Search

In this chapter, we review the pervasiveness of cyber threats and the roles of both attackers and cyber users (i.e. the targets of the attackers); the lack of awareness of cyber-threats by users; the complexity of the new cyber environment, including cyber risks; engineering approaches and tools to mitigate cyber threats; and current research to identify proactive steps that users and groups can take to reduce cyber-threats. In addition, we review the research needed on the psychology of users that poses risks to users from cyber-attacks. For the latter, we review the available theory at the individual and group levels that may help individual users, groups and organizations take actions against cyber threats. We end with future research needs and conclusions. In our discussion, we first agreed that cyber threats are making cyber environments more complex and uncomfortable for average users; second, we concluded that various factors are important (e.g., timely actions are often necessary in cyber space to counter the threats of the attacks that commonly occur at internet speeds, but also the ‘slow and low’ attacks that are difficult to detect, threats that occur only after pre-specified conditions have been satisfied that trigger an unsuspecting attack). Third, we concluded that advanced persistent threats (APTs) pose a risk to users but also to national security (viz., the persistent threats posed by other Nations). Fourth, we contend that using “red” teams to search cyber defenses for vulnerabilities encourages users and organizations to better defend themselves. Fifth, the current state of theory leaves many questions unanswered that researchers must pursue to mitigate or neutralize present and future threats. Lastly, we agree with the literature that cyber space has had a dramatic impact on American life and that the cyber domain is a breeding ground for disorder. However, we also believe that actions by users and researchers can be taken to stay safe and ahead of existing and future threats.

As more people browse the Web to gather information, recognizing Web browsing behavior signatures can replace or complement keystroke authentication where authentication is defined as the capability of identifying an individual within a set of individuals. We claim that recurring temporal patterns of Web site visits can help identify an individual of interest and, more generally, categorize Web browsing behavior. Furthermore, just like keystroke authentication, attribution of Web behavior is not obtrusive and has applications in cyberwarfare as a new biometric technique. In this paper we describe some exploratory work and preliminary comparative results of machine learning techniques applicable to the attribution of Web browsing behavior problem.

In heterogeneous multi-agent systems, where human and non-human agents coexist, intelligent proxy agents can help smooth out fundamental differences. In this context, delegating the coordination role to proxy agents can improve the overall outcome of a task at the expense of human cognitive overload due to switching subtasks. Stability and commitment are characteristics of human teamwork, but must not prevent the detection of better opportunities. In addition, coordination proxy agents must be trained from examples as a single agent, but must interact with multiple agents. We apply machine learning techniques to the task of learning team preferences from mixed-initiative interactions and compare the outcome results of different simulated user patterns. This chapter introduces a novel approach for the adjustable autonomy of coordination proxies based on the reinforcement learning of abstract actions. In conclusion, some consequences of the symbiotic relationship that such an approach suggests are discussed.

SSTR operations are becoming a priority for the Department of Defense (DoD) and are being given the same stature as the more traditional combat operations. The near-term goal of SSTR is to provide the local populace with security, restore essential services, and meet humanitarian needs, while in the longer term ensure a stable infrastructure. Large scale disasters are an example where SSTR operations can provide value. In such scenarios, local governments and non-governmental institutions are under great stress in order to respond in a timely manner to provide basic relief to the affected communities, and there may be outburst of aid from the local groups. All of this can lead to a less than desirable outcome if poorly coordinated! The use of unmanned aerial vehicles (UAVs) to support intelligence, surveillance and reconnaissance (ISR) is becoming increasingly important. These assets can enable the collection of needed information for the execution of a given set of tasks. In large scale operations, however, the ability for the UAVs to self-coordinate may be needed as it will be difficult for human operators to effectively control large teams of UAVs. This paper will begin by introducing some of the key aspects of multiagent coordination, with a focus on the operational challenges with regard to SSTR such as disaster management response as well as UAV coordination. We will then discuss the coordination challenges and gaps in order to motivate an adaptive, multiagent based approach to coordination as well as additional opportunities for research. We will conclude with a brief summary

On-policy reinforcement learning provides online adaptation, a characteristic of intelligent systems and lifelong learning. Unlike dynamic programming, an exhaustive sweep of the search space is not necessary for convergence in reinforcement learning with an efficient exploration strategy. For efficient and "believable" online performance, an exploration strategy also has to avoid cycling through previous solutions and know when to stop without getting stuck in a local optimum. This paper addresses the above problem with tabu search (TS) exploration. Several strategies for reinforcement learning are introduced. Experimental results are presented in the game of Go, a deterministic, perfect-information two-player game, and Sarsa learning vector quantization (SLVQ), an on-policy reinforcement learning algorithm.

This little gem of a book introduces the key aspects of mobile technology affecting human-computer interaction, which could dramatically transform our day-to-day lives. It also provides general guidance on research methods, including data analysis, and plenty of external references. This book could serve as a supplemental text for an undergraduate class in psychology, information systems, or the social sciences. Steve Love conveys the mobile technology’s strong impact on HCI by presenting issues from three main disciplines—cognitive psychology, application design, and the social sciences—and he uses cell phones and PDAs as his main example devices. The book doesn’t cover computer science issues or mobile HCI theory in great detail but discusses issues pertinent to research projects.