Adaptive just-in-time code diversification

We present a method to regenerate diversified code dynamically in a Java bytecode JIT compiler, and to update the diversification frequently during the execution of the program. This way, we can significantly reduce the time frame in which attackers can let a program leak useful address space information and subsequently use the leaked information in memory exploits. A proof of concept implementation is evaluated, showing that even though code is recompiled frequently, we can achieved smaller overheads than the previous state of the art, which generated diversity only once during the whole execution of a program

Strategic Culture as the Basis for Military Adaptive Capacity: Overcoming battlefield technological surprises

The ability of a military to respond to environmental changes rather than rigidly adhere to previously defined concepts of operation is paramount to overcoming unforeseen battlefield technological challenges. A force with the greater capacity for learning and adaptation will possess significant advantages in overcoming unforeseen challenges. However, it is unclear as to what determines the flexibility or adaptive capacity of a military during military engagements. To address this issue, this study focuses on intra- war adaptation as a product of a military’s strategic culture in overcoming enemy technological surprises. The work demonstrates the symbiotic relationship between strategic culture and adaptability that ultimately determines how effectively a force will respond to unforeseen battlefield challenges. For this reason, strategic culture is indispensable in explaining why militaries may continue to act in ways that are incongruous with prevailing operational circumstances while others are adept at responding to Clausewitzian fog and friction

Cyber security research frameworks for coevolutionary network defense

Cyber security is increasingly a challenge for organizations everywhere. Defense systems that require less expert knowledge and can adapt quickly to threats are strongly needed to combat the rise of cyber attacks. Computational intelligence techniques can be used to rapidly explore potential solutions while searching in a way that is unaffected by human bias. Several architectures have been created for developing and testing systems used in network security, but most are meant to provide a platform for running cyber security experiments as opposed to automating experiment processes. In the first paper, we propose a framework termed Distributed Cyber Security Automation Framework for Experiments (DCAFE) that enables experiment automation and control in a distributed environment. Predictive analysis of adversaries is another thorny issue in cyber security. Game theory can be used to mathematically analyze adversary models, but its scalability limitations restrict its use. Computational game theory allows us to scale classical game theory to larger, more complex systems. In the second paper, we propose a framework termed Coevolutionary Agent-based Network Defense Lightweight Event System (CANDLES) that can coevolve attacker and defender agent strategies and capabilities and evaluate potential solutions with a custom network defense simulation. The third paper is a continuation of the CANDLES project in which we rewrote key parts of the framework. Attackers and defenders have been redesigned to evolve pure strategy, and a new network security simulation is devised which specifies network architecture and adds a temporal aspect. We also add a hill climber algorithm to evaluate the search space and justify the use of a coevolutionary algorithm --Abstract, page iv

Artificial Intelligence and Machine Learning: A Perspective on Integrated Systems Opportunities and Challenges for Multi-Domain Operations

This paper provides a perspective on historical background, innovation and applications of Artificial Intelligence (AI) and Machine Learning (ML), data successes and systems challenges, national security interests, and mission opportunities for system problems. AI and ML today are used interchangeably, or together as AI/ML, and are ubiquitous among many industries and applications. The recent explosion, based on a confluence of new ML algorithms, large data sets, and fast and cheap computing, has demonstrated impressive results in classification and regression and used for prediction, and decision-making. Yet, AI/ML today lacks a precise definition, and as a technical discipline, it has grown beyond its origins in computer science. Even though there are impressive feats, primarily of ML, there still is much work needed in order to see the systems benefits of AI, such as perception, reasoning, planning, acting, learning, communicating, and abstraction. Recent national security interests in AI/ML have focused on problems including multidomain operations (MDO), and this has renewed the focus on a systems view of AI/ML. This paper will address the solutions for systems from an AI/ML perspective and that these solutions will draw from methods in AI and ML, as well as computational methods in control, estimation, communication, and information theory, as in the early days of cybernetics. Along with the focus on developing technology, this paper will also address the challenges of integrating these AI/ML systems for warfare