A self‐learning stream classifier for flow‐based botnet detection

Botnets have been recently recognized as one of the most formidable threats on the Internet. Different approaches have been designed to detect these types of attacks. However, as botnets evolve their behavior to mislead the signature‐based detection systems, learning‐based methods may be deployed to provide a generalization capacity in identifying unknown botnets. Developing an adaptable botnet detection system, which incrementally evolves with the incoming flow stream, remains as a challenge. In this paper, a self‐learning botnet detection system is proposed, which uses an adaptable classification model. The system uses an ensemble classifier and, in order to enhance its generalization capacity, updates its model continuously on receiving new unlabeled traffic flows. The system is evaluated with a comprehensive data set, which contains a wide variety of botnets. The experiments demonstrate that the proposed system can successfully adapt in a dynamic environment where new botnet types are observed during the system operation. We also compare the system performance with other methods.     

僵尸网络活动调查分析

僵尸网络已经成为网络攻击者首选的攻击平台，用以发起分布式拒绝服务攻击、窃取敏感信息和发送垃圾邮件等，对公共互联网的正常运行和互联网用户的利益造成了严重的威胁。较大规模地发现和监测实际僵尸网络的活动行为并对其规律进行深入调查分析，是更为全面地监测僵尸网络和对其实施反制的必要前提。通过对所监测的1961个实际僵尸网络的活动情况进行了深入调查和分析，从中给出了僵尸网络数量增长情况、控制服务器分布、僵尸网络规模、被控主机分布以及僵尸网络各种攻击行为的分析结果。     

基于深度学习的僵尸网络检测技术研究

入侵检测系统通过分析网络流量来学习正常和异常行为,并能够检测到未知的攻击。一个入侵检测系统的性能高度依赖于特征的设计,而针对不同入侵的特征设计则是一个很复杂的问题。因此,提出了一种基于深度学习检测僵尸网络的系统。该系统利用卷积神经网络(Convolutional Neural Network,CNN)和长短期记忆网络(Long Short-Term Memory,LSTM)分别学习网络流量的空间特征和时序特征,而特征学习的整个过程由深度神经网络自动完成,不依赖于人工设计特征。实验结果表明,该系统在僵尸网络检测方面具有良好的表现。     

基于分布式的僵尸网络主动探测方法研究

僵尸网络是当前互联网上存在的一类严重安全威胁。传统的被动监控方法需要经过证据积累、检测和反应的过程,只能在实际恶意活动发生之后发现僵尸网络的存在。提出了基于僵尸网络控制端通信协议指纹的分布式主动探测方法,通过逆向分析僵尸网络的控制端和被控端样本,提取僵尸网络通信协议,并从控制端回复信息中抽取通信协议交互指纹,最后基于通信协议指纹对网络上的主机进行主动探测。基于该方法,设计并实现了ActiveSpear主动探测系统,该系统采用分布式架构,扫描所使用的IP动态变化,支持对多种通信协议的僵尸网络控制端的并行扫描。在实验环境中对系统的功能性验证证明了方法的有效性,实际环境中对系统扫描效率的评估说明系统能够在可接受的时间内完成对网段的大规模扫描。     

僵尸网络检测研究

僵尸网络是一种严重威胁网络安全的攻击平台。文章先给出僵尸网络的定义,然后分析其工作机制,命令与控制机制。针对当前主流的僵尸网络检测方法,按照不同的行为特征进行分类,根据僵尸网络的静态特征、动态特征以及混合特征,对当前的主要检测方法进行了归纳、分析和总结。并在文章最后提出,建立一个完备的僵尸网络检测模型需要将僵尸网络的动态特征检测模型与静态特征检测模型相互结合,而这才是僵尸网络检测模型未来发展的重点。     

基于决策树的僵尸流量检测方法研究

僵尸网络目前是互联网面临的安全威胁之一,检测网络中潜在的僵尸网络流量对提高互联网安全性具有重要意义。论文重点研究了基于IRC协议的僵尸网络,以僵尸主机与聊天服务器之间的会话特征为基础,提出了一种基于决策树的僵尸网络流量检测方法。实验证明该方法是可行的。     

Adaptive traffic sampling for P2P botnet detection

Peer‐to‐peer (P2P) botnets have become one of the major threats to network security. Most existing botnet detection systems detect bots by examining network traffic. Unfortunately, the traffic volumes typical of current high‐speed Internet Service Provider and enterprise networks are challenging for these network‐based systems, which perform computationally complex analyses. In this paper, we propose an adaptive traffic sampling system that aims to effectively reduce the volume of traffic that P2P botnet detectors need to process while not degrading their detection accuracy. Our system first identifies a small number of potential P2P bots in high‐speed networks as soon as possible, and then samples as many botnet‐related packets as possible with a predefined target sampling rate. The sampled traffic then can be delivered to fine‐grained detectors for further in‐depth analysis. We evaluate our system using traffic datasets of real‐world and popular P2P botnets. The experiments demonstrate that our system can identify potential P2P bots quickly and accurately with few false positives and greatly increase the proportion of botnet‐related packets in the sampled packets while maintain the high detection accuracy of the fine‐grained detectors.     

Modeling and countermeasures of a social network-based botnet with strong destroy-resistance

To defeat botnets and ensure cyberspace security,a novel social network-based botnet with strong destroy-resistance (DR-SNbot),as well as its corresponding countermeasure,was proposed.DR-SNbot constructed command and control servers (C＆C-Servers) based on social network.Each C＆C-Server corresponded to a unique pseudo-random nickname.The botmaster issues commanded by hiding them in diaries using information hiding techniques,and then a novel C＆C channel was established.When different proportions of C＆C-Servers were invalid,DR-SNbot would send out different levels of alarms to inform attackers to construct new C＆C-Servers.Then,DR-SNbot could automatically repair C＆C communication to ensure its strong destroy-resistance.Under the experimental settings,DR-SNbot could resume the C＆C communication in a short period of time to keep 100% of the control rate even if all the current C＆C-Servers were invalid.Finally,a botnet nickname detecting method was proposed based on the difference of lexical features of legal nicknames and pseudo-random nicknames.Experimental results show that the proposed method can effectively (precision:96.88%,recall:93%) detect pseudo-random nicknames generated by social network-based botnets with customized algorithms.     

BotCatcher:botnet detection system based on deep learning

Machine learning technology has wide application in botnet detection.However,with the changes of the forms and command and control mechanisms of botnets,selecting features manually becomes increasingly difficult.To solve this problem,a botnet detection system called BotCatcher based on deep learning was proposed.It automatically extracted features from time and space dimension,and established classifier through multiple neural network constructions.BotCatcher does not depend on any prior knowledge which about the protocol and the topology,and works without manually selecting features.The experimental results show that the proposed model has good performance in botnet detection and has ability to accurately identify botnet traffic .     

隐马尔可夫模型在P2P僵尸网络检测中的应用

针对P2P僵尸网络的特点,将隐马尔可夫模型应用于P2P僵尸网络检测技术中.首先根据当前僵尸网络的发展状况及存在的问题分析了P2P僵尸网络的生命周期和行为特征;然后对僵尸主机的状态划分采用隐马尔可夫模型对P2P僵尸网络进行数学建模,并提出一种P2P僵尸网络的检测方法.通过实验,验证了检测方法的可靠性和合理性.