Exploit

Abstract: Abstract Chapter 1 of this book defined and developed the strategic management question—Why do some firms outperform other firms?—and described the evolution of resource-based theory as one approach to answering this question. Chapter 2 introduced the concept of a strategic factor market to demonstrate that whether a firm gains competitive advantages does not depend just on strategies that create competitive imperfections in product markets, but on the total cost of implementing these strategies. This total cost is determined by the competitiveness of strategic factor markets. One of the central conclusions of this argument is that firms that exploit resources and capabilities they already control in choosing and implementing strategies are more likely to gain competitive advantages than firms that acquire the resources and capabilities they need to implement a strategy in more competitive factor markets. Of course, this conclusion is hardly unique to resource-based theory. Indeed, identifying and exploiting a firm’s strengths while avoiding its weaknesses has been one of the central features of one of the oldest organizing frameworks in the field of strategic management— the SWOT framework (Ans off 1965; Andrews 1971; Hofer and Schendel 1978). This framework, summarized in Figure 3.1, suggests that firms obtain competitive advantages by implementing strategies that exploit their internal strengths, through responding to environmental opportunities, while neutralizing external threats and avoiding internal weak-nesses.

Abstract: The ever-increasing demand for higher data rates in wireless communications in the face of limited or underutilized spectral resources has motivated the introduction of cognitive radio. Traditionally, licensed spectrum is allocated over relatively long time periods and is intended to be used only by licensees. Various measurements of spectrum utilization have shown substantial unused resources in frequency, time, and space [1], [2]. The concept behind cognitive radio is to exploit these underutilized spectral resources by reusing unused spectrum in an opportunistic manner [3], [4]. The phrase cognitive radio is usually attributed to Mitola [4], but the idea of using learning and sensing machines to probe the radio spectrum was envisioned several decades earlier (cf., [5]).

Abstract: Machine learning's ability to rapidly evolve to changing and complex situations has helped it become a fundamental tool for computer security. That adaptability is also a vulnerability: attackers can exploit machine learning systems. We present a taxonomy identifying and analyzing attacks against machine learning systems. We show how these classes influence the costs for the attacker and defender, and we give a formal structure defining their interaction. We use our framework to survey and analyze the literature of attacks against machine learning systems. We also illustrate our taxonomy by showing how it can guide attacks against SpamBayes, a popular statistical spam filter. Finally, we discuss how our taxonomy suggests new lines of defenses.