Artificial intelligence in the cyber domain: Offense and defense

Artificial intelligence techniques have grown rapidly in recent years, and their applications in practice can be seen in many fields, ranging from facial recognition to image analysis. In the cybersecurity domain, AI-based techniques can provide better cyber defense tools and help adversaries improve methods of attack. However, malicious actors are aware of the new prospects too and will probably attempt to use them for nefarious purposes. This survey paper aims at providing an overview of how artificial intelligence can be used in the context of cybersecurity in both offense and defense.Web of Science123art. no. 41

Military and Security Applications: Cybersecurity (Encyclopedia of Optimization, Third Edition)

The domain of cybersecurity is growing as part of broader military and security applications, and the capabilities and processes in this realm have qualities and characteristics that warrant using solution methods in mathematical optimization. Problems of interest may involve continuous or discrete variables, a convex or non-convex decision space, differing levels of uncertainty, and constrained or unconstrained frameworks. Cyberattacks, for example, can be modeled using hierarchical threat structures and may involve decision strategies from both an organization or individual and the adversary. Network traffic flow, intrusion detection and prevention systems, interconnected human-machine interfaces, and automated systems – these all require higher levels of complexity in mathematical optimization modeling and analysis. Attributes such as cyber resiliency, network adaptability, security capability, and information technology flexibility – these require the measurement of multiple characteristics, many of which may involve both quantitative and qualitative interpretations. And for nearly every organization that is invested in some cybersecurity practice, decisions must be made that involve the competing objectives of cost, risk, and performance. As such, mathematical optimization has been widely used and accepted to model important and complex decision problems, providing analytical evidence for helping drive decision outcomes in cybersecurity applications. In the paragraphs that follow, this chapter highlights some of the recent mathematical optimization research in the body of knowledge applied to the cybersecurity space. The subsequent literature discussed fits within a broader cybersecurity domain taxonomy considering the categories of analyze, collect and operate, investigate, operate and maintain, oversee and govern, protect and defend, and securely provision. Further, the paragraphs are structured around generalized mathematical optimization categories to provide a lens to summarize the existing literature, including uncertainty (stochastic programming, robust optimization, etc.), discrete (integer programming, multiobjective, etc.), continuous-unconstrained (nonlinear least squares, etc.), continuous-constrained (global optimization, etc.), and continuous-constrained (nonlinear programming, network optimization, linear programming, etc.). At the conclusion of this chapter, research implications and extensions are offered to the reader that desires to pursue further mathematical optimization research for cybersecurity within a broader military and security applications context

An Integrated Framework for Sensing Radio Frequency Spectrum Attacks on Medical Delivery Drones

Drone susceptibility to jamming or spoofing attacks of GPS, RF, Wi-Fi, and operator signals presents a danger to future medical delivery systems. A detection framework capable of sensing attacks on drones could provide the capability for active responses. The identification of interference attacks has applicability in medical delivery, disaster zone relief, and FAA enforcement against illegal jamming activities. A gap exists in the literature for solo or swarm-based drones to identify radio frequency spectrum attacks. Any non-delivery specific function, such as attack sensing, added to a drone involves a weight increase and additional complexity; therefore, the value must exceed the disadvantages. Medical delivery, high-value cargo, and disaster zone applications could present a value proposition which overcomes the additional costs. The paper examines types of attacks against drones and describes a framework for designing an attack detection system with active response capabilities for improving the reliability of delivery and other medical applications.Comment: 7 pages, 1 figures, 5 table

Current Trends in Small Unmanned Aircraft Systems: Implications for U.S. Special Operations Forces

This paper assesses current trends in small unmanned aircraft systems (sUAS) technology and its applications to the Special Operations Forces (SOF) community. Of critical concern to SOF is that commercial-off-the-shelf (COTS) sUAS technologies are relatively inexpensive, improving at a dramatic rate, and widely available throughout the world. Insurgents, terrorists, violent extremist organizations (VEOs) and other nefarious actors have used COTS sUAS to conduct offensive attacks as well as to develop battlefield situation awareness; these technological improvements combined with their widespread availability will require enhanced and rapidly adaptive counter-sUAS measures in the future. To understand the most current trends in the unmanned aircraft systems (UAS) technology and their applicability to SOF, this paper analyzes the definition and classification of sUAS, their major applications, and characteristics. In the military context, UAS are principally used for intelligence, surveillance, and reconnaissance (ISR), border security, counterinsurgency, attack and strike, target identification and designation, communications relay, electronic attack, remote sensing, and aerial mapping. As technology improves, smaller versions of sUAS will be used by both friendly operators and maligned actors (insurgents, terrorists, VEOs, nation states) as force multipliers for military operations. As armed forces around the world continue to invest in research and development of sUAS technologies, there will be tremendous potential to revolutionize warfare, particularly in context of special operations. Consequently, the use of sUAS technology by SOF is likely to escalate over the next decade, as is the likelihood of sUAS countermeasures due to the availability of the technology within nefarious organizations

Security, privacy and safety evaluation of dynamic and static fleets of drones

Inter-connected objects, either via public or private networks are the near future of modern societies. Such inter-connected objects are referred to as Internet-of-Things (IoT) and/or Cyber-Physical Systems (CPS). One example of such a system is based on Unmanned Aerial Vehicles (UAVs). The fleet of such vehicles are prophesied to take on multiple roles involving mundane to high-sensitive, such as, prompt pizza or shopping deliveries to your homes to battlefield deployment for reconnaissance and combat missions. Drones, as we refer to UAVs in this paper, either can operate individually (solo missions) or part of a fleet (group missions), with and without constant connection with the base station. The base station acts as the command centre to manage the activities of the drones. However, an independent, localised and effective fleet control is required, potentially based on swarm intelligence, for the reasons: 1) increase in the number of drone fleets, 2) number of drones in a fleet might be multiple of tens, 3) time-criticality in making decisions by such fleets in the wild, 4) potential communication congestions/lag, and 5) in some cases working in challenging terrains that hinders or mandates-limited communication with control centre (i.e., operations spanning long period of times or military usage of such fleets in enemy territory). This self-ware, mission-focused and independent fleet of drones that potential utilises swarm intelligence for a) air-traffic and/or flight control management, b) obstacle avoidance, c) self-preservation while maintaining the mission criteria, d) collaboration with other fleets in the wild (autonomously) and e) assuring the security, privacy and safety of physical (drones itself) and virtual (data, software) assets. In this paper, we investigate the challenges faced by fleet of drones and propose a potential course of action on how to overcome them.Comment: 12 Pages, 7 Figures, Conference, The 36th IEEE/AIAA Digital Avionics Systems Conference (DASC'17

InfoSwarms: Drone Swarms and Information Warfare

Drone swarms, which can be used at sea, on land, in the air, and even in space, are fundamentally information-dependent weapons. No study to date has examined drone swarms in the context of information warfare writ large. This article explores the dependence of these swarms on information and the resultant connections with areas of information warfare—electronic, cyber, space, and psychological—drawing on open-source research and qualitative reasoning. Overall, the article offers insights into how this important emerging technology fits into the broader defense ecosystem and outlines practical approaches to strengthening related information warfare capabilities

AN ANALYSIS OF HOW THE U.S. GOVERNMENT CAN EFFECTIVELY TACKLE SUPPLY CHAIN BARRIERS TO SCALE UP THE LOW COST UNMANNED AERIAL VEHICLE (UAV) SWARMING TECHNOLOGY (LOCUST) PROGRAM

The LOCUST program is a scalable system of inexpensive swarming unmanned aerial vehicles to provide disruptive capability in contested environments against anti-area access denial defenses, enabling manned strike operations and localized landing site superiority with reduced cost, risk, and operator launch and workload. Our research and analysis will emphasize the challenges of moving from a U.S. Special Operations Command (USSOCOM) effort to a large program of record. Specific supply chain concerns that will be addressed include: 1) DOD organizational structure; 2) service-specific objectives and currently operating platforms; 3) requirements generation and related procurements to include production and quality challenges; 4) safety and quality assurance standards; 5) lead times, inventory plans, and throughput to include supplier base considerations and consolidations; and 6) latest evolving technologies and continuous improvement principles. Our team will utilize the Define, Measure, Analyze, Improve, Control (DMAIC) evaluative methodology that focuses on data-driven improvement cycles to better optimize process, design and results. Our results and recommendations highlighted multiple strategies that the Office of Naval Research (ONR) must focus on when developing the LOCUST supply chain. These conclusions and findings address both current supply chain development opportunities for the LOCUST program, as well as where the program must focus its efforts in the future.http://archive.org/details/ananalysisofhowt1094563516Civilian, Department of the NavyCivilian, Department of the ArmyCivilian, Department of the ArmyApproved for public release; distribution is unlimited

Artificial Intelligence, International Competition, and the Balance of Power (May 2018)

World leaders, CEOs, and academics have suggested that a revolution in artificial intelligence is upon us. Are they right, and what will advances in artificial intelligence mean for international competition and the balance of power? This article evaluates how developments in artificial intelligence (AI) — advanced, narrow applications in particular — are poised to influence military power and international politics. It describes how AI more closely resembles “enabling” technologies such as the combustion engine or electricity than a specific weapon. AI’s still-emerging developments make it harder to assess than many technological changes, especially since many of the organizational decisions about the adoption and uses of new technology that generally shape the impact of that technology are in their infancy. The article then explores the possibility that key drivers of AI development in the private sector could cause the rapid diffusion of military applications of AI, limiting first-mover advantages for innovators. Alternatively, given uncertainty about the technological trajectory of AI, it is also possible that military uses of AI will be harder to develop based on private-sector AI technologies than many expect, generating more potential first-mover advantages for existing powers such as China and the United States, as well as larger consequences for relative power if a country fails to adapt. Finally, the article discusses the extent to which U.S. military rhetoric about the importance of AI matches the reality of U.S. investments.LBJ School of Public Affair