基于域的P2P僵尸网络防御体系

针对当前传统安全技术不能对P2P环境下的僵尸网络进行有效防御的问题,在P2P僵尸网络病毒的一般性行为特征的基础上,设计了一种基于域的P2P僵尸网络的防御体系,并提出了利用僵局网络通信数据流特征向量的相似度分析解决因僵尸结点过少,无法检测出IP聚焦而无法识别僵尸结点的问题.该防御体系采用层次化结构,按P2P网络的逻辑地址段划分域,在城内采用将主机恶意行为与P2P流识别相结合的方法判别僵尸网络的通信数据流并提取特征向量.实验结果表明,该体系具有较高的性能和通用性.     

基于数据挖掘策略的P2P僵尸网络检测方法研究

僵尸网络由一群被病毒感染的计算机组成,它严重的威胁着Internet的安全。其原理是黑客把病毒植入到目标计算机,然后黑客通过Internet控制这些计算机来实施DDoS攻击、盗取认证信息、分发垃圾邮件和其他恶意行为。通过仿P2P软件,P2P僵尸网络用多个主控制器来避免单点丢失（single pointof failure）,并且使用加密技术使得各种各样的misuse detection技术失效。与正常网络行为不同的是,P2P僵尸网络建立了大量不占用带宽的会话,这就使它不会暴露在异常检测技术下。本文采用P2P僵尸网络不同于正常网络行为的特征作为数据挖掘的参数,然后对这些参数进行聚类并加以区分来获得可接受精度范围内可信任的结果。为了证明该方法在发现僵尸网络主机上的有效性,我们在实际的网络环境中进行了验证测试。     

基于主机行为异常的P2P僵尸网络在线检测方法

僵尸网络已经成为当前最为严重的网络威胁之一,其中P2P僵尸网络得到迅速发展,其自身的通信特征给检测带来巨大的挑战.针对P2P僵尸网络检测技术的研究已经引起研究人员的广泛关注.提出一种P2P僵尸网络在线检测方法,首先采用信息熵技术发现网络流量中的异常点,然后通过分析P2P僵尸网络中主机的行为异常,利用统计学中的假设检验技术,从正常的网络流量数据中识别出可疑P2P僵尸主机,同时根据僵尸主机通信模式的相似性进行最终确认.实验结果表明该方法能够有效实现P2P僵尸网络的在线检测.     

P2P僵尸网络研究

僵尸网络是近年来网络安全最严重的威胁之一.P2P僵尸网络是在传统僵尸网络基础上发展起来的,其命令与控制机制具有隐蔽性和健壮性,使检测和防范变得更加困难.本文对P2P僵尸网络的构建、命令与控制机制、检测与反制技术进行了研究与分析.     

P2P僵尸网络研究

P2P僵尸网络是一种新型网络攻击方式,因其稳定可靠、安全隐蔽的特性被越来越多地用于实施网络攻击,给网络安全带来严峻挑战.为深入理解P2P僵尸网络工作机理和发展趋势,促进检测技术研究,首先分析了P2P僵尸程序功能结构,然后对P2P僵尸网络结构进行了分类,并分析了各类网络结构的特点;在介绍了P2P僵尸网络生命周期的基础上,着重阐述了P2P僵尸网络在各个生命周期的工作机制;针对当前P2P僵尸网络检测研究现状,对检测方法进行了分类并介绍了各类检测方法的检测原理;最后对P2P僵尸网络的发展趋势进行了展望,并提出一种改进的P2P僵尸网络结构.     

动态自组织P2P僵尸网络的构建及其防御

僵尸网络作为大规模攻击活动的基础平台,严重威胁网络空间安全,从预测的角度对其开展研究具有重要的现实意义。针对现有研究在终端感知、身份识别和动态对抗中存在的不足,本文概括僵尸网络生命周期,总结P2P结构僵尸网络的脆弱点,建立P2P僵尸网络动态对抗模型,分析节点真实性判断和网络拓扑优化重构的重要性。在此基础上,从攻击者视角提出一种新颖的动态自组织P2P僵尸网络模型DSBot。该模型在架构设计上可扩展至各类目标设备,通过基于可信度矩阵和真实性验证的节点安全性评估机制增强终端对抗性,并提出分阶段感染策略。借鉴无线自组网和多智能体的思路和方法,刻画节点属性多维表示和基于状态标识的动态网络框架,以此为基础设计O(N_i)更新算法、均匀连接算法和节点主动移除算法,并结合相应的初始化和调整机制提出网络自组织重构策略,从而进一步提升网络的健壮性。其中,O(N_i)更新算法确保节点的可信度,均匀连接算法降低网络暴露风险,节点主动移除算法实时移除可疑节点。从平均等待时间、命令可达率、网络连接度和重构稳定时间等方面对DSBot模型进行评估。实验结果表明,DSBot模型在效率和韧性上可满足僵尸网络命令控制机制的基本需求。最后,从终端清除、命令控制服务器打击和命令控制过程等方面讨论了可能的防御策略。本文旨在通过预测新型僵尸网络模型来完善防御解决方案。     

P2P僵尸网络的传播建模与分析

为了有效控制自愿式P2P僵尸网络的大规模爆发,从动力学的角度研究了僵尸网络的传播规律.首先,根据僵尸网络的形成过程,建立了一个时滞微分方程模型;其次,通过详细的数学分析得出了有效控制僵尸网络的阈值表达式;最后,数值模拟验证了理论分析的正确性.理论分析和实验结果均表明,当基本再生数的值小于1时,僵尸网络可以被完全控制;否则,安全防御措施只能减小僵尸网络的规模.模拟结果还表明,降低僵尸程序的感染率或提高网络节点的免疫率,可以有效控制僵尸网络的爆发.实际网络管理中,可以通过不均匀分布网络节点、及时下载漏洞补丁等措施控制僵尸程序的传播.     

一种新颖的P2P僵尸网络检测技术

针对当前僵尸网络向P2P方向发展的趋势,在对P2P僵尸网络本质的理解和把握的基础上,提出了一种新颖的P2P僵尸网络检测技术。对于某个被监视的网络,关注其内部每台主机的通信行为和网络恶意活动。把这些通信行为和网络恶意活动分类,找出具有相似或相关通信和网络恶意行为的主机。根据我们对定义的理解,这些主机就属于某个P2P僵尸网络。     

一种新颖的P2P僵尸网络检测技术

针对当前僵尸网络向P2P方向发展的趋势,在对P2P僵尸网络本质的理解和把握的基础上,提出了一种新颖的P2P僵尸网络检测技术。对于某个被监视的网络,关注其内部每台主机的通信行为和网络恶意活动。把这些通信行为和网络恶意活动分类,找出具有相似或相关通信和网络恶意行为的主机。根据我们对定义的理解,这些主机就属于某个P2P僵尸网络。     

结构化对等网络中P2P 僵尸网络传播模型

依赖结构化对等网传播的P2P僵尸是未来互联网面临的重要威胁.详细分析了两种典型的结构化P2P协议Chord和Kademlia的工作原理,在此基础上,使用数学建模的方法建立了结构化P2P僵尸网络的传播模型.该模型将Kademlia,Chord协议与双因子免疫机制、主机在线率等因素相结合,较为全面地研究了两种典型的结构化P2P网络中僵尸的传播机理,并使用软件仿真的方法模拟了节点超过百万时,结构化P2P网络中僵尸的传播行为,通过软件仿真得出的数据与理论数据进行对比,验证了模型的正确性.从实验结果可以看出:对于Kademlia和Chord两种结构化P2P网络,僵尸传播无论是双因子免疫模型还是结合双因子与主机在线率的模型,理论模型与仿真结果都非常吻合,体现了模型的准确性,为僵尸的检测与防御提供了理论依据.     

基于统计特征的隐匿P2P主机实时检测系统

针对当前隐匿恶意程序多转为使用分布式架构来应对检测和反制的问题,为快速精确地检测出处于隐匿阶段的对等网络(P2P)僵尸主机,最大限度地降低其危害,提出了一种基于统计特征的隐匿P2P主机实时检测系统。首先,基于3个P2P主机统计特征采用机器学习方法检测出监控网络内的所有P2P主机;然后,再基于两个P2P僵尸主机统计特征,进一步检测出P2P僵尸主机。实验结果证明,所提系统能在5 min内检测出监控网内所有隐匿的P2P僵尸主机,准确率高达到99.7%,而误报率仅为0.3%。相比现有检测方法,所提系统检测所需统计特征少,且时间窗口较小,具备实时检测的能力。     

Detecting P2P botnets by discovering flow dependency in C&C traffic

Botnets are widely used by attackers and they have evolved from centralized structures to distributed structures. Most of the modern P2P bots launch attacks in a stealthy way and the detection approaches based on the malicious traffic of bots are inefficient. In this paper, an approach that aims to detect Peer-to-Peer (P2P) botnets is proposed. Unlike previous works, the approach is independent of any malicious traffic generated by bots and does not require bots’ information provided by external systems. It detects P2P bots by focusing on the instinct characteristics of their Command and Control (C&C) communications, which are identified by discovering flow dependencies in C&C traffic. After discovering the flow dependencies, our approach distinguishes P2P bots and normal hosts by clustering technique. Experimental results on real-world network traces merged with synthetic P2P botnet traces indicate that 1) flow dependency can be used to detect P2P botnets, and 2) the proposed approach can detect P2P botnets with a high detection rate and a low false positive rate.     

双层架构的P2P Botnet研究

Botnet(僵尸网络)是由bot(僵尸主机)组成的可通信、可被攻击者控制的网络,而P2P botnet是一种利用P2P技术构建控制信道的僵尸网络.对比于以往具有明显追踪特征的P2P botnet而言,一种基于双层架构的P2P botnet在控制感染主机方面采用了更加隐蔽和灵活的方式,使追踪难度增大.通过模拟实验对基于这种通信控制模型的僵尸网络进行了功能和性能方面的研究,并提出了相应的防御与追踪方案.     

Detecting P2P bots by mining the regional periodicity

Peer-to-peer (P2P) botnets outperform the traditional Internet relay chat (IRC) botnets in evading detection and they have become a prevailing type of threat to the Internet nowadays.Current methods for detecting P2P botnets,such as similarity analysis of network behavior and machine-learning based classification,cannot handle the challenges brought about by different network scenarios and botnet variants.We noticed that one important but neglected characteristic of P2P bots is that they periodically send requests to update their peer lists or receive commands from botmasters in the command-and-control (C&C) phase.In this paper,we propose a novel detection model named detection by mining regional periodicity (DMRP),including capturing the event time series,mining the hidden periodicity of host behaviors,and evaluating the mined periodic patterns to identify P2P bot traffic.As our detection model is built based on the basic properties of P2P protocols,it is difficult for P2P bots to avoid being detected as long as P2P protocols are employed in their C&C.For hidden periodicity mining,we introduce the so-called regional periodic pattern mining in a time series and present our algorithms to solve the mining problem.The experimental evaluation on public datasets demonstrates that the algorithms are promising for efficient P2P bot detection in the C&C phase.     

一种高鲁棒性的新型P2P僵尸网络

提出一种利用认证sensor组建的蜜罐先知型半分布式P2P僵尸网络(Botnet),通过连接比C(p)和度数比D(p)2个度量函数,并在peer-list更新过程中使用不同数量servent bots,讨论其鲁棒性的变化。结果表明,与传统Botnet相比,该类Botnet具有较高的鲁棒性。     

MRRbot: 基于冗余机制的多角色P2P僵尸网络模型

对现有僵尸网络的防御已取得很大成效,但僵尸网络不断演变进化,尤其在三网融合不断推进的背景下,这给防御者带来新的挑战.因此,预测未来僵尸网络以及时应对,非常必要.提出了一种基于冗余机制的多角色P2P僵尸网络模型(MRRbot),该模型引入虚壳僵尸终端,能够很大程度上验证僵尸终端的软硬件环境,增强其可信度和针对性;采用信息冗余机制和服务终端遴选算法,使僵尸终端能够均衡、高效地访问服务终端,提高命令控制信道的健壮性和抗毁性.对MRRbot的可控性、时效性和抗毁性进行了理论分析和实验评估,并就其健壮性与前人工作进行了比较.结果表明,MRRbot能够高效下发指令,有效对抗防御,更具威胁.探讨了可能的防御策略,提出基于志愿者网络的防御体系.     

Scalable P2P bot detection system based on network data stream

P2P Botnets are one of the most malevolent threats to the Internet users due to their resiliency against takedown efforts. In this paper, we propose a bot detection system that is capable of detecting stealthy bots in a network. This system treats network traffic as a data stream, segregating the traffic into two parallel streams. The detection is based on failure traffic and communication traffic. The traffic is analyzed during small time window, and the infected hosts are reported immediately. The network administrator can monitor the status of hosts in the network and can take the necessary action before the infected hosts harm the system or can involve in the attacks. Experiments and evaluation of the proposed system on a variety of P2P data transfer applications and P2P botnets have demonstrated high accuracy of detection. The scalability of the proposed system is exhibited through its implementation on Hadoop MapReduce.     

Tsunami: A parasitic,indestructible botnet on Kad

While current botnets rely on a central server or bootstrap nodes for their operations, in this paper we identify and investigate a new type of botnet, called Tsunami, in which no such bottleneck nodes exist. In particular, we study how a Tsunami botnet can build a parasitic relationship with a widely deployed P2P system, Kad, to successfully issue commands to its bots, launch various attacks, including distributed denial of service (DDoS) and spam, at ease, as well as receive responses from the bots. Our evaluation shows that in a Kad network with four million nodes, even with only 6 % nodes being Tsunami bots, Tsunami can reach 75 % of its bots in less than 4 min and receive responses from 99 % of bots. We further propose how we may defend against Tsunami and evaluate the defense solution.     

Resource monitoring for the detection of parasite P2P botnets

Detecting botnet behaviors in networks is a popular topic in the current research literature. The problem of detection of P2P botnets has been denounced as one of the most difficult ones, and this is even sounder when botnets use existing P2P networks infrastructure (parasite P2P botnets). The majority of the detection proposals available at present are based on monitoring network traffic to determine the potential existence of command-and-control communications (C&C) between the bots and the botmaster. As a different and novel approach, this paper introduces a detection scheme which is based on modeling the evolution of the number of peers sharing a resource in a P2P network over time. This allows to detect abnormal behaviors associated to parasite P2P botnet resources in this kind of environments. We perform extensive experiments on Mainline network, from which promising detection results are obtained while patterns of parasite botnets are tentatively discovered.     

ARTiFACIAL: Automated Reverse Turing test using FACIAL features

Web services designed for human users are being abused by computer programs (bots). The bots steal thousands of free e-mail accounts in a minute, participate in online polls to skew results, and irritate people by joining online chat rooms. These real-world issues have recently generated a new research area called human interactive proofs (HIP), whose goal is to defend services from malicious attacks by differentiating bots from human users. In this paper, we make two major contributions to HIP. First, based on both theoretical and practical considerations, we propose a set of HIP design guidelines that ensure a HIP system to be secure and usable. Second, we propose a new HIP algorithm based on detecting human face and facial features. Human faces are the most familiar object to humans, rendering it possibly the best candidate for HIP. We conducted user studies and showed the ease of use of our system to human users. We designed attacks using the best existing face detectors and demonstrated the challenge they presented to bots.