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

Decision Support Elements and Enabling Techniques to Achieve a Cyber Defence Situational Awareness Capability

[ES] La presente tesis doctoral realiza un análisis en detalle de los elementos de decisión necesarios para mejorar la comprensión de la situación en ciberdefensa con especial énfasis en la percepción y comprensión del analista de un centro de operaciones de ciberseguridad (SOC). Se proponen dos arquitecturas diferentes basadas en el análisis forense de flujos de datos (NF3). La primera arquitectura emplea técnicas de Ensemble Machine Learning mientras que la segunda es una variante de Machine Learning de mayor complejidad algorítmica (lambda-NF3) que ofrece un marco de defensa de mayor robustez frente a ataques adversarios. Ambas propuestas buscan automatizar de forma efectiva la detección de malware y su posterior gestión de incidentes mostrando unos resultados satisfactorios en aproximar lo que se ha denominado un SOC de próxima generación y de computación cognitiva (NGC2SOC). La supervisión y monitorización de eventos para la protección de las redes informáticas de una organización debe ir acompañada de técnicas de visualización. En este caso, la tesis aborda la generación de representaciones tridimensionales basadas en métricas orientadas a la misión y procedimientos que usan un sistema experto basado en lógica difusa. Precisamente, el estado del arte muestra serias deficiencias a la hora de implementar soluciones de ciberdefensa que reflejen la relevancia de la misión, los recursos y cometidos de una organización para una decisión mejor informada. El trabajo de investigación proporciona finalmente dos áreas claves para mejorar la toma de decisiones en ciberdefensa: un marco sólido y completo de verificación y validación para evaluar parámetros de soluciones y la elaboración de un conjunto de datos sintéticos que referencian unívocamente las fases de un ciberataque con los estándares Cyber Kill Chain y MITRE ATT & CK.[CA] La present tesi doctoral realitza una anàlisi detalladament dels elements de decisió necessaris per a millorar la comprensió de la situació en ciberdefensa amb especial èmfasi en la percepció i comprensió de l'analista d'un centre d'operacions de ciberseguretat (SOC). Es proposen dues arquitectures diferents basades en l'anàlisi forense de fluxos de dades (NF3). La primera arquitectura empra tècniques de Ensemble Machine Learning mentre que la segona és una variant de Machine Learning de major complexitat algorítmica (lambda-NF3) que ofereix un marc de defensa de major robustesa enfront d'atacs adversaris. Totes dues propostes busquen automatitzar de manera efectiva la detecció de malware i la seua posterior gestió d'incidents mostrant uns resultats satisfactoris a aproximar el que s'ha denominat un SOC de pròxima generació i de computació cognitiva (NGC2SOC). La supervisió i monitoratge d'esdeveniments per a la protecció de les xarxes informàtiques d'una organització ha d'anar acompanyada de tècniques de visualització. En aquest cas, la tesi aborda la generació de representacions tridimensionals basades en mètriques orientades a la missió i procediments que usen un sistema expert basat en lògica difusa. Precisament, l'estat de l'art mostra serioses deficiències a l'hora d'implementar solucions de ciberdefensa que reflectisquen la rellevància de la missió, els recursos i comeses d'una organització per a una decisió més ben informada. El treball de recerca proporciona finalment dues àrees claus per a millorar la presa de decisions en ciberdefensa: un marc sòlid i complet de verificació i validació per a avaluar paràmetres de solucions i l'elaboració d'un conjunt de dades sintètiques que referencien unívocament les fases d'un ciberatac amb els estàndards Cyber Kill Chain i MITRE ATT & CK.[EN] This doctoral thesis performs a detailed analysis of the decision elements necessary to improve the cyber defence situation awareness with a special emphasis on the perception and understanding of the analyst of a cybersecurity operations center (SOC). Two different architectures based on the network flow forensics of data streams (NF3) are proposed. The first architecture uses Ensemble Machine Learning techniques while the second is a variant of Machine Learning with greater algorithmic complexity (lambda-NF3) that offers a more robust defense framework against adversarial attacks. Both proposals seek to effectively automate the detection of malware and its subsequent incident management, showing satisfactory results in approximating what has been called a next generation cognitive computing SOC (NGC2SOC). The supervision and monitoring of events for the protection of an organisation's computer networks must be accompanied by visualisation techniques. In this case, the thesis addresses the representation of three-dimensional pictures based on mission oriented metrics and procedures that use an expert system based on fuzzy logic. Precisely, the state-of-the-art evidences serious deficiencies when it comes to implementing cyber defence solutions that consider the relevance of the mission, resources and tasks of an organisation for a better-informed decision. The research work finally provides two key areas to improve decision-making in cyber defence: a solid and complete verification and validation framework to evaluate solution parameters and the development of a synthetic dataset that univocally references the phases of a cyber-attack with the Cyber Kill Chain and MITRE ATT & CK standards.Llopis Sánchez, S. (2023). Decision Support Elements and Enabling Techniques to Achieve a Cyber Defence Situational Awareness Capability [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/19424

A Machine Learning based Empirical Evaluation of Cyber Threat Actors High Level Attack Patterns over Low level Attack Patterns in Attributing Attacks

Cyber threat attribution is the process of identifying the actor of an attack incident in cyberspace. An accurate and timely threat attribution plays an important role in deterring future attacks by applying appropriate and timely defense mechanisms. Manual analysis of attack patterns gathered by honeypot deployments, intrusion detection systems, firewalls, and via trace-back procedures is still the preferred method of security analysts for cyber threat attribution. Such attack patterns are low-level Indicators of Compromise (IOC). They represent Tactics, Techniques, Procedures (TTP), and software tools used by the adversaries in their campaigns. The adversaries rarely re-use them. They can also be manipulated, resulting in false and unfair attribution. To empirically evaluate and compare the effectiveness of both kinds of IOC, there are two problems that need to be addressed. The first problem is that in recent research works, the ineffectiveness of low-level IOC for cyber threat attribution has been discussed intuitively. An empirical evaluation for the measure of the effectiveness of low-level IOC based on a real-world dataset is missing. The second problem is that the available dataset for high-level IOC has a single instance for each predictive class label that cannot be used directly for training machine learning models. To address these problems in this research work, we empirically evaluate the effectiveness of low-level IOC based on a real-world dataset that is specifically built for comparative analysis with high-level IOC. The experimental results show that the high-level IOC trained models effectively attribute cyberattacks with an accuracy of 95% as compared to the low-level IOC trained models where accuracy is 40%.Comment: 20 page

A Review of Digital Twins and their Application in Cybersecurity based on Artificial Intelligence

The potential of digital twin technology is yet to be fully realized due to its diversity and untapped potential. Digital twins enable systems' analysis, design, optimization, and evolution to be performed digitally or in conjunction with a cyber-physical approach to improve speed, accuracy, and efficiency over traditional engineering methods. Industry 4.0, factories of the future, and digital twins continue to benefit from the technology and provide enhanced efficiency within existing systems. Due to the lack of information and security standards associated with the transition to cyber digitization, cybercriminals have been able to take advantage of the situation. Access to a digital twin of a product or service is equivalent to threatening the entire collection. There is a robust interaction between digital twins and artificial intelligence tools, which leads to strong interaction between these technologies, so it can be used to improve the cybersecurity of these digital platforms based on their integration with these technologies. This study aims to investigate the role of artificial intelligence in providing cybersecurity for digital twin versions of various industries, as well as the risks associated with these versions. In addition, this research serves as a road map for researchers and others interested in cybersecurity and digital security.Comment: 60 pages, 8 Figures, 15 Table

2011 Annual Report of the Graduate School of Engineering and Management, Air Force Institute of Technology

The Graduate School\u27s Annual Report highlights research focus areas, new academic programs, faculty accomplishments and news, and provides top-level sponsor-funded research data and information

Cyber Law and Espionage Law as Communicating Vessels

Professor Lubin\u27s contribution is Cyber Law and Espionage Law as Communicating Vessels, pp. 203-225. Existing legal literature would have us assume that espionage operations and “below-the-threshold” cyber operations are doctrinally distinct. Whereas one is subject to the scant, amorphous, and under-developed legal framework of espionage law, the other is subject to an emerging, ever-evolving body of legal rules, known cumulatively as cyber law. This dichotomy, however, is erroneous and misleading. In practice, espionage and cyber law function as communicating vessels, and so are better conceived as two elements of a complex system, Information Warfare (IW). This paper therefore first draws attention to the similarities between the practices – the fact that the actors, technologies, and targets are interchangeable, as are the knee-jerk legal reactions of the international community. In light of the convergence between peacetime Low-Intensity Cyber Operations (LICOs) and peacetime Espionage Operations (EOs) the two should be subjected to a single regulatory framework, one which recognizes the role intelligence plays in our public world order and which adopts a contextual and consequential method of inquiry. The paper proceeds in the following order: Part 2 provides a descriptive account of the unique symbiotic relationship between espionage and cyber law, and further explains the reasons for this dynamic. Part 3 places the discussion surrounding this relationship within the broader discourse on IW, making the claim that the convergence between EOs and LICOs, as described in Part 2, could further be explained by an even larger convergence across all the various elements of the informational environment. Parts 2 and 3 then serve as the backdrop for Part 4, which details the attempt of the drafters of the Tallinn Manual 2.0 to compartmentalize espionage law and cyber law, and the deficits of their approach. The paper concludes by proposing an alternative holistic understanding of espionage law, grounded in general principles of law, which is more practically transferable to the cyber realmhttps://www.repository.law.indiana.edu/facbooks/1220/thumbnail.jp

Next-Generation Industrial Control System (ICS) Security:Towards ICS Honeypots for Defence-in-Depth Security

The advent of Industry 4.0 and smart manufacturing has led to an increased convergence of traditional manufacturing and production technologies with IP communications. Legacy Industrial Control System (ICS) devices are now exposed to a wide range of previously unconsidered threats, which must be considered to ensure the safe operation of industrial processes. Especially as cyberspace is presenting itself as a popular domain for nation-state operations, including against critical infrastructure. Honeypots are a well-known concept within traditional IT security, and they can enable a more proactive approach to security, unlike traditional systems. More work needs to be done to understand their usefulness within OT and critical infrastructure. This thesis advances beyond current honeypot implementations and furthers the current state-of-the-art by delivering novel ways of deploying ICS honeypots and delivering concrete answers to key research questions within the area. This is done by answering the question previously raised from a multitude of perspectives. We discuss relevant legislation, such as the UK Cyber Assessment Framework, the US NIST Framework for Improving Critical Infrastructure Cybersecurity, and associated industry-based standards and guidelines supporting operator compliance. Standards and guidance are used to frame a discussion on our survey of existing ICS honeypot implementations in the literature and their role in supporting regulatory objectives. However, these deployments are not always correctly configured and might differ from a real ICS. Based on these insights, we propose a novel framework towards the classification and implementation of ICS honeypots. This is underpinned by a study into the passive identification of ICS honeypots using Internet scanner data to identify honeypot characteristics. We also present how honeypots can be leveraged to identify when bespoke ICS vulnerabilities are exploited within the organisational network—further strengthening the case for honeypot usage within critical infrastructure environments. Additionally, we demonstrate a fundamentally different approach to the deployment of honeypots. By deploying it as a deterrent, to reduce the likelihood that an adversary interacts with a real system. This is important as skilled attackers are now adept at fingerprinting and avoiding honeypots. The results presented in this thesis demonstrate that honeypots can provide several benefits to the cyber security of and alignment to regulations within the critical infrastructure environment

Mecanismos dinâmicos de segurança para redes softwarizadas e virtualizadas

The relationship between attackers and defenders has traditionally been asymmetric, with attackers having time as an upper hand to devise an exploit that compromises the defender. The push towards the Cloudification of the world makes matters more challenging, as it lowers the cost of an attack, with a de facto standardization on a set of protocols. The discovery of a vulnerability now has a broader impact on various verticals (business use cases), while previously, some were in a segregated protocol stack requiring independent vulnerability research. Furthermore, defining a perimeter within a cloudified system is non-trivial, whereas before, the dedicated equipment already created a perimeter. This proposal takes the newer technologies of network softwarization and virtualization, both Cloud-enablers, to create new dynamic security mechanisms that address this asymmetric relationship using novel Moving Target Defense (MTD) approaches. The effective use of the exploration space, combined with the reconfiguration capabilities of frameworks like Network Function Virtualization (NFV) and Management and Orchestration (MANO), should allow for adjusting defense levels dynamically to achieve the required security as defined by the currently acceptable risk. The optimization tasks and integration tasks of this thesis explore these concepts. Furthermore, the proposed novel mechanisms were evaluated in real-world use cases, such as 5G networks or other Network Slicing enabled infrastructures.A relação entre atacantes e defensores tem sido tradicionalmente assimétrica, com os atacantes a terem o tempo como vantagem para conceberem uma exploração que comprometa o defensor. O impulso para a Cloudificação do mundo torna a situação mais desafiante, pois reduz o custo de um ataque, com uma padronização de facto sobre um conjunto de protocolos. A descoberta de uma vulnerabilidade tem agora um impacto mais amplo em várias verticais (casos de uso empresarial), enquanto anteriormente, alguns estavam numa pilha de protocolos segregados que exigiam uma investigação independente das suas vulnerabilidades. Além disso, a definição de um perímetro dentro de um sistema Cloud não é trivial, enquanto antes, o equipamento dedicado já criava um perímetro. Esta proposta toma as mais recentes tecnologias de softwarização e virtualização da rede, ambas facilitadoras da Cloud, para criar novos mecanismos dinâmicos de segurança que incidem sobre esta relação assimétrica utilizando novas abordagens de Moving Target Defense (MTD). A utilização eficaz do espaço de exploração, combinada com as capacidades de reconfiguração de frameworks como Network Function Virtualization (NFV) e Management and Orchestration (MANO), deverá permitir ajustar dinamicamente os níveis de defesa para alcançar a segurança necessária, tal como definida pelo risco actualmente aceitável. As tarefas de optimização e de integração desta tese exploram estes conceitos. Além disso, os novos mecanismos propostos foram avaliados em casos de utilização no mundo real, tais como redes 5G ou outras infraestruturas de Network Slicing.Programa Doutoral em Engenharia Informátic

Mal-Who? Mal-What? Mal-Where? The Future Cyber-Threat Of A Non-Fiction Neuromancer: Legally Un-Attributable, Cyberspace-Bound, Decentralized Autonomous Entities

For decades, science fiction writers have tackled philosophical and existential questions arising from the creation of artificial intelligence (“AI”) by human beings. AI, however, is no longer a fictional concept, but rather an evolving part of modern society. How will AI systems impact United States’ national security interests? Considering the increased national security threat coming from actors in cyberspace, policymakers should consider the cybersecurity risk of AI systems that operate entirely in cyberspace. This article opines that a serious threat to national security will arise from a cyberspace-bound, decentralized autonomous entity (“CyDAE”) because of the “unexplainability” of current AI system design (that is, the difficulty understanding why or how the AI arrived at its conclusion or behaved the way it did), the lack of legal personhood arrangements for autonomous systems, and the already difficult task of attributing acts in cyberspace to human actors or States because of outdated Westphalian notions of sovereignty and territoriality. The article ultimately offers several broad policy suggestions, including: (1) an AI registry; (2) “explainability” criteria for AI system designs; (3) requiring human oversight for legal personhood arrangements (whether arranged in a corporation, limited liability structure, or otherwise) tailored specifically for AI autonomous systems that lack human members; and (4) universal jurisdiction of States over malicious CyDAEs that obfuscate attributive links to human actors or States

Enabling an Anatomic View to Investigate Honeypot Systems: A Survey

A honeypot is a type of security facility deliberately created to be probed, attacked, and compromised. It is often used for protecting production systems by detecting and deflecting unauthorized accesses. It is also useful for investigating the behavior of attackers, and in particular, unknown attacks. For the past 17 years plenty of effort has been invested in the research and development of honeypot techniques, and they have evolved to be an increasingly powerful means of defending against the creations of the blackhat community. In this paper, by studying a wide set of honeypots, the two essential elements of honeypots—the decoy and the captor—are captured and presented, together with two abstract organizational forms—independent and cooperative—where these two elements can be integrated. A novel decoy and captor (D-C) based taxonomy is proposed for the purpose of studying and classifying the various honeypot techniques. An extensive set of independent and cooperative honeypot projects and research that cover these techniques is surveyed under the taxonomy framework. Furthermore, two subsets of features from the taxonomy are identified, which can greatly influence the honeypot performances. These two subsets of features are applied to a number of typical independent and cooperative honeypots separately in order to validate the taxonomy and predict the honeypot development trends