Chapter 1 summarizes the dramatic but unexpected societal and international security changes that have accompanied the introduction of the Internet. It also provides a quick introduction to packet-based switching that underpins the Internet as well as the World Wide Web, which transformed the Internet from a technical wonder into a very useful societal tool. It lays out the principle challenges of cybersecurity, considers malicious actors and motivations, and begins to consider the roles governments play in making cyberspace safer.

Chapter 9 takes up artificial intelligence (AI) and ethics. Beginning in Ancient Greece with the first autonomous machines, this chapter presents a brief history of AI. It then examines excessively ambitious expectations in the twentieth century for the potential for AI and the adverse consequences for research funding that resulted, now dubbed the “AI Winter.” New technologies, especially those with the elevated expectations of AI, often draw a lot of positive speculation, some of it misplaced. The chapter also reviews technologies that were explored in developing AI, such as logic, symbol manipulation, problem solving, expert systems and machine learning based largely on artificial neural networks. It also examines “adversarial attacks” in which very slight changes in an input can change the classification of an image. The applications of AI technologies to robots are discussed and caveats issued for their use. These include ethical issues that arise with the use of lethal autonomous weapons systems. The chapter closes with a discussion of the application of AI technologies to cybersecurity.

Introduction

In this chapter, we consider a canonical online problem that captures the basic decision question encountered in online algorithms. Assume that you arrive at a ski resort in the middle of the ski season. To rent a pair of skis, it takes $1 per day, while to buy them outright, it costs $P. On each new day, you only get to see whether the season is on-going or not, and have to decide whether to buy the ski or keep renting. The objective is to ski for as long as the season lasts with minimum cost possible without, however, knowing the remaining length of the skiing season. This problem is popularly known as the ski-rental problem. The ski-rental problem illustrates the inherent challenge of making decisions under uncertainty, where the uncertainty can even be controlled by an adversary depending on your current or past decisions.

The ski-rental problem models the classic rent/buy dilemma, where the uncertainty about future utility makes the problem challenging. It is highly relevant in various real-world applications, e.g., whether to rent/buy an expensive equipment or a luxury item with unknown number of days of utility, networking/scheduling problems where there are multiple servers with different service guarantees and prices. In scheduling, the following simple problem is equivalent to the ski-rental problem. Consider two servers, where one is shared and follows a FIFO discipline and has a minimal cost while the other is costly but dedicated. The decision to make for each user/packet is whether to stay with the shared server or jump to the dedicated one any time until it is served/processed.

In this chapter, we consider both deterministic and randomized algorithms for the ski-rental problem, and derive optimal algorithms in both settings, which is typically not possible for most of the other online problems considered in the book. We also describe the generic technique to lower bound the competitive ratio of randomized algorithms using Yao's principle. Two extensions of the ski-rental problem – the TCP (transmission control protocol) acknowledgement problem and the Bahncard problem – are also discussed at the end.

In Chapter 5, we look at approaches that belong to heuristic algorithms. These methods are derived from observations nature provide. In our argumentation for specific heuristic optimization algorithms, we discuss the local search and the hill climbing problem. One of the outcomes of this discussion is the argument for attempting to avoid cycling during a search. Tabu search optimization is built on this premise where we avoid cycling. An entirely different class of heuristic optimization algorithms are given by Particle Swarm optimization and Ant Colony optimization algorithms. In contrast to Tabu search and local search, the PSO and AC optimization algorithms utilize a number of agents in order to search for optimality. Another multi agent based algorithm is the Genetic algorithm. GA’s are inspired by Darwin’s survival of the fittest principle and use the terminology found in the field of genetics. Additionally, in this chapter we use heuristic optimization to formulate optimum control concepts, including hybrid control using fuzzy logic-based controllers and Matlab scripts to realize each of the heuristic optimization algorithms.