Game theory with learning for cyber security monitoring

Carefully crafted computer worms such as Stuxnet and recent data breaches on retail organizations (e.g., Target, Home Depot) are very sophisticated security attacks on critical cyber infrastructures. Such attacks are referred to as advanced persistent threat (APT), and are on constant rise with severe implications. In all these attacks, the presence of an attacker itself is difficult to detect as they log-in as legitimate users. Hence, these attacks comprising multiple actions are challenging to differentiate from benign and therefore common detection techniques have to deal with high false positive rates. While machine learning and game theoretic models have been applied for intrusion detection, machine learning techniques lack the ability to model the rationality of the players, while the game theoretic approaches rely on the strict assumption of full rationality and complete information. This thesis discusses an approach that proposes Q-Learning to model the decision process of a security administrator which addresses the joint limitations of using game theory and machine learning techniques for this problem. This work compares variations of Q-Learning with a traditional stochastic game model by performing a simulation under different pair of profiles for attackers and defenders using parameters derived from real incident data of a large computer organization. Analysis on the strengths and weaknesses of the algorithms, and how the parameters in the algorithms affect the performance are studied. Simulation results show that Naive Q-Learning, despite the restricted information on the opponent, better reduces the impact of an attacker compared to Minmax Q-Learning against all attackers, or Stochastic Games players against less rational opponents

A Comprehensive Insight into Game Theory in relevance to Cyber Security

The progressively ubiquitous connectivity in the present information systems pose newer challenges tosecurity. The conventional security mechanisms have come a long way in securing the well-definedobjectives of confidentiality, integrity, authenticity and availability. Nevertheless, with the growth in thesystem complexities and attack sophistication, providing security via traditional means can beunaffordable. A novel theoretical perspective and an innovative approach are thus required forunderstanding security from decision-making and strategic viewpoint. One of the analytical tools whichmay assist the researchers in designing security protocols for computer networks is game theory. Thegame-theoretic concept finds extensive applications in security at different levels, including thecyberspace and is generally categorized under security games. It can be utilized as a robust mathematicaltool for modelling and analyzing contemporary security issues. Game theory offers a natural frameworkfor capturing the defensive as well as adversarial interactions between the defenders and the attackers.Furthermore, defenders can attain a deep understanding of the potential attack threats and the strategiesof attackers by equilibrium evaluation of the security games. In this paper, the concept of game theoryhas been presented, followed by game-theoretic applications in cybersecurity including cryptography.Different types of games, particularly those focused on securing the cyberspace, have been analysed andvaried game-theoretic methodologies including mechanism design theories have been outlined foroffering a modern foundation of the science of cybersecurity

Knowledge-based Decision Making for Simulating Cyber Attack Behaviors

Computer networks are becoming more complex as the reliance on these network increases in this era of exponential technological growth. This makes the potential gains for criminal activity on these networks extremely serious and can not only devastate organizations or enterprises but also the general population. As complexity of the network increases so does the difficulty to protect the networks as more potential vulnerabilities are introduced. Despite best efforts, traditional defenses like Intrusion Detection Systems and penetration tests are rendered ineffective to even amateur cyber adversaries. Networks now need to be analyzed at all times to preemptively detect weaknesses which harbored a new research field called Cyber Threat Analytics. However, current techniques for cyber threat analytics typically perform static analysis on the network and system vulnerabilities but few address the most variable and most critical piece of the puzzle -- the attacker themselves. This work focuses on defining a baseline framework for modeling a wide variety of cyber attack behaviors which can be used in conjunction with a cyber attack simulator to analyze the effects of individual or multiple attackers on a network. To model a cyber attacker\u27s behaviors with reasonable accuracy and flexibility, the model must be based on aspects of an attacker that are used in real scenarios. Real cyber attackers base their decisions on what they know and learn about the network, vulnerabilities, and targets. This attacker behavior model introduces the aspect of knowledge-based decision making to cyber attack behavior modeling with the goal of providing user configurable options. This behavior model employs Cyber Attack Kill Chain along with an ensemble of the attacker capabilities, opportunities, intent, and preferences. The proposed knowledge-based decision making model is implemented to enable the simulation of a variety of network attack behaviors and their effects. This thesis will show a number of simulated attack scenarios to demonstrate the capabilities and limitations of the proposed model

Strategies for Cybercrime Prevention in Information Technology Businesses

Cybercrime continues to be a devastating phenomenon, impacting individuals and businesses across the globe. Information technology (IT) businesses need solutions to defend and secure their data and networks from cyberattacks. Grounded in general systems theory and transformational leadership theory, the purpose of this qualitative multiple case study was to explore strategies IT business leaders use to protect their systems from a cyberattack. The participants included six IT business leaders with experience in cybersecurity or system security in the Midlands region of South Carolina. Data were collected using semistructured interviews and reviews of government standards documents; data were analyzed using thematic analysis. Three themes emerged from the study: (a) cybercrime prevention strategy; (b) cybersecurity awareness, training, and education; and (c) effective leadership. A key recommendation is for IT business leaders to ensure employees are current on cybersecurity awareness and defense techniques through regular training and education, use third-party vendors that are subject matter experts where they lack talent, and develop leaders with a transformational mindset. The implications for positive social change include the potential for IT business leaders and employees to become more proactive in learning and implementing effective cybercrime prevention strategies to keep their businesses profitable and support the needs of stakeholders and clients

A Game-Theoretic Decision-Making Framework for Engineering Self-Protecting Software Systems

Targeted and destructive nature of strategies used by attackers to break down a software system require mitigation approaches with dynamic awareness. Making a right decision, when facing today’s sophisticated and dynamic attacks, is one of the most challenging aspects of engineering self-protecting software systems. The challenge is due to: (i) the consideration of the satisfaction of various security and non-security quality goals and their inherit conflicts with each other when selecting a countermeasure, (ii) the proactive and dynamic nature of these security attacks which make their detection and consequently their mitigation challenging, and (iii) the incorporation of uncertainties such as the intention and strategy of the adversary to attack the software system. These factors motivated the need for a decision-making engine that facilitates adaptive security from a holistic view of the software system and the attacker. Inspired by game theory, in this research work, we model the interactions between the attacker and the software system as a two-player game. Using game-theoretic techniques, the self-protecting software systems is able to: (i) fuse the strategies of attackers into the decision-making model, and (ii) refine the strategies in dynamic attack scenarios by utilizing what has learned from the system’s and adversary’s interactions. This PhD research devises a novel framework with three phases: (i) modeling quality/malicious goals aiming at quantifying them into the decision-making engine, (ii) designing game-theoretic techniques which build the decision model based on the satisfaction level of quality/malicious goals, and (iii) realizing the decision-making engine in a working software system. The framework aims at exhibiting a plug-and-play capability to adapt a game-theoretic technique that suite security goals and requirements of the software. In order to illustrate the plug-and-play capability of our proposed framework, we have designed and developed three decision-making engines. Each engine aims at addressing a different challenge in adaptive security. Hence, three distinct techniques are designed: (i) incentive-based (“IBSP”), (ii) learning-based (“MARGIN”), and (iii) uncertainty-based (“UBSP”). For each engine a game-theoretic approach is taken considering the security requirements and the input information. IBSP maps the quality goals and the incentives of the attacker to the interdependencies among defense and attack strategies. MARGIN, protects the software system against dynamic strategies of attacker. UBSP, handles adversary-type uncertainty. The evaluations of these game-theoretic approaches show the benefits of the proposed framework in terms of satisfaction of security and non-security goals of the software system