Despite the conventional wisdom that proactive security is superior to reactive security, we show that reactive security can be competitive with proactive security as long as the reactive defender learns from past attacks instead of myopically overreacting to the last attack. Our game-theoretic model follows common practice in the security literature by making worst-case assumptions about the attacker: we grant the attacker complete knowledge of the defender’s strategy and do not require the attacker to act rationally. In this model, we bound the competitive ratio between a reactive defense algorithm (which is inspired by online learning theory) and the best fixed proactive defense. Additionally, we show that, unlike proactive defenses, this reactive strategy is robust to a lack of information about the attacker’s incentives and knowledge.
According to one common view, information security comes down to technical measures. Given better access control policy models, formal proofs of cryptographic protocols, approved firewalls, better ways of detecting intrusions and malicious code, and better tools for system evaluation and assurance, the problems can be solved. The author puts forward a contrary view: information insecurity is at least as much due to perverse incentives. Many of the problems can be explained more clearly and convincingly using the language of microeconomics: network externalities, asymmetric information, moral hazard, adverse selection, liability dumping and the tragedy of the commons. We study the effect of user incentives on software security in a network of individual users under costly patching and negative network security externalities. For proprietary software or freeware, we compare four alternative policies to manage network security: (i) consumer self-patching (where no external incentives are provided for patching or purchasing); (ii) mandatory patching; (iii) patching rebate; and (iv) usage tax. We show that for proprietary software, when the software security risk and the patching costs are high, for both a welfare-maximizing social planner and a profit-maximizing vendor, a patching rebate dominates the other policies. However, when the patching cost or the security risk is low, self-patching is best. We also show that when a rebate is effective, the profit-maximizing rebate is decreasing in the security risk and increasing in patching costs. The welfare-maximizing rebates are also increasing in patching costs, but can be increasing in the effective security risk when patching costs are high. For freeware, a usage tax is the most effective policy except when both patching costs, and security risk are low, in which case a patching rebate prevails. Optimal patching rebates and taxes tend to increase with increased security risk and patching costs, but can decrease in the security risk for high-risk levels. Our results suggest that both the value generated from software and vendor profits can be significantly improved by mechanisms that target user incentives to maintain software security. 