Efficient botnet herding within the Tor network

During 2013 the Tor network had a massive spike in new users as a botnet started using Tor hidden services to hide its C&C (Command and Control) servers. This resulted in network congestion and reduced performance for all users. Tor hidden services are attractive to botnet herders because they provide anonymity for both the C&C servers and the bots. The aim of this paper is to present a superior way that Tor hidden services can be used for botnet C&C which minimises harm to the Tor network while retaining all security benefits.     

Peri-Watchdog: Hunting for hidden botnets in the periphery of online social networks

In order to evade detection of ever-improving defense techniques, modern botnet masters are constantly looking for new communication platforms for delivering C&C (Command and Control) information. Attracting their attention is the emergence of online social networks such as Twitter, as the information dissemination mechanism provided by these networks can naturally be exploited for spreading botnet C&C information, and the enormous amount of normal communications co-existing in these networks makes it a daunting task to tease out botnet C&C messages.Against this backdrop, we explore graph-theoretic techniques that aid effective monitoring of potential botnet activities in large open online social networks. Our work is based on extensive analysis of a Twitter dataset that contains more than 40 million users and 1.4 billion following relationships, and mine patterns from the Twitter network structure that can be leveraged for improving efficiency of botnet monitoring. Our analysis reveals that the static Twitter topology contains a small-sized core sugraph, after removing which, the Twitter network breaks down into small connected components, each of which can be handily monitored for potential botnet activities. Based on this observation, we propose a method called Peri-Watchdog, which computes the core of a large online social network and derives the set of nodes that are likely to pass botnet C&C information in the periphery of online social network. We analyze the time complexity of Peri-Watchdog under its normal operations. We further apply Peri-Watchdog on the Twitter graph injected with synthetic botnet structures and investigate the effectiveness of Peri-Watchdog in detecting potential C&C information from these botnets.To verify whether patterns observed from the static Twitter graph are common to other online social networks, we analyze another online social network dataset, BrightKite, which contains evolution of social graphs formed by its users in half a year. We show not only that there exists a similarly relatively small core in the BrightKite network, but also this core remains stable over the course of BrightKite evolution. We also find that to accommodate the dynamic growth of BrightKite, the core has to be updated about every 18 days under a constrained monitoring capacity.     

Automatic protocol reverse-engineering: Message format extraction and field semantics inference

Understanding the command-and-control (C&C) protocol used by a botnet is crucial for anticipating its repertoire of nefarious activity. However, the C&C protocols of botnets, similar to many other application layer protocols, are undocumented. Automatic protocol reverse-engineering techniques enable understanding undocumented protocols and are important for many security applications, including the analysis and defense against botnets. For example, they enable active botnet infiltration, where a security analyst rewrites messages sent and received by a bot in order to contain malicious activity and to provide the botmaster with an illusion of successful and unhampered operation.In this work, we propose a novel approach to automatic protocol reverse engineering based on dynamic program binary analysis. Compared to previous work that examines the network traffic, we leverage the availability of a program that implements the protocol. Our approach extracts more accurate and complete protocol information and enables the analysis of encrypted protocols. Our automatic protocol reverse-engineering techniques extract the message format and field semantics of protocol messages sent and received by an application that implements an unknown protocol specification. We implement our techniques into a tool called Dispatcher and use it to analyze the previously undocumented C&C protocol of MegaD, a spam botnet that at its peak produced one third of the spam on the Internet.     

Botnet command and control based on Short Message Service and human mobility

Many serious threats for PCs are spreading to the mobile environment. A mobile botnet, which is a collection of hijacked smartphones under the control of hackers, is one of them. With the quick development of the computing and communication abilities of smartphones, many command and control (C&C) techniques in PC botnets can be easily reused in mobile botnets. However, some particular functions and characteristics of smartphones may provide botmasters with additional means to control their mobile botnets. This paper presents two special C&C mechanisms that leverage Short Message Service and human mobility, respectively. The first one designs a SMS-based flooding algorithm to propagate commands. We theoretically prove that the uniform random graph is the optimal topology for this botnet, and demonstrate its high efficiency and stealth with various simulations. The second one utilizes Bluetooth to transmit botnet commands when hijacked smartphones encounter each other while in motion. We study its performance in a 100 m × 100 m square area with NS-2 simulations, and show that human-mobility characteristics facilitate the command propagation. Even if the infection rate is low, the command can still be effectively propagated provided that the mobility of devices is high. In the end, we propose effective defense strategies against these two special C&C mechanisms.     

New facets of mobile botnet: architecture and evaluation

It is without a doubt that botnets pose a growing threat to the Internet, with DDoS attacks of any kind carried out by botnets to be on the rise. Nowadays, botmasters rely on advanced Command and Control (C&C) infrastructures to achieve their goals and most importantly to remain undetected. This work introduces two novel botnet architectures that consist only of mobile devices and evaluates both their impact in terms of DNS amplification and TCP flooding attacks, and their cost pertaining to the maintenance of the C&C channel. The first one puts forward the idea of using a continually changing mobile HTTP proxy in front of the botherder, while the other capitalizes on DNS protocol as a covert channel for coordinating the botnet. That is, for the latter, the messages exchanged among the bots and the herder appear as legitimate DNS transactions. Also, a third architecture is described and assessed, which is basically an optimized variation of the first one. Namely, it utilizes a mixed layout where all the attacking bots are mobile, but the proxy machines are typical PCs not involved in the actual attack. For the DNS amplification attack, which is by nature more powerful, we report an amplification factor that fluctuates between 32.7 and 34.1. Also, regarding the imposed C&C cost, we assert that it is minimal (about 0.25 Mbps) per bot in the worst case happening momentarily when the bot learns about the parameters of the attack.     

CloudBot: Advanced mobile botnets using ubiquitous cloud technologies

The mobile botnet is a collection of compromised mobile devices that can remotely receive commands from the botmaster. Exploiting unique features of mobile networks and smartphones, mobile botnets pose a severe threat to mobile users, because smartphones have become an indispensable part of our daily lives and carried a lot of private information. With the development of cloud computing technologies, botmaster can utilize ubiquitous cloud technologies to construct robust and scalable C&C (command and control) channel for mobile botnet. In this paper, we propose Cloudbot, a novel mobile botnet, which outperforms existing mobile botnets in terms of robustness, controllability, scalability, and stealthiness. Although the basic idea of using cloud technologies seems straightforward, we explore the design space of exploiting such services and tackle several challenging issues to overcome the limitations of existing mobile botnets. We have implemented CloudBot by exploiting popular push services and cloud storage services, and evaluated it through extensive experiments. The results demonstrate not only the feasibility of CloudBot but also its advantages, such as stealthiness, robustness, and performance.     

The SIC botnet lifecycle model: A step beyond traditional epidemiological models

Botnets, overlay networks built by cyber criminals from numerous compromised network-accessible devices, have become a pressing security concern in the Internet world. Availability of accurate mathematical models of population size evolution enables security experts to plan ahead and deploy adequate resources when responding to a growing threat of an emerging botnet. In this paper, we introduce the Susceptible-Infected-Connected (SIC) botnet model. Prior botnet models are largely the same as the models for the spread of malware among computers and disease among humans. The SIC model possesses some key improvements over earlier models: (1) keeping track of only key node stages (Infected and Connected), hence being applicable to a larger set of botnets; and (2) being a Continuous-Time Markov Chain-based model, it takes into account the stochastic nature of population size evolution. The SIC model helps the security experts with the following two key analyses: (1) estimation of the global botnet size during its initial appearance based on local measurements; and (2) comparison of botnet mitigation strategies such as disinfection of nodes and attacks on botnet’s Command and Control (C&C) structure. The analysis of the mitigation strategies has been strengthened by the development of an analytical link between the SIC model and the P2P botnet mitigation strategies. Specifically, one can analyze how a random sybil attack on a botnet can be fine-tuned based on the insight drawn from the use of the SIC model. We also show that derived results may be used to model the sudden growth and size fluctuations of real-world botnets.     

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.     

Dissecting SpyEye – Understanding the design of third generation botnets

Botnet malware is improving with the latest (3rd) generation exemplified by the SpyEye and Zeus botnets. These botnets are important to understand because they target online financial transactions, primarily with banks. In this paper, we analyze the components from multiple generations of the SpyEye botnet in order to understand both how it works and how it is evolving. SpyEye is a sophisticated piece of malware with a modular design that eases the incorporation of improvements. We will discuss in detail the complete framework of SpyEye botnet consisting of the Bot Development Kit (BDK), the plugin architecture, the backend storage server, the bot design and the web-based Command and Control (C&C) management system. In addition, we also examine the techniques used by SpyEye to steal money.     

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.     

Improving command and control speech recognition on mobile devices: using predictive user models for language modeling

Command and control (C&C) speech recognition allows users to interact with a system by speaking commands or asking questions restricted to a fixed grammar containing pre-defined phrases. Whereas C&C interaction has been commonplace in telephony and accessibility systems for many years, only recently have mobile devices had the memory and processing capacity to support client-side speech recognition. Given the personal nature of mobile devices, statistical models that can predict commands based in part on past user behavior hold promise for improving C&C recognition accuracy. For example, if a user calls a spouse at the end of every workday, the language model could be adapted to weight the spouse more than other contacts during that time. In this paper, we describe and assess statistical models learned from a large population of users for predicting the next user command of a commercial C&C application. We explain how these models were used for language modeling, and evaluate their performance in terms of task completion. The best performing model achieved a 26% relative reduction in error rate compared to the base system. Finally, we investigate the effects of personalization on performance at different learning rates via online updating of model parameters based on individual user data. Personalization significantly increased relative reduction in error rate by an additional 5%.     

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.     

A Learning Model to Detect Android C&C Applications Using Hybrid Analysis

Smartphone devices particularly Android devices are in use by billions of people everywhere in the world. Similarly, this increasing rate attracts mobile botnet attacks which is a network of interconnected nodes operated through the command and control (C&C) method to expand malicious activities. At present, mobile botnet attacks launched the Distributed denial of services (DDoS) that causes to steal of sensitive data, remote access, and spam generation, etc. Consequently, various approaches are defined in the literature to detect mobile botnet attacks using static or dynamic analysis. In this paper, a novel hybrid model, the combination of static and dynamic methods that relies on machine learning to detect android botnet applications is proposed. Furthermore, results are evaluated using machine learning classifiers. The Random Forest (RF) classifier outperform as compared to other ML techniques i.e., Naïve Bayes (NB), Support Vector Machine (SVM), and Simple Logistic (SL). Our proposed framework achieved 97.48% accuracy in the detection of botnet applications. Finally, some future research directions are highlighted regarding botnet attacks detection for the entire community.     

面向可扩展僵尸网络的安全控制方法

僵尸网络是互联网面临的主要威胁之一。当前,网络服务类型多样、安全漏洞频出、以物联网设备为代表的海量联网设备部署更加有利于僵尸网络全球扩展。未来僵尸网络将更加具有跨平台特性和隐匿性,这给网络空间带来了严重的安全隐患。因此,针对僵尸网络自身开展深入研究,可以为新的僵尸网络防御研究提供研究对象,对于设计下一代网络安全防护体系具有重要意义。提出一种基于HTTP的可扩展僵尸网络框架来解决僵尸网络自身存在的兼容性、隐匿性与安全性问题,该框架基于中心式控制模型, 采用HTTP 作为僵尸网络通信协议,并对通信内容进行基于对称密码学的块加密。进一步地,提出了一种面向多平台架构的僵尸网络安全控制方法,该方法利用源码级代码集成与交叉编译技术解决兼容性问题,引入动态密钥加密通信机制克服传统僵尸网络流量存在规律性和易被分析的不足,设计服务器迁移与重连机制解决中心式僵尸网络模型存在的单点失效问题,以提高僵尸网络存活率。3 个不同控制性水平场景下的仿真实验结果表明,僵尸网络的规模与其命令与控制（C＆amp;C,command and control）服务器服务负载之间存在线性关系;此外,在僵尸网络规模相同的条件下,越高的控制性会带来越高的吞吐量和越大的系统负载,从而验证了所提方法的有效性和现实可行性。     

基于聚类技术的僵尸网络检测方案

现有僵尸网络检测方案需要先验知识以获取匹配模式,无法满足实时处理的要求。为此,从分析僵尸网络的特点出发,通过比较僵尸频道消息字符串的相似度,提出基于聚类技术的僵尸网络检测方案。实验结果表明,该方案能有效检测隐藏在正常网络数据流中的僵尸频道。     

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。     

A Spatio-Temporal malware and country clustering algorithm: 2012 IIJ MITF case study

A huge number of botnet malware variants can be downloaded by zombie personal computers as secondary injections and upgrades according to their botmasters to perform different distributed/coordinated cyber attacks such as phishing, spam e-mail, malicious Web sites, ransomware, DDoS. In order to generate a faster response to new threats and better understanding of botnet activities, grouping them based on their malicious behaviors has become extremely important. This paper presents a Spatio-Temporal malware clustering algorithm based on its (weekly-hourly-country) features. The dataset contains more than 32 million of malware download logs from 100 honeypots set up by Malware Investigation Task Force (MITF) of Internet Initiative Japan Inc. (IIJ) from 2011 to 2012. The Top-20 malware clustering results coincidentally correspond to Conficker.B and Conficker.C with relatively high precision and recall rates up to 100.0, 88.9 % and 91.7, 100.0 %, respectively. On the other hand, the resulting two clusters of Top-20 countries are comparable to those with high and low growth rates recently reported in 2015 by Asghari et al. Therefore, our approach can be validated and evaluated to yield precision and recall of up to 75.0 and 86.7 %, respectively.     

Cluster Ensemble with Link-Based Approach for Botnet Detection

Botnet detection is one of the most imminent tasks for cyber security. Among popular botnet countermeasures, an intrusion detection system is the prominent mechanism. In the past, packet-based intrusion detection systems were popular. However, flow-based intrusion detection systems have been preferred in recent years due to their ability to adapt to modern high-speed networks. A collection of flows from an enterprise network usually contains both botnet traffic and normal traffic. To classify this traffic, supervised machine learning algorithms, i.e., classifications, have been applied and achieved a high accuracy. In an effort to improve the ability of intrusion detection systems against botnets, some studies have suggested partitioning flows into clusters before applying the classifications and this step could significantly reduce the complexity of a flow set. However, the instability of individual clustering algorithms is still a constraint for botnet detection.To overcome this bottleneck, we propose a novel method that combines individual partitions to become a strong learner through the use of a link-based algorithm. Our experiments show that our cluster ensemble model outperforms existing botnet detection mechanisms with a high reliability. We also determine the balance between accuracy and computer resources for botnet detection, and thereby propose a range for the maximum duration time of flows in botnet research.     

Scalable fine-grained behavioral clustering of HTTP-based malware

A large number of today’s botnets leverage the HTTP protocol to communicate with their botmasters or perpetrate malicious activities. In this paper, we present a new scalable system for network-level behavioral clustering of HTTP-based malware that aims to efficiently group newly collected malware samples into malware family clusters. The end goal is to obtain malware clusters that can aid the automatic generation of high quality network signatures, which can in turn be used to detect botnet command-and-control (C&C) and other malware-generated communications at the network perimeter.We achieve scalability in our clustering system by simplifying the multi-step clustering process proposed in [31], and by leveraging incremental clustering algorithms that run efficiently on very large datasets. At the same time, we show that scalability is achieved while retaining a good trade-off between detection rate and false positives for the signatures derived from the obtained malware clusters. We implemented a proof-of-concept version of our new scalable malware clustering system and performed experiments with about 65,000 distinct malware samples. Results from our evaluation confirm the effectiveness of the proposed system and show that, compared to [31], our approach can reduce processing times from several hours to a few minutes, and scales well to large datasets containing tens of thousands of distinct malware samples.     

Clustering botnet communication traffic based on n-gram feature selection

Recognized as one the most serious security threats on current Internet infrastructure, botnets can not only be implemented by existing well known applications, e.g. IRC, HTTP, or Peer-to-Peer, but also can be constructed by unknown or creative applications, which makes the botnet detection a challenging problem. Previous attempts for detecting botnets are mostly to examine traffic content for bot command on selected network links or by setting up honeypots. Traffic content, however, can be encrypted with the evolution of botnet, and as a result leading to a fail of content based detection approaches. In this paper, we address this issue and propose a new approach for detecting and clustering botnet traffic on large-scale network application communities, in which we first classify the network traffic into different applications by using traffic payload signatures, and then a novel decision tree model is used to classify those traffic to be unknown by the payload content (e.g. encrypted traffic) into known application communities where network traffic is clustered based on n-gram features selected and extracted from the content of network flows in order to differentiate the malicious botnet traffic created by bots from normal traffic generated by human beings on each specific application. We evaluate our approach with seven different traffic trace collected on three different network links and results show the proposed approach successfully detects two IRC botnet traffic traces with a high detection rate and an acceptable low false alarm rate.