An evaluation of current and future botnet defenses

Botnets are large networks of compromised machines under the control of a bot master. These botnets constantly evolve their defences to allow the continuation of their malicious activities. The constant development of new botnet mitigation strategies and their subsequent defensive countermeasures has lead to a technological arms race, one which the bot masters have significant incentives to win. This dissertation analyzes the current and future states of the botnet arms race by introducing a taxonomy of botnet defences and a simulation framework for evaluating botnet techniques. The taxonomy covers current botnet techniques and highlights possible future techniques for further analysis under the simulation framework. This framework allows the evaluation of the effect techniques such as reputation systems and proof of work schemes have on the resources required to disable a peer-to-peer botnet. Given the increase in the resources required, our results suggest that the prospects of eliminating the botnet threat are limited.

Protecting web 2.0 services from botnet exploitations

Recently, botnet, a network of compromised computers, has been recognized as the biggest threat to the Internet. The bots in a botnet communicate with the botnet owner via a communication channel called Command and Control (C & C) channel. There are three main C & C channels: Internet Relay Chat (IRC), Peer-to-Peer (P2P) and web-based protocols. By exploiting the flexibility of the Web 2.0 technology, the web-based botnet has reached a new level of sophistication. In August 2009, such botnet was found on Twitter, one of the most popular Web 2.0 services. In this paper, we will describe a new type of botnet that uses Web 2.0 service as a C & C channel and a temporary storage for their stolen information. We will then propose a novel approach to thwart this type of attack. Our method applies a unique identifier of the computer, an encryption algorithm with session keys and a CAPTCHA verification.

Botnet-en azterketa eta analisia

Egun sare informatikoak ezinbesteko tresna bilakatu dira eguneroko eginkizun askotarako. Horren ondorioz, mota guztietako informazio mordoa garraiatzen da sarean barna. Sarearen erabilerak hainbat abantaila ekarri ditu, baina baita arriskuak ere. Sareko informazio horri guztiari etekina atera nahian zenbait informazio lapur eta zibergaizkilek tresnak garatzen dituzte etengabe. Arazo horri aurre egiteko babes mekanismo ugari garatu dituzte segurtasun aditu eta eragileek. Baina era berean, mekanismo horiek gainditzeko erasoak automatizatzeko eta indartzeko gai diren tresnak berriak agertu dira. Azken hauen artean kokatzen dira botnet-ak, gaur egungo mehatxu handienetako bat segurtasun aditu askoren iritziz. Botnetak kontroladore baten edo batzuen agindupean egon daitezkeen makina multzoak dira. Makina horiek, bot edo zombie izenez ezagunak, ezkutuan martxan dagoen software bati esker kontrolatu ohi dira. Jatorrian bot-en mekanismoa atazak automatizatzeko erabiltzen bazen ere, gaur egun ezaugarri hori aprobetxatuz erasoak eta beste motako ekintza ez-zilegi batzuk egiteko erabiltzen dira. Botneten tamaina milaka makinakoa izatera irits daiteke. Horri esker egin ditzaketen erasoen ahalmena handitu egiten da eta, ondorioz, etekin handiagoa ateratzeko aukerak handitzen dira ere. Beste ezaugarri nagusienetako bat malwareren bati esker kontrolpean dauden makinen jabeak ohartu gabe funtzionatzea da. Azken urteotan botneten hazkundea nabarmena izan da eta izugarrizko mehatxua bilakatu dira sarearen funtzionamendurako eta sareko sistemen segurtasunerako. Horrek motibatuta garatu da proiektu hau. Funtsean botnetak zer diren, hauen bilakaera eta nola funtzionatzen duten azaldu nahi da. Segurtasun neurri batzuk ere aztertzen dira. Azkenik, azterketa praktikoa ere lantzen da, Zeus eta Flu izeneko botnetak modu lokalean probatuz.

A survey on latest botnet attack and defense

A botnet is a group of compromised computers, which are remotely controlled by hackers to launch various network attacks, such as DDoS attack and information phishing. Botnet has become a popular and productive tool behind many cyber attacks. Recently, the owners of some botnets, such as storm worm, torpig and conflicker, are employing fluxing techniques to evade detection. Therefore, the understanding of their fluxing tricks is critical to the success of defending from botnet attacks. Motivated by this, we survey the latest botnet attacks and defenses in this paper. We begin with introducing the principles of fast fluxing (FF) and domain fluxing (DF), and explain how these techniques were employed by botnet owners to fly under the radar. Furthermore, we investigate the state-of-art research on fluxing detection. We also compare and evaluate those fluxing detection methods by multiple criteria. Finally, we discuss future directions on fighting against botnet based attacks.

The probability model of peer-to-peer botnet propagation

Active Peer-to-Peer worms are great threat to the network security since they can propagate in automated ways and flood the Internet within a very short duration. Modeling a propagation process can help us to devise effective strategies against a worm's spread. This paper presents a study on modeling a worm's propagation probability in a P2P overlay network and proposes an optimized patch strategy for defenders. Firstly, we present a probability matrix model to construct the propagation of P2P worms. Our model involves three indispensible aspects for propagation: infected state, vulnerability distribution and patch strategy. Based on a fully connected graph, our comprehensive model is highly suited for real world cases like Code Red II. Finally, by inspecting the propagation procedure, we propose four basic tactics for defense of P2P botnets. The rationale is exposed by our simulated experiments and the results show these tactics are of effective and have considerable worth in being applied in real-world networks.

Detecting SMS-based control commands in a Botnet from infected android devices

An increasing number of Android devices are being infected and at risk of becoming part of a botnet. Among all types of botnets, control and cornmand based botnets are very popular. In this paper we introduce an effective and efficient method to ddect SMS-based control commands ftvm infected Android devices. Specifically, we rely on the important radio activities recorded in Android log files. These radio activities are currently overlooked by researchers. We show the effectiveness of our rnethod by using the examples frorn published literature. Our method requires much less user knowledge but is more generic than traditional approaches.

Overcoming reputation and proof-of-work systems in botnets

Reputation and proof-of-work systems have been outlined as methods bot masters will soon use to defend their peer-to-peer botnets. These techniques are designed to prevent sybil attacks, such as those that led to the downfall of the Storm botnet. To evaluate the effectiveness of these techniques, a botnet that employed these techniques was simulated, and the amount of resources required to stage a successful sybil attack against it measured. While the proof-of-work system was found to increase the resources required for a successful sybil attack, the reputation system was found to lower the amount of resources required to disable the botnet.

Parametric differences between a real-world distributed denial-of-service attack and a flash event

Distributed Denial-of-Service (DDoS) attacks continue to be one of the most pernicious threats to the delivery of services over the Internet. Not only are DDoS attacks present in many guises, they are also continuously evolving as new vulnerabilities are exploited. Hence accurate detection of these attacks still remains a challenging problem and a necessity for ensuring high-end network security. An intrinsic challenge in addressing this problem is to effectively distinguish these Denial-of-Service attacks from similar looking Flash Events (FEs) created by legitimate clients. A considerable overlap between the general characteristics of FEs and DDoS attacks makes it difficult to precisely separate these two classes of Internet activity. In this paper we propose parameters which can be used to explicitly distinguish FEs from DDoS attacks and analyse two real-world publicly available datasets to validate our proposal. Our analysis shows that even though FEs appear very similar to DDoS attacks, there are several subtle dissimilarities which can be exploited to separate these two classes of events.

基于P2P的僵尸网络及其防御

僵尸网络作为网络犯罪活动的平台,正朝着P2P等分布式结构发展.研究僵尸网络的发展方向以及构建技术,有助于我们全面地了解僵尸网络活动的特点,从而更好地开展僵尸网络的检测和防范研究.本文分析了攻击者的需求,提出了一种基于层次化P2P网络技术的新型僵尸网络结构,并对这种僵尸网络的可行性和具体的传播、通讯、控制等各个方面进行了深入分析和探讨.在此基础上,我们通过模拟实验对各种防御策略的有效性进行了分析和评估,实验数据表明,在考虑实际可操作性条件下,现有的防御策略难以有效摧毁P2P结构僵尸网络.最后,我们讨论了这种新型僵尸网络可能的防御方法.

Lutte aux botnets : les politiques de prévention s'avèrent-elles efficaces?

Les alertes que nos logiciels antivirus nous envoient ou encore les différents reportages diffusés dans les médias nous font prendre conscience de l'existence des menaces dans le cyberespace. Que ce soit les pourriels, les attaques par déni de service ou les virus, le cyberespace regorge de menaces qui persistent malgré les efforts qui sont déployés dans leur lutte. Est-ce que cela a à voir avec l'efficacité des politiques en place actuellement pour lutter contre ce phénomène? Pour y répondre, l'objectif général du présent mémoire est de vérifier quelles sont les politiques de prévention (lois anti-pourriel, partenariats publics-privés et démantèlements de botnets) qui influencent le plus fortement le taux de menaces informatiques détectées, tout en s'attardant également à l'effet de différents facteurs socio-économiques sur cette variable. Les données collectées par le logiciel antivirus de la compagnie ESET ont été utilisées. Les résultats suggèrent que les partenariats publics-privés offrant une assistance personnalisée aux internautes s'avèrent être la politique de prévention la plus efficace. Les démantèlements de botnets peuvent également s'avérer efficaces, mais seulement lorsque plusieurs acteurs/serveurs importants du réseau sont mis hors d'état de nuire. Le démantèlement du botnet Mariposa en est un bon exemple. Les résultats de ce mémoire suggèrent que la formule partenariats-démantèlements serait le choix le plus judicieux pour lutter contre les cybermenaces. Ces politiques de prévention possèdent toutes deux des méthodes efficaces pour lutter contre les menaces informatiques et c'est pourquoi elles devraient être mises en commun pour assurer une meilleure défense contre ce phénomène.

INFO2009 - Computer Crime Learning Resource

An E-Learning Gateway for the latest news and information relating to Computer Crime for INFO2009

Browsing behavior mimicking attacks on popular web sites for large botnets

With the significant growth of botnets, application layer DDoS attacks are much easier to launch using large botnet, and false negative is always a problem for intrusion detection systems in real practice. In this paper, we propose a novel application layer DDoS attack tool, which mimics human browsing behavior following three statistical distributions, the Zipf-like distribution for web page popularity, the Pareto distribution for page request time interval for an individual browser, and the inverse Gaussian distribution for length of browsing path. A Markov model is established for individual bot to generate attack request traffic. Our experiments indicated that the attack traffic that generated by the proposed tool is pretty similar to the real traffic. As a result, the current statistics based detection algorithms will result high false negative rate in general. In order to counter this kind of attacks, we discussed a few preliminary solutions at the end of this paper.

Reliable aggregation on network traffic for web based knowledge discovery

The web is a rich resource for information discovery, as a result web mining is a hot topic. However, a reliable mining result depends on the reliability of the data set. For every single second, the web generate huge amount of data, such as web page requests, file transportation. The data reflect human behavior in the cyber space and therefore valuable for our analysis in various disciplines, e.g. social science, network security. How to deposit the data is a challenge. An usual strategy is to save the abstract of the data, such as using aggregation functions to preserve the features of the original data with much smaller space. A key problem, however is that such information can be distorted by the presence of illegitimate traffic, e.g. botnet recruitment scanning, DDoS attack traffic, etc. An important consideration in web related knowledge discovery then is the robustness of the aggregation method , which in turn may be affected by the reliability of network traffic data. In this chapter, we first present the methods of aggregation functions, and then we employe information distances to filter out anomaly data as a preparation for web data mining.

Polymorphic malware detection using Hierarchical Hidden Markov Model

Binary signatures have been widely used to detect malicious software on the current Internet. However, this approach is unable to achieve the accurate identification of polymorphic malware variants, which can be easily generated by the malware authors using code generation engines. Code generation engines randomly produce varying code sequences but perform the same desired malicious functions. Previous research used flow graph and signature tree to identify polymorphic malware families. The key difficulty of previous research is the generation of precisely defined state machine models from polymorphic variants. This paper proposes a novel approach, using Hierarchical Hidden Markov Model (HHMM), to provide accurate inductive inference of the malware family. This model can capture the features of self-similar and hierarchical structure of polymorphic malware family signature sequences. To demonstrate the effectiveness and efficiency of this approach, we evaluate it with real malware samples. Using more than 15,000 real malware, we find our approach can achieve high true positives, low false positives, and low computational cost.