Campus networks have recently experienced a proliferation of devices ranging from personal use devices (e.g. smartphones, laptops, tablets), to special-purpose network equipment (e.g. firewalls, network address translation boxes, network caches, load balancers, virtual private network servers, and authentication servers), as well as special-purpose systems (badge readers, IP phones, cameras, location trackers, etc.). To establish directives and regulations regarding the ways in which these heterogeneous systems are allowed to interact with each other and the network infrastructure, organizations typically appoint policy writing committees (PWCs) to create acceptable use policy (AUP) documents describing the rules and behavioral guidelines that all campus network interactions must abide by. While users are the audience for AUP documents produced by an organization's PWC, network administrators are the responsible party enforcing the contents of such policies using low-level CLI instructions and configuration files that are typically difficult to understand and are almost impossible to show that they do, in fact, enforce the AUPs. In other words, mapping the contents of imprecise unstructured sentences into technical configurations is a challenging task that relies on the interpretation and expertise of the network operator carrying out the policy enforcement. Moreover, there are multiple places where policy enforcement can take place. For example, policies governing servers (e.g., web, mail, and file servers) are often encoded into the server's configuration files. However, from a security perspective, conflating policy enforcement with server configuration is a dangerous practice because minor server misconfigurations could open up avenues for security exploits. On the other hand, policies that are enforced in the network tend to rarely change over time and are often based on one-size-fits-all policies that can severely limit the fast-paced dynamics of emerging research workflows found in campus networks. This dissertation addresses the above problems by leveraging recent advances in Software-Defined Networking (SDN) to support systems that enable novel in-network approaches developed to support an organization's network security policies. Namely, we introduce PoLanCO, a human-readable yet technically-precise policy language that serves as a middle-ground between the imprecise statements found in AUPs and the technical low-level mechanisms used to implement them. Real-world examples show that PoLanCO is capable of implementing a wide range of policies found in campus networks. In addition, we also present the concept of Network Security Caps, an enforcement layer that separates server/device functionality from policy enforcement. A Network Security Cap intercepts packets coming from, and going to, servers and ensures policy compliance before allowing network devices to process packets using the traditional forwarding mechanisms. Lastly, we propose the on-demand security exceptions model to cope with the dynamics of emerging research workflows that are not suited for a one-size-fits-all security approach. In the proposed model, network users and providers establish trust relationships that can be used to temporarily bypass the policy compliance checks applied to general-purpose traffic -- typically by network appliances that perform Deep Packet Inspection, thereby creating network bottlenecks. We describe the components of a prototype exception system as well as experiments showing that through short-lived exceptions researchers can realize significant improvements for their special-purpose traffic.

<div> <div> <div> <p>Nowadays, the pace of coordinated cyber security crimes has become drastically more rapid, and network attacks have become more advanced and diversified. The explosive growth of network security threats poses serious challenges for building secure Cyber-based Systems (CBS). Existing studies have addressed a breadth of challenges related to detecting network attacks. However, there is still a lack of studies on the detection of sophisticated Multi-stage Attacks (MSAs). </p> <p>The objective of this dissertation is to address the challenges of modeling and detecting sophisticated network attacks, such as multiple interleaved MSAs. We present the interleaving concept and investigate how interleaving multiple MSAs can deceive intrusion detection systems. Using one of the important statistical machine learning (ML) techniques, Hidden Markov Models (HMM), we develop three architectures that take into account the stealth nature of the interleaving attacks, and that can detect and track the progress of these attacks. These architectures deploy a set of HMM templates of known attacks and exhibit varying performance and complexity. </p> <p>For performance evaluation, various metrics are proposed which include (1) attack risk probability, (2) detection error rate, and (3) the number of correctly detected stages. Extensive simulation experiments are conducted to demonstrate the efficacy of the proposed architecture in the presence of multiple multi-stage attack scenarios, and in the presence of false alerts with various rates. </p> </div> </div> </div>

Les équipements intelligents, notamment les smartphones, sont la cible de nombreuses attaques de sécurité. Par ailleurs, la mise en œuvre de mécanismes de protection usuels est souvent inadaptée du fait de leurs ressources fortement contraintes. Dans ce contexte, nous proposons d'utiliser des chaînes de fonctions de sécurité qui sont composées de plusieurs services de sécurité, tels que des pare-feux ou des antivirus, automatiquement configurés et déployés dans le réseau. Cependant, ces chaînes sont connues pour être difficiles à valider. Cette difficulté est causée par la complexité de ces compositions qui impliquent des centaines, voire des milliers de règles de configuration. Dans cette thèse, nous proposons l'architecture d'un orchestrateur exploitant la programmabilité des réseaux pour automatiser la configuration et le déploiement de chaînes de fonctions de sécurité. Il est important que ces chaînes de sécurité soient correctes afin d’éviter l'introduction de failles de sécurité dans le réseau. Aussi, notre orchestrateur repose sur des méthodes automatiques de vérification et de synthèse, encore appelées méthodes formelles, pour assurer la correction des chaînes. Notre travail appréhende également l'optimisation du déploiement des chaînes dans le réseau, afin de préserver ses ressources et sa qualité de service. / Smart environments, in particular smartphones, are the target of multiple security attacks. Moreover, the deployment of traditional security mechanisms is often inadequate due to their highly constrained resources. In that context, we propose to use chains of security functions which are composed of several security services, such as firewalls or antivirus, automatically configured and deployed in the network. Chains of security functions are known as being error prone and hard to validate. This difficulty is caused by the complexity of these constructs that involve hundreds and even thousands of configuration rules. In this PhD thesis, we propose the architecture of an orchestrator, exploiting the programmability brought by software defined networking, for the automated configuration and deployment of chains of security functions. It is important to automatically insure that these security chains are correct, before their deployment in order to avoid the introduction of security breaches in the network. To do so, our orchestrator relies on methods of automated verification and synthesis, also known as formal methods, to ensure the correctness of the chains. Our work also consider the optimization of the deployment of chains of security functions in the network, in order to maintain its resources and quality of service.

Given the continuing advancement of networking applications and our increased dependence upon software-based systems, there is a pressing need to develop improved security techniques for defending modern information technology (IT) systems from malicious cyber-attacks. Indeed, anyone can be impacted by such activities, including individuals, corporations, and governments. Furthermore, the sustained expansion of the network user base and its associated set of applications is also introducing additional vulnerabilities which can lead to criminal breaches and loss of critical data. As a result, the broader cybersecurity problem area has emerged as a significant concern, with many solution strategies being proposed for both intrusion detection and prevention. Now in general, the cybersecurity dilemma can be treated as a conflict-resolution setup entailing a security system and minimum of two decision agents with competing goals (e.g., the attacker and the defender). Namely, on the one hand, the defender is focused on guaranteeing that the system operates at or above an adequate (specified) level. Conversely, the attacker is focused on trying to interrupt or corrupt the system’s operation. In light of the above, this dissertation introduces novel methodologies to build appropriate strategies for system administrators (defenders). In particular, detailed mathematical models of security systems are developed to analyze overall performance and predict the likely behavior of the key decision makers influencing the protection structure. The initial objective here is to create a reliable intrusion detection mechanism to help identify malicious attacks at a very early stage, i.e., in order to minimize potentially critical consequences and damage to system privacy and stability. Furthermore, another key objective is also to develop effective intrusion prevention (response) mechanisms. Along these lines, a machine learning based solution framework is developed consisting of two modules. Specifically, the first module prepares the system for analysis and detects whether or not there is a cyber-attack. Meanwhile, the second module analyzes the type of the breach and formulates an adequate response. Namely, a decision agent is used in the latter module to investigate the environment and make appropriate decisions in the case of uncertainty. This agent starts by conducting its analysis in a completely unknown milieu but continually learns to adjust its decision making based upon the provided feedback. The overall system is designed to operate in an automated manner without any intervention from administrators or other cybersecurity personnel. Human input is essentially only required to modify some key model (system) parameters and settings. Overall, the framework developed in this dissertation provides a solid foundation from which to develop improved threat detection and protection mechanisms for static setups, with further extensibility for handling streaming data.

<p>With an increasingly digitally connected society comes complexity, uncertainty, and risk. Network monitoring, incident management, and digital forensics is of increasing importance with the escalation of cybercrime and other network supported serious crimes. New laws and regulations governing electronic communications, cybercrime, and data retention are being proposed, continuously requiring new methods and tools.</p><p>This thesis introduces a novel approach to real-time network risk assessment based on hidden Markov models to represent the likelihood of transitions between security states. The method measures risk as a composition of individual hosts, providing a precise, fine-grained model for assessing risk and providing decision support for incident response. The approach has been integrated with an existing framework for distributed, large-scale intrusion detection, and the results of the risk assessment are applied to prioritize the alerts produced by the intrusion detection sensors. Using this implementation, the approach is evaluated on both simulated and real-world data.</p><p>Network monitoring can encompass large networks and process enormous amounts of data, and the practice and its ubiquity can represent a great threat to the privacy and confidentiality of network users. Existing measures for anonymization and pseudonymization are analyzed with respect to the trade-off of performing meaningful data analysis while protecting the identities of the users. The results demonstrate that most existing solutions for pseudonymization are vulnerable to a range of attacks. As a solution, some remedies for strengthening the schemes are proposed, and a method for unlinkable transaction pseudonyms is considered.</p><p>Finally, a novel method for performing digital forensic reconstructions in a virtual security testbed is proposed. Based on a hypothesis of the security incident in question, the testbed is configured with the appropriate operating systems, services, and exploits. Attacks are formulated as event chains and replayed on the testbed. The effects of each event are analyzed in order to support or refute the hypothesis. The purpose of the approach is to facilitate reconstruction experiments in digital forensics. Two examples are given to demonstrate the approach; one overview example based on the Trojan defense and one detailed example of a multi-step attack. Although a reconstruction can neither prove a hypothesis with absolute certainty, nor exclude the correctness of other hypotheses, a standardized environment combined with event reconstruction and testing can lend credibility to an investigation and can be a valuable asset in court.</p>

<p>Client-side computers connected to the Internet today are exposed to a lot malicious activity. Browsing the web can easily result in malware infection even if the user only visits well known and trusted sites. Attackers use website vulnerabilities and ad-networks to expose their malicious code to a large user base. The continuing trend of the attackers seems to be botnet construction that collects large amounts of data which could be a serious threat to company secrets and personal integrity. Meanwhile security researches are using a technology known as honeypots/honeyclients to find and analyze new malware. This thesis takes the concept of honeyclients and combines it with a proxy and database software to construct a new kind of real time defense mechanism usable in live environments. The concept is given the name Honeyscout and it analyzes any content before it reaches the user by using visited sites as a starting point for further crawling, blacklisting any malicious content found. A proof-of-concept honeyscout has been developed using the honeyclient Monkey-Spider by Ali Ikinci as a base. Results from the evaluation shows that the concept has potential as an effective and user-friendly defense technology. There are however large needs to further optimize and speed up the crawling process.</p>

Wireless network access protocols are used in numerous safety critical applications. Network availability is essential for safety critical applications,since loss of availability can cause personal or material damage. An adversary can disrupt the availability of a wireless network using denial of service (DoS) attacks. The most widely used wireless protocols are vulnerable to DoS attacks. Researchers have published DoS attacks against IEEE 802.11 local area networks (LANs), IEEE 802.16 wide area networks (WANs) and GSM andUMTS mobile networks. In this work, we analyze DoS vulnerabilities in wireless network protocols and define four categories of attacks:  jamming attacks, flooding attacks, semantic attacks and implementation specific attacks. We identify semantic attacks as the most severe threat to current andfuture wireless protocols, and as the category that has received the least attention by researchers. During the first phase of the research project we discover semantic DoS vulnerabilities in the IEEE 802.11 communication protocols through manual analysis. The 802.11 standard has been subject to manual analysis of DoS vulnerabilities for more than a decade, thus our results indicate that protocol vulnerabilities can elude manual analysis. We conclude that formal methods are required in order to improve protocol robustness against semantic DoS attacks.We propose a formal method that can be used to automatically discover protocol vulnerabilities. The formal method defines a protocol model, adversary model and cost model. The protocol participants and adversary are modeled as finite state transducers, while the cost is modeled as a function of time. Our primary goal is to construct a formal method that is practical, i.e. does not require a vast amount of resources to implement, and useful, i.e. able to discover protocol vulnerabilities. We verify and validate our proposed method by modeling the 802.11w amendment to the 802.11 standard using Promela as the modeling language. We then use the SPIN model checker to verify the model properties and experiments to validate the results. The modeling and experiments result in the discovery and experimental validation of four new deadlock vulnerabilities that had eluded manual analysis. We find one deadlock vulnerability in 802.11i and three deadlock vulnerabilitiesin 802.11w. A deadlock vulnerability is the most severe form of communication protocol DoS vulnerabilities, and their discovery and removal are an essential part of robust protocol design. Thus, we conclude that our proposed formal method is both practical and useful.

With an increasingly digitally connected society comes complexity, uncertainty, and risk. Network monitoring, incident management, and digital forensics is of increasing importance with the escalation of cybercrime and other network supported serious crimes. New laws and regulations governing electronic communications, cybercrime, and data retention are being proposed, continuously requiring new methods and tools. This thesis introduces a novel approach to real-time network risk assessment based on hidden Markov models to represent the likelihood of transitions between security states. The method measures risk as a composition of individual hosts, providing a precise, fine-grained model for assessing risk and providing decision support for incident response. The approach has been integrated with an existing framework for distributed, large-scale intrusion detection, and the results of the risk assessment are applied to prioritize the alerts produced by the intrusion detection sensors. Using this implementation, the approach is evaluated on both simulated and real-world data. Network monitoring can encompass large networks and process enormous amounts of data, and the practice and its ubiquity can represent a great threat to the privacy and confidentiality of network users. Existing measures for anonymization and pseudonymization are analyzed with respect to the trade-off of performing meaningful data analysis while protecting the identities of the users. The results demonstrate that most existing solutions for pseudonymization are vulnerable to a range of attacks. As a solution, some remedies for strengthening the schemes are proposed, and a method for unlinkable transaction pseudonyms is considered. Finally, a novel method for performing digital forensic reconstructions in a virtual security testbed is proposed. Based on a hypothesis of the security incident in question, the testbed is configured with the appropriate operating systems, services, and exploits. Attacks are formulated as event chains and replayed on the testbed. The effects of each event are analyzed in order to support or refute the hypothesis. The purpose of the approach is to facilitate reconstruction experiments in digital forensics. Two examples are given to demonstrate the approach; one overview example based on the Trojan defense and one detailed example of a multi-step attack. Although a reconstruction can neither prove a hypothesis with absolute certainty, nor exclude the correctness of other hypotheses, a standardized environment combined with event reconstruction and testing can lend credibility to an investigation and can be a valuable asset in court.

Covert communication refers to the process of communicating data through a channel that is neither designed, nor intended to transfer information. Traditionally, covert channels are considered as security threats in computer systems and a great deal of attention has been given to countermeasures for covert communication schemes. The evolution of computer networks led the communication community to revisit the concept of covert communication not only as a security threat but also as an alternative way of providing security and privacy to communication networks. In fact, the heterogeneous structure of computer networks and the diversity of communication protocols provide an appealing setting for covert channels. This dissertation is an exploration on a novel design methodology for undetectable and robust covert channels in communication networks. Our new design methodology is based on the concept of behavioral mimicry in computer systems. The objective is to design a covert transmitter that has enough degrees of freedom to behave like an ordinary transmitter and react normally to unpredictable network events, yet it has the ability to modulate a covert message over its behavioral fingerprints in the network. To this end, we argue that the inherent randomness in communication protocols and network environments is the key in finding the proper medium for network covert channels. We present a few examples on how random behaviors in communication protocols lead to discovery of suitable shared resources for covert channels. The proposed design methodology is tested on two new covert communication schemes, one is designed for wireless networks and the other one is optimized for public communication networks (e.g., Internet). Each design is accompanied by a comprehensive analysis from undetectability, achievable covert rate and reliability perspectives. In particular, we introduced turbo covert channels, a family of extremely robust model-based timing covert channels that achieve provable polynomial undetectability in public communication networks. This means that the covert channel is undetectable against any polynomial-time statistical test that analyzes samples of the covert traffic and the legitimate traffic of the network. Target applications for the proposed covert communication schemes are discussed including detailed practical scenarios in which the proposed channels can be implemented.

Social networking extending the social circle of people has already become an important integral part of our daily lives. As reported by ComScore, social networking sites such as Facebook and Twitter have reached 82 percent of the world's online population, representing 1.2 billion users around the world. In the meantime, fueled by the dramatic advancements of smartphones and the ubiquitous connections of Bluetooth/WiFi/3G/LTE networks, social networking further becomes available for mobile users and keeps them posted on the up-to-date worldwide news and messages from their friends and families anytime anywhere. The convergence of social networking, advanced smartphones, and stable network infrastructures brings us a pervasive and omnipotent communication platform, named mobile social network (MSN), helping us stay connected better than ever. In the MSN, multiple communication techniques help users to launch a variety of applications in multiple communication domains including single-user domain, two-user domain, user-chain domain, and user-star domain. Within different communication domains, promising mobile applications are fostered. For example, nearby friend search application can be launched in the two-user or user-chain domains to help a user find other physically-close peers who have similar interests and preferences; local service providers disseminate advertising information to nearby users in the user-star domain; and health monitoring enables users to check the physiological signals in the single-user domain. Despite the tremendous benefits brought by the MSN, it still faces many technique challenges among of which security and privacy protections are the most important ones as smartphones are vulnerable to security attacks, users easily neglect their privacy preservation, and mutual trust relationships are difficult to be established in the MSN. In this thesis, we explore the unique characteristics and study typical research issues of the MSN. We conduct our research with a focus on security and privacy preservation while considering human factors. Specifically, we consider the profile matching application in the two-user domain, the cooperative data forwarding in the user-chain domain, the trustworthy service evaluation application in the user-star domain, and the healthcare monitoring application in the single-user domain. The main contributions are, i) considering the human comparison behavior and privacy requirements, we first propose a novel family of comparison-based privacy-preserving profile matching (PPM) protocols. The proposed protocols enable two users to obtain comparison results of attribute values in their profiles, while the attribute values are not disclosed. Taking user anonymity requirement as an evaluation metric, we analyze the anonymity protection of the proposed protocols. From the analysis, we found that the more comparison results are disclosed, the less anonymity protection is achieved by the protocol. Further, we explore the pseudonym strategy and an anonymity enhancing technique where users could be self-aware of the anonymity risk level and take appropriate actions when needed; ii) considering the inherent MSN nature --- opportunistic networking, we propose a cooperative privacy-preserving data forwarding (PDF) protocol to help users forward data to other users. We indicate that privacy and effective data forwarding are two conflicting goals: the cooperative data forwarding could be severely interrupted or even disabled when the privacy preservation of users is applied, because without sharing personal information users become unrecognizable to each other and the social interactions are no longer traceable. We explore the morality model of users from classic social theory, and use game-theoretic approach to obtain the optimal data forwarding strategy. Through simulation results, we show that the proposed cooperative data strategy can achieve both the privacy preservation and the forwarding efficiency; iii) to establish the trust relationship in a distributed MSN is a challenging task. We propose a trustworthy service evaluation (TSE) system, to help users exchange their service reviews toward local vendors. However, vendors and users could be the potential attackers aiming to disrupt the TSE system. We then consider the review attacks, i.e., vendors rejecting and modifying the authentic reviews of users, and the Sybil attacks, i.e., users abusing their pseudonyms to generate fake reviews. To prevent these attacks, we explore the token technique, the aggregate signature, and the secret sharing techniques. Simulation results show the security and the effectiveness of the TSE system can be guaranteed; iv) to improve the efficiency and reliability of communications in the single-user domain, we propose a prediction-based secure and reliable routing framework (PSR). It can be integrated with any specific routing protocol to improve the latter's reliability and prevent data injection attacks during data communication. We show that the regularity of body gesture can be learned and applied by body sensors such that the route with the highest predicted link quality can always be chose for data forwarding. The security analysis and simulation results show that the PSR significantly increases routing efficiency and reliability with or without the data injection attacks.