e-mail worms spread over a logical network defined by e-mail address relationships, making traditional epidemic models invalid for modeling the propagation of e-mail worms. In addition, we show that the topological epidemic models presented by M. Boguna, et al. (2000) largely overestimate epidemic spreading speed in topological networks due to their implicit homogeneous mixing assumption. For this reason, we rely on simulations to study e-mail worm propagation in this paper. We present an e-mail worm simulation model that accounts for the behaviors of e-mail users, including e-mail checking time and the probability of opening an e-mail attachment. Our observations of e-mail lists suggest that an Internet e-mail network follows a heavy-tailed distribution in terms of node degrees, and we model it as a power-law network. To study the topological impact, we compare e-mail worm propagation on power-law topology with worm propagation on two other topologies: small-world topology and random-graph topology. The impact of the power-law topology on the spread of e-mail worms is mixed: E-mail worms spread more quickly on a power-law topology than on a small-world topology or a random-graph topology, but immunization defense is more effective on a power-law topology.     

动态环境下的主动蠕虫攻击分析

鉴于当前很少有传播模型充分考虑到P2P节点动态特征对主动蠕虫攻击的影响, 提出两个动态环境下的主动蠕虫传播模型。分析了主动蠕虫两种常见的攻击方式, 给出了相应攻击背景下的节点状态转换过程, 在综合考虑P2P节点动态特征的基础上提出了两种主动蠕虫传播模型, 并对所提出的模型进行了数值分析, 探讨动态环境下影响主动蠕虫传播速度的关键因素。实验结果表明, 通过提高P2P节点的离线率和免疫力可以有效地抑制主动蠕虫对P2P网络的攻击。     

大规模蠕虫在线追踪培养皿

提出了一个用于反向追踪大规模网络蠕虫传播的虚拟实验环境，能够用于网络蠕虫检测和防御实验。实验环境使用虚拟机技术，虚拟大量主机和网络设备参加，尽量符合网络实际。在可控的范围内，使用真实的感染代码引发大规模蠕虫的爆发，观测蠕虫的传播过程。实验环境中可以发现蠕虫的传播特性，实时收集网络蠕虫的流量数据和感染过程。     

Contagion蠕虫传播仿真分析

Contagion 蠕虫利用正常业务流量进行传播,不会引起网络流量异常,具有较高的隐蔽性,逐渐成为网络安全的一个重要潜在威胁.为了能够了解Contagion蠕虫传播特性,需要构建一个合适的仿真模型.已有的仿真模型主要面向主动蠕虫,无法对Contagion蠕虫传播所依赖的业务流量进行动态模拟.因此,提出了一个适用于Contagion蠕虫仿真的Web和P2P业务流量动态仿真模型,并通过选择性抽象,克服了数据包级蠕虫仿真的规模限制瓶颈,在通用网络仿真平台上,实现了一个完整的Contagion蠕虫仿真系统.利用该系统,对Contagion蠕虫传播特性进行了仿真分析.结果显示:该仿真系统能够有效地用于Contagion蠕虫传播分析.     

Treating scalability and modelling human countermeasures against local preference worms via gradient models

A network worm is a specific type of malicious software that self propagates by exploiting application vulnerabilities in network-connected systems. Worm propagation models are mathematical models that attempt to capture the propagation dynamics of scanning worms as a means to understand their behaviour. It turns out that the emerged scalability in worm propagation plays an important role in order to describe the propagation in a realistic way. On the other hand human-based countermeasures also drastically affect the propagation in time and space. This work elaborates on a recent propagation model (Avlonitis et al. in J Comput Virol 3, 87–92, 2007) that makes use of Partial Differential Equations in order to treat correctly scalability and non-uniform behaviour (e.g., local preference worms). The aforementioned gradient model is extended in order to take into account human-based countermeasures that influence the propagation of local-preference worms in the Internet. Certain aspects of scalability emerged in random and local preference strategies are also discussed by means of random field considerations. As a result the size of a critical network that needs to be studied in order to describe the global propagation of a scanning worm is estimated. Finally, we present simulation results that validate the proposed analytical results and demonstrate the higher propagation rate of local preference worms compared with random scanning worms.     

P2P干预式蠕虫传播仿真分析*

对P2P干预式主动型蠕虫的传播机制进行了研究,指出其传播主要包括四个阶段:信息收集,攻击渗透、自我推进与干预激活。研究发现,P2P干预式蠕虫实际是一种拓扑蠕虫,能利用邻居节点信息准确地确定攻击目标,而且攻击非常隐蔽。采用仿真的方法研究了P2P相关参数对P2P干预式蠕虫传播的影响。仿真实验表明,潜伏主机激活率对干预式蠕虫传播的影响最大,而攻击率对干预式蠕虫传播的影响较小。     

基于拓扑结构的蠕虫防御策略仿真分析

提出了基于拓扑结构控制的蠕虫防御策略,并通过构建仿真模型对其进行了仿真验证分析.首先对蠕虫传播所依赖的拓扑结构的主要形式进行了分析,提出了相应的生成算法,并对算法的有效性进行了验证;随后提出了三种拓扑结构控制策略仿真模型;最后分别对这三种策略在不同拓扑结构下的蠕虫传播控制性能进行了仿真实验.实验结果证明:通过适当地控制拓扑结构,可以有效地遏制拓扑相关蠕虫传播.     

Design of a multi_agent system for worm spreading_reduction

With the explosive growth of Internet applications, the threats of network worms against computer systems and network security are seriously increasing. Many recent researches concentrate on providing a propagation model and early warning. In fact, the defense against worms in a realistic environment is an open problem. In this work, we present WSRMAS (worm spreading_reduction multi_agent system) as a system that includes a worm defense mechanism to considerably reduce the rate at which hosts are infected. As WSRMAS needs a suitable infra-structure, its architecture was elaborated and an agent platform was designed and implemented to support WSRMAS functions. The proposed system was provided once with a centralized plan and second with a decentralized (distributed) plan. In both cases the system performance was evaluated. Also different communication capabilities using Knowledge Query Manipulation Language (KQML) were exploited to improve WSRMAS performance. The ratio between worm and anti-worm spreading was studied to investigate its influence on the defense efficiency. Taking into account that some machines may not deploy WSRMAS, consequently, the effectiveness of WSRMAS under different operational conditions has been studied.     

被动型P2P蠕虫后期传播分析

复杂多变的网络环境使传统的蠕虫传播模型不能真实地反映被动型P2P蠕虫后期传播规律。针对该问题,通过建立蠕虫传播模型和仿真实验分析,揭示被动型P2P蠕虫后期传播的主要特征。结果表明,不修补漏洞的P2P节点数量和恢复系统后P2P节点及时修补漏洞的概率都与被动型P2P蠕虫传播有紧密的联系,在安全意识较低的网络环境中被动型P2P蠕虫可以持续传播。