Towards secure message systems

Message systems, which transfer information from sender to recipient via communication networks, are indispensable to our modern society. The enormous user base of message systems and their critical role in information delivery make it the top priority to secure message systems. This dissertation focuses on securing the two most representative and dominant messages systems---e-mail and instant messaging (IM)---from two complementary aspects: defending against unwanted messages and ensuring reliable delivery of wanted messages.;To curtail unwanted messages and protect e-mail and instant messaging users, this dissertation proposes two mechanisms DBSpam and HoneyIM, which can effectively thwart e-mail spam laundering and foil malicious instant message spreading, respectively. DBSpam exploits the distinct characteristics of connection correlation and packet symmetry embedded in the behavior of spam laundering and utilizes a simple statistical method, Sequential Probability Ratio Test, to detect and break spam laundering activities inside a customer network in a timely manner. The experimental results demonstrate that DBSpam is effective in quickly and accurately capturing and suppressing e-mail spam laundering activities and is capable of coping with high speed network traffic. HoneyIM leverages the inherent characteristic of spreading of IM malware and applies the honey-pot technology to the detection of malicious instant messages. More specifically, HoneyIM uses decoy accounts in normal users\u27 contact lists as honey-pots to capture malicious messages sent by IM malware and suppresses the spread of malicious instant messages by performing network-wide blocking. The efficacy of HoneyIM has been validated through both simulations and real experiments.;To improve e-mail reliability, that is, prevent losses of wanted e-mail, this dissertation proposes a collaboration-based autonomous e-mail reputation system called CARE. CARE introduces inter-domain collaboration without central authority or third party and enables each e-mail service provider to independently build its reputation database, including frequently contacted and unacquainted sending domains, based on the local e-mail history and the information exchanged with other collaborating domains. The effectiveness of CARE on improving e-mail reliability has been validated through a number of experiments, including a comparison of two large e-mail log traces from two universities, a real experiment of DNS snooping on more than 36,000 domains, and extensive simulation experiments in a large-scale environment

DECEPTION BASED TECHNIQUES AGAINST RANSOMWARES: A SYSTEMATIC REVIEW

Ransomware is the most prevalent emerging business risk nowadays. It seriously affects business continuity and operations. According to Deloitte Cyber Security Landscape 2022, up to 4000 ransomware attacks occur daily, while the average number of days an organization takes to identify a breach is 191. Sophisticated cyber-attacks such as ransomware typically must go through multiple consecutive phases (initial foothold, network propagation, and action on objectives) before accomplishing its final objective. This study analyzed decoy-based solutions as an approach (detection, prevention, or mitigation) to overcome ransomware. A systematic literature review was conducted, in which the result has shown that deception-based techniques have given effective and significant performance against ransomware with minimal resources. It is also identified that contrary to general belief, deception techniques mainly involved in passive approaches (i.e., prevention, detection) possess other active capabilities such as ransomware traceback and obstruction (thwarting), file decryption, and decryption key recovery. Based on the literature review, several evaluation methods are also analyzed to measure the effectiveness of these deception-based techniques during the implementation process

A Cloud-based Intrusion Detection and Prevention System for Mobile Voting in South Africa

Publishe ThesisInformation and Communication Technology (ICT) has given rise to new technologies and solutions that were not possible a few years ago. One of these new technologies is electronic voting, also known as e-voting, which is the use of computerised equipment to cast a vote. One of the subsets of e-voting is mobile voting (m-voting). M-voting is the use of mobile phones to cast a vote outside the restricted electoral boundaries. Mobile phones are pervasive; they offer connection anywhere, at any time. However, utilising a fast-growing medium such as the mobile phone to cast a vote, poses various new security threats and challenges. Mobile phones utilise equivalent software design used by personal computers which makes them vulnerable or exposed to parallel security challenges like viruses, Trojans and worms. In the past, security solutions for mobile phones encountered several restrictions in practice. Several methods were used; however, these methods were developed to allow lightweight intrusion detection software to operate directly on the mobile phone. Nevertheless, such security solutions are bound to fail securing a device from intrusions as they are constrained by the restricted memory, storage, computational resources, and battery power of mobile phones. This study compared and evaluated two intrusion detection systems (IDSs), namely Snort and Suricata, in order to propose a cloud-based intrusion detection and prevention system (CIDPS) for m-voting in South Africa. It employed simulation as the primary research strategy to evaluate the IDSs. A quantitative research method was used to collect and analyse data. The researcher established that as much as Snort has been the preferred intrusion detection and prevention system (IDPS) in the past, Suricata presented more effective and accurate results close to what the researcher anticipated. The results also revealed that, though Suricata was proven effective enough to protect m-voting while saving the computational resources of mobile phones, more work needs to be done to alleviate the false-negative alerts caused by the anomaly detection method. This study adopted Suricata as a suitable cloud-based analysis engine to protect a mobile voting application like XaP

A reputation framework for behavioural history: developing and sharing reputations from behavioural history of network clients

The open architecture of the Internet has enabled its massive growth and success by facilitating easy connectivity between hosts. At the same time, the Internet has also opened itself up to abuse, e.g. arising out of unsolicited communication, both intentional and unintentional. It remains an open question as to how best servers should protect themselves from malicious clients whilst offering good service to innocent clients. There has been research on behavioural profiling and reputation of clients, mostly at the network level and also for email as an application, to detect malicious clients. However, this area continues to pose open research challenges. This thesis is motivated by the need for a generalised framework capable of aiding efficient detection of malicious clients while being able to reward clients with behaviour profiles conforming to the acceptable use and other relevant policies. The main contribution of this thesis is a novel, generalised, context-aware, policy independent, privacy preserving framework for developing and sharing client reputation based on behavioural history. The framework, augmenting existing protocols, allows fitting in of policies at various stages, thus keeping itself open and flexible to implementation. Locally recorded behavioural history of clients with known identities are translated to client reputations, which are then shared globally. The reputations enable privacy for clients by not exposing the details of their behaviour during interactions with the servers. The local and globally shared reputations facilitate servers in selecting service levels, including restricting access to malicious clients. We present results and analyses of simulations, with synthetic data and some proposed example policies, of client-server interactions and of attacks on our model. Suggestions presented for possible future extensions are drawn from our experiences with simulation

A Defense Framework Against Denial-of-Service in Computer Networks

Denial-of-Service (DoS) is a computer security problem that poses a serious challenge totrustworthiness of services deployed over computer networks. The aim of DoS attacks isto make services unavailable to legitimate users, and current network architectures alloweasy-to-launch, hard-to-stop DoS attacks. Particularly challenging are the service-level DoSattacks, whereby the victim service is flooded with legitimate-like requests, and the jammingattack, in which wireless communication is blocked by malicious radio interference. Theseattacks are overwhelming even for massively-resourced services, and effective and efficientdefenses are highly needed. This work contributes a novel defense framework, which I call dodging, against service-level DoS and wireless jamming. Dodging has two components: (1) the careful assignment ofservers to clients to achieve accurate and quick identification of service-level DoS attackersand (2) the continuous and unpredictable-to-attackers reconfiguration of the client-serverassignment and the radio-channel mapping to withstand service-level and jamming DoSattacks. Dodging creates hard-to-evade baits, or traps, and dilutes the attack "fire power".The traps identify the attackers when they violate the mapping function and even when theyattack while correctly following the mapping function. Moreover, dodging keeps attackers"in the dark", trying to follow the unpredictably changing mapping. They may hit a fewtimes but lose "precious" time before they are identified and stopped. Three dodging-based DoS defense algorithms are developed in this work. They are moreresource-efficient than state-of-the-art DoS detection and mitigation techniques. Honeybees combines channel hopping and error-correcting codes to achieve bandwidth-efficientand energy-efficient mitigation of jamming in multi-radio networks. In roaming honeypots, dodging enables the camouflaging of honeypots, or trap machines, as real servers,making it hard for attackers to locate and avoid the traps. Furthermore, shuffling requestsover servers opens up windows of opportunity, during which legitimate requests are serviced.Live baiting, efficiently identifies service-level DoS attackers by employing results fromthe group-testing theory, discovering defective members in a population using the minimumnumber of tests. The cost and benefit of the dodging algorithms are analyzed theoretically,in simulation, and using prototype experiments

Análise de malware com suporte de hardware

Orientadores: Paulo Lício de Geus, André Ricardo Abed GrégioDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: O mundo atual é impulsionado pelo uso de sistemas computacionais, estando estes pre- sentes em todos aspectos da vida cotidiana. Portanto, o correto funcionamento destes é essencial para se assegurar a manutenção das possibilidades trazidas pelos desenvolvi- mentos tecnológicos. Contudo, garantir o correto funcionamento destes não é uma tarefa fácil, dado que indivíduos mal-intencionados tentam constantemente subvertê-los visando benefíciar a si próprios ou a terceiros. Os tipos mais comuns de subversão são os ataques por códigos maliciosos (malware), capazes de dar a um atacante controle total sobre uma máquina. O combate à ameaça trazida por malware baseia-se na análise dos artefatos coletados de forma a permitir resposta aos incidentes ocorridos e o desenvolvimento de contramedidas futuras. No entanto, atacantes têm se especializado em burlar sistemas de análise e assim manter suas operações ativas. Para este propósito, faz-se uso de uma série de técnicas denominadas de "anti-análise", capazes de impedir a inspeção direta dos códigos maliciosos. Dentre essas técnicas, destaca-se a evasão do processo de análise, na qual são empregadas exemplares capazes de detectar a presença de um sistema de análise para então esconder seu comportamento malicioso. Exemplares evasivos têm sido cada vez mais utilizados em ataques e seu impacto sobre a segurança de sistemas é considerá- vel, dado que análises antes feitas de forma automática passaram a exigir a supervisão de analistas humanos em busca de sinais de evasão, aumentando assim o custo de se manter um sistema protegido. As formas mais comuns de detecção de um ambiente de análise se dão através da detecção de: (i) código injetado, usado pelo analista para inspecionar a aplicação; (ii) máquinas virtuais, usadas em ambientes de análise por questões de escala; (iii) efeitos colaterais de execução, geralmente causados por emuladores, também usados por analistas. Para lidar com malware evasivo, analistas tem se valido de técnicas ditas transparentes, isto é, que não requerem injeção de código nem causam efeitos colaterais de execução. Um modo de se obter transparência em um processo de análise é contar com suporte do hardware. Desta forma, este trabalho versa sobre a aplicação do suporte de hardware para fins de análise de ameaças evasivas. No decorrer deste texto, apresenta-se uma avaliação das tecnologias existentes de suporte de hardware, dentre as quais máqui- nas virtuais de hardware, suporte de BIOS e monitores de performance. A avaliação crítica de tais tecnologias oferece uma base de comparação entre diferentes casos de uso. Além disso, são enumeradas lacunas de desenvolvimento existentes atualmente. Mais que isso, uma destas lacunas é preenchida neste trabalho pela proposição da expansão do uso dos monitores de performance para fins de monitoração de malware. Mais especificamente, é proposto o uso do monitor BTS para fins de construção de um tracer e um debugger. O framework proposto e desenvolvido neste trabalho é capaz, ainda, de lidar com ataques do tipo ROP, um dos mais utilizados atualmente para exploração de vulnerabilidades. A avaliação da solução demonstra que não há a introdução de efeitos colaterais, o que per- mite análises de forma transparente. Beneficiando-se desta característica, demonstramos a análise de aplicações protegidas e a identificação de técnicas de evasãoAbstract: Today¿s world is driven by the usage of computer systems, which are present in all aspects of everyday life. Therefore, the correct working of these systems is essential to ensure the maintenance of the possibilities brought about by technological developments. However, ensuring the correct working of such systems is not an easy task, as many people attempt to subvert systems working for their own benefit. The most common kind of subversion against computer systems are malware attacks, which can make an attacker to gain com- plete machine control. The fight against this kind of threat is based on analysis procedures of the collected malicious artifacts, allowing the incident response and the development of future countermeasures. However, attackers have specialized in circumventing analysis systems and thus keeping their operations active. For this purpose, they employ a series of techniques called anti-analysis, able to prevent the inspection of their malicious codes. Among these techniques, I highlight the analysis procedure evasion, that is, the usage of samples able to detect the presence of an analysis solution and then hide their malicious behavior. Evasive examples have become popular, and their impact on systems security is considerable, since automatic analysis now requires human supervision in order to find evasion signs, which significantly raises the cost of maintaining a protected system. The most common ways for detecting an analysis environment are: i) Injected code detec- tion, since injection is used by analysts to inspect applications on their way; ii) Virtual machine detection, since they are used in analysis environments due to scalability issues; iii) Execution side effects detection, usually caused by emulators, also used by analysts. To handle evasive malware, analysts have relied on the so-called transparent techniques, that is, those which do not require code injection nor cause execution side effects. A way to achieve transparency in an analysis process is to rely on hardware support. In this way, this work covers the application of the hardware support for the evasive threats analysis purpose. In the course of this text, I present an assessment of existing hardware support technologies, including hardware virtual machines, BIOS support, performance monitors and PCI cards. My critical evaluation of such technologies provides basis for comparing different usage cases. In addition, I pinpoint development gaps that currently exists. More than that, I fill one of these gaps by proposing to expand the usage of performance monitors for malware monitoring purposes. More specifically, I propose the usage of the BTS monitor for the purpose of developing a tracer and a debugger. The proposed framework is also able of dealing with ROP attacks, one of the most common used technique for remote vulnerability exploitation. The framework evaluation shows no side-effect is introduced, thus allowing transparent analysis. Making use of this capability, I demonstrate how protected applications can be inspected and how evasion techniques can be identifiedMestradoCiência da ComputaçãoMestre em Ciência da ComputaçãoCAPE

A machine-learning-based system for real-time advanced persistent threat detection and prediction

It is widely cited that cyber attacks have become more prevalent on a global scale. In light of this, the cybercrime industry has been established for various purposes such as political, economic and socio-cultural aims. Such attacks can be used as a harmful weapon and cyberspace is often cited as a battlefield. One of the most serious types of cyber attacks is the Advanced Persistent Threat (APT), which is a new and more complex version of multi-step attack. The main aim of the APT attack is espionage and data exfiltration, which has the potential to cause significant damage and substantial financial loss. This research aims to develop a novel system to detect and predict APT attacks. A Machine-Learning-based APT detection system, called MLAPT, is proposed. MLAPT runs through three main phases: (1) Threat detection, in which eight methods are developed to detect different techniques used during the various APT steps. The implementation and validation of these methods with real traffic is a significant contribution to the current body of research; (2) Alert correlation, in which a correlation framework is designed to link the outputs of the detection methods, aiming to find alerts that could be related and belong to one APT scenario; and (3) Attack prediction, in which a machine-learning-based prediction module is proposed based on the correlation framework output, to be used by the network security team to determine the probability of the early alerts to develop a complete APT attack. The correlation framework and prediction module are two other major contributions in this work. MLAPT is experimentally evaluated and the presented system is able to predict APT in its early steps with a prediction accuracy of 84.8%

Discovering New Vulnerabilities in Computer Systems

Vulnerability research plays a key role in preventing and defending against malicious computer system exploitations. Driven by a multi-billion dollar underground economy, cyber criminals today tirelessly launch malicious exploitations, threatening every aspect of daily computing. to effectively protect computer systems from devastation, it is imperative to discover and mitigate vulnerabilities before they fall into the offensive parties\u27 hands. This dissertation is dedicated to the research and discovery of new design and deployment vulnerabilities in three very different types of computer systems.;The first vulnerability is found in the automatic malicious binary (malware) detection system. Binary analysis, a central piece of technology for malware detection, are divided into two classes, static analysis and dynamic analysis. State-of-the-art detection systems employ both classes of analyses to complement each other\u27s strengths and weaknesses for improved detection results. However, we found that the commonly seen design patterns may suffer from evasion attacks. We demonstrate attacks on the vulnerabilities by designing and implementing a novel binary obfuscation technique.;The second vulnerability is located in the design of server system power management. Technological advancements have improved server system power efficiency and facilitated energy proportional computing. However, the change of power profile makes the power consumption subjected to unaudited influences of remote parties, leaving the server systems vulnerable to energy-targeted malicious exploit. We demonstrate an energy abusing attack on a standalone open Web server, measure the extent of the damage, and present a preliminary defense strategy.;The third vulnerability is discovered in the application of server virtualization technologies. Server virtualization greatly benefits today\u27s data centers and brings pervasive cloud computing a step closer to the general public. However, the practice of physical co-hosting virtual machines with different security privileges risks introducing covert channels that seriously threaten the information security in the cloud. We study the construction of high-bandwidth covert channels via the memory sub-system, and show a practical exploit of cross-virtual-machine covert channels on virtualized x86 platforms

Advanced Threat Intelligence: Interpretation of Anomalous Behavior in Ubiquitous Kernel Processes

Targeted attacks on digital infrastructures are a rising threat against the confidentiality, integrity, and availability of both IT systems and sensitive data. With the emergence of advanced persistent threats (APTs), identifying and understanding such attacks has become an increasingly difficult task. Current signature-based systems are heavily reliant on fixed patterns that struggle with unknown or evasive applications, while behavior-based solutions usually leave most of the interpretative work to a human analyst. This thesis presents a multi-stage system able to detect and classify anomalous behavior within a user session by observing and analyzing ubiquitous kernel processes. Application candidates suitable for monitoring are initially selected through an adapted sentiment mining process using a score based on the log likelihood ratio (LLR). For transparent anomaly detection within a corpus of associated events, the author utilizes star structures, a bipartite representation designed to approximate the edit distance between graphs. Templates describing nominal behavior are generated automatically and are used for the computation of both an anomaly score and a report containing all deviating events. The extracted anomalies are classified using the Random Forest (RF) and Support Vector Machine (SVM) algorithms. Ultimately, the newly labeled patterns are mapped to a dedicated APT attacker–defender model that considers objectives, actions, actors, as well as assets, thereby bridging the gap between attack indicators and detailed threat semantics. This enables both risk assessment and decision support for mitigating targeted attacks. Results show that the prototype system is capable of identifying 99.8% of all star structure anomalies as benign or malicious. In multi-class scenarios that seek to associate each anomaly with a distinct attack pattern belonging to a particular APT stage we achieve a solid accuracy of 95.7%. Furthermore, we demonstrate that 88.3% of observed attacks could be identified by analyzing and classifying a single ubiquitous Windows process for a mere 10 seconds, thereby eliminating the necessity to monitor each and every (unknown) application running on a system. With its semantic take on threat detection and classification, the proposed system offers a formal as well as technical solution to an information security challenge of great significance.The financial support by the Christian Doppler Research Association, the Austrian Federal Ministry for Digital and Economic Affairs, and the National Foundation for Research, Technology and Development is gratefully acknowledged