No / In recent years, the Internet has enabled access to widespread remote services in the distributed computing environment; however, integrity of data transmission in the distributed computing platform is hindered by a number of security issues. For instance, the botnet phenomenon is a prominent threat to Internet security, including the threat of malicious codes. The botnet phenomenon supports a wide range of criminal activities, including distributed denial of service (DDoS) attacks, click fraud, phishing, malware distribution, spam emails, and building machines for illegitimate exchange of information/materials. Therefore, it is imperative to design and develop a robust mechanism for improving the botnet detection, analysis, and removal process. Currently, botnet detection techniques have been reviewed in different ways; however, such studies are limited in scope and lack discussions on the latest botnet detection techniques. This paper presents a comprehensive review of the latest state-of-the-art techniques for botnet detection and figures out the trends of previous and current research. It provides a thematic taxonomy for the classification of botnet detection techniques and highlights the implications and critical aspects by qualitatively analyzing such techniques. Related to our comprehensive review, we highlight future directions for improving the schemes that broadly span the entire botnet detection research field and identify the persistent and prominent research challenges that remain open. / University of Malaya, Malaysia (No. FP034-2012A)

Yes / Over the past decade, the digitization of services transformed the healthcare sector leading to a sharp rise in cybersecurity threats. Poor cybersecurity in the healthcare sector, coupled with high value of patient records attracted the attention of hackers. Sophisticated advanced persistent threats and malware have significantly contributed to increasing risks to the health sector. Many recent attacks are attributed to the spread of malicious software, e.g., ransomware or bot malware. Machines infected with bot malware can be used as tools for remote attack or even cryptomining. This paper presents a novel approach, called BotDet, for botnet Command and Control (C&C) traffic detection to defend against malware attacks in critical ultrastructure systems. There are two stages in the development of the proposed system: 1) we have developed four detection modules to detect different possible techniques used in botnet C&C communications and 2) we have designed a correlation framework to reduce the rate of false alarms raised by individual detection modules. Evaluation results show that BotDet balances the true positive rate and the false positive rate with 82.3% and 13.6%, respectively. Furthermore, it proves BotDet capability of real time detection.

Yes / The world has seen exponential growth in deploying Internet of Things (IoT) devices. In recent years, connected IoT devices have surpassed the number of connected non-IoT devices. The number of IoT devices continues to grow and they are becoming a critical component of the national infrastructure. IoT devices' characteristics and inherent limitations make them attractive targets for hackers and cyber criminals. Botnet attack is one of the serious threats on the Internet today. This article proposes pattern-based feature selection methods as part of a machine learning (ML) based botnet detection system. Specifically, two methods are proposed: the first is based on the most dominant pattern feature values and the second is based on Maximal Frequent Itemset (MFI) mining. The proposed feature selection method uses Gini Impurity (GI) and an unsupervised clustering method to select the most influential features automatically. The evaluation results show that the proposed methods have improved the performance of the detection system. The developed system has a True Positive Rate (TPR) of 100% and a False Positive Rate (FPR) of 0% for best performing models. In addition, the proposed methods reduce the computational cost of the system as evidenced by the detection speed of the system.

With the advent of ubiquitous computing and Internet of Things (IoT), potentially billions of devices will create a broad range of data services and applications, which will require the communication networks to efficiently manage the increasing complexity. Cognitive network has been envisioned as a new paradigm to address this challenge, which has the capability of reasoning, planning and learning by incorporating cutting edge technologies including knowledge representation, context awareness, network optimization and machine learning. Cognitive network spans over the entire communication system including the core network and wireless links across the entire protocol stack. Cognitive Radio Network (CRN) is a part of cognitive network over wireless links, which endeavors to better utilize the spectrum resources. Core network provides a reliable backend infrastructure to the entire communication system. However, the CR communication and core network infrastructure have attracted various security threats, which become increasingly severe in pace with the growing complexity and adversity of the modern Internet. The focus of this dissertation is to exploit the security vulnerabilities of the state-of-the-art cognitive communication systems, and to provide detection, mitigation and protection mechanisms to allow security enhanced cognitive communications including wireless communications in CRNs and wired communications in core networks. In order to provide secure and reliable communications in CRNs: emph{first}, we incorporate security mechanisms into fundamental CRN functions, such as secure spectrum sensing techniques that will ensure trustworthy reporting of spectrum reading. emph{Second}, as no security mechanism can completely prevent all potential threats from entering CRNs, we design a systematic passive monitoring framework, emph{SpecMonitor}, based on unsupervised machine learning methods to strategically monitor the network traffic and operations in order to detect abnormal and malicious behaviors. emph{Third}, highly capable cognitive radios allow more sophisticated reactive jamming attack, which imposes a serious threat to CR communications. By exploiting MIMO interference cancellation techniques, we propose jamming resilient CR communication mechanisms to survive in the presence of reactive jammers. Finally, we focus on protecting the core network from botnet threats by applying cognitive technologies to detect network-wide Peer-to-Peer (P2P) botnets, which leads to the design of a data-driven botnet detection system, called emph{PeerClean}. In all the four research thrusts, we present thorough security analysis, extensive simulations and testbed evaluations based on real-world implementations. Our results demonstrate that the proposed defense mechanisms can effectively and efficiently counteract sophisticated yet powerful attacks. / Ph. D.

The threat of data theft posed by self-propagating, remotely controlled bot malware is increasing. Cyber criminals are motivated to steal sensitive data, such as user names, passwords, account numbers, and credit card numbers, because these items can be parlayed into cash. For anonymity and economy of scale, bot networks have become the cyber criminal’s weapon of choice. In 2010 a single botnet included over one million compromised host computers, and one of the largest botnets in 2011 was specifically designed to harvest financial data from its victims. Unfortunately, current intrusion detection methods are unable to effectively detect data extraction techniques employed by bot malware. The research described in this Dissertation Report addresses that problem. This work builds on a foundation of research regarding artificial immune systems (AIS) and botnet activity detection. This work is the first to isolate and assess features derived from human computer interaction in the detection of data theft by bot malware and is the first to report on a novel use of the HTTP protocol by a contemporary variant of the Zeus bot.

The internet has matured into the focal point of our era. Its ecosystem is vast, complex, and in many regards unaccounted for. One of the most prevalent aspects of the internet is spam. Similar to the rest of the internet, spam has evolved from simply meaning ‘unwanted emails’ to a blanket term that encompasses any unsolicited or illegitimate content that appears in the wide range of media that exists on the internet. Many forms of spam permeate the internet, and spam architects continue to develop tools and methods to avoid detection. On the other side, cyber security engineers continue to develop more sophisticated detection tools to curb the harmful effects that come with spam. This virtual arms race has no end in sight. Most efforts thus far have been toward accurately detecting spam from ham, and rightfully so since initial detection is essential. However, research is lacking in understanding the current ecosystem of spam, spam campaigns, and the behavior of the botnets that drive the majority of spam traffic. This thesis focuses on characterizing spam, particularly the spam that appears in forums, where the spam is delivered by bots posing as legitimate users. Forum spam is used primarily to push advertisements or to boost other websites’ perceived popularity by including HTTP links in the content of the post. We conduct an experiment to collect a sample of the blog posts and network activity of the spambots that exist in the internet. We then present a corpora available to conduct analysis on and proceed with our own analysis. We cluster associated groups of users and IP addresses into entities, which we accept as a model of the underlying botnets that interact with our honeypots. We use Natural Language Processing (NLP) and Machine Learning (ML) to determine that creating semantic-based models of botnets are sufficient for distinguishing them from one another. We also find that the syntactic structure of posts has little variation from botnet to botnet. Finally we confirm that to a large degree botnet behavior and content hold across different domains.

The work analyze current detection methods of anomalies and security incidents in DNS traffic, and than design new obfuscation techniques which are capable of evading anomaly detection. Network attacks, exploiting the DNS protocol for tunneling of other network traffic, were selected for implementation part of the work. Control of botnet is considered as malicious application of tunneling through the DNS protocol. The main result of the work is to emphasize the necessity of discovering new detection principles of anomalies and security incidents in DNS traffic.

Cybersecurity has become an increasingly important concern for both military and civilian infrastructure globally. Because of the complexity that comes with wireless networks, adversaries have many means of infiltration and disruption of wireless networks. While there is much research done in defending these networks, understanding the robustness of these networks is tantamount for both designing new networks and examining possible security deficiencies in preexisting networks. This dissertation proposes to examine the robustness of wireless networks on three major fronts: the physical layer, the data-link layer, and the network layer. At the physical layer, denial-of-service jamming attacks are considered, and both additive interference and no interference are modeled in an optimal configuration and five common network topologies. At the data-link layer, data transmission efficacy and denial-of-sleep attacks are considered with the goal of maximizing throughput under a constrained lifetime. At the network layer, valid and anomalous communications are considered with the goal of classifying those anomalous communications apart from valid ones. This dissertation proposes that a thorough analysis of the aforementioned three layers provides valuable insights to robustness on general wireless networks.

The need for website administrators to efficiently and accurately detect the presence of web bots has shown to be a challenging problem. As the sophistication of modern web bots increases, specifically their ability to more closely mimic the behavior of humans, web bot detection schemes are more quickly becoming obsolete by failing to maintain effectiveness. Though machine learning-based detection schemes have been a successful approach to recent implementations, web bots are able to apply similar machine learning tactics to mimic human users, thus bypassing such detection schemes. This work seeks to address the issue of machine learning based bots bypassing machine learning-based detection schemes, by introducing a novel unsupervised learning approach to cluster users based on behavioral biometrics. The idea is that, by differentiating users based on their behavior, for example how they use the mouse or type on the keyboard, information can be provided for website administrators to make more informed decisions on declaring if a user is a human or a bot. This approach is similar to how modern websites require users to login before browsing their website; which in doing so, website administrators can make informed decisions on declaring if a user is a human or a bot. An added benefit of this approach is that it is a human observational proof (HOP); meaning that it will not inconvenience the user (user friction) with human interactive proofs (HIP) such as CAPTCHA, or with login requirements

La popularité des réseaux informatiques et d'Internet s'accompagne d'un essor des applications communicantes et de la multiplication des protocoles dont le fonctionnement est plus ou moins compliqué, ce qui implique également des performances différentes en termes de robustesse. Un premier objectif de cette thèse est d'approfondir plus en détails la robustesse de protocoles s'illustrant par d'extraordinaires performances empiriques tels que les botnets. Différents protocoles employés par les botnets sont donc modélisés dans cette thèse. Par ailleurs, l'essor et la diversité des protocoles s'accompagnent d'un manque de spécification volontaire ou non que la rétro-ingénierie tente de retrouver. Une première phase essentielle est notamment de découvrir les types de messages. La technique mise en \oe uvre dans cette étude s'appuie sur les machines à vecteurs de supports tout en ayant au préalable spécifié de nouvelles représentations des messages dont la complexité de calcul est très réduite par rapport aux autres techniques existantes. Enfin, il existe généralement un grand nombre d'applications distinctes pour un même protocole et identifier précisément le logiciel ou le type d'équipement utilisé (marque, version) est un atout essentiel dans plusieurs domaines tels que la supervision ou la sécurité des réseaux. S'appuyant uniquement sur les types de messages, le comportement d'un équipement, c'est-à-dire la manière dont il interagit avec les autres, est une information très avantageuse lorsqu'elle est couplée avec les délais entre les messages. Enfin, la grammaire d'un protocole connu permet de construire les arbres syntaxiques des messages, dont le contenu et la structure sémantiquement riche, avaient peu été étudiés jusqu'à maintenant dans le cadre de l'identification des équipements.