With the advent of cloud computing and wireless sensor networks, the number of cyberattacks has rapidly increased. Therefore, the proportionate security of networks has become a challenge for organizations. Information security advisors of organizations face difficult and complex decisions in the evaluation and selection of information security controls that permit the defense of their resources and assets. Information security controls must be selected based on an appropriate level of security. However, their selection needs intensive investigation regarding vulnerabilities, risks, and threats prevailing in the organization as well as consideration of the implementation, mitigation, and budgetary constraints of the organization. The goal of this paper was to improve the information security control analysis method by proposing a formalized approach, i.e., fuzzy Analytical Hierarchy Process (AHP). This approach was used to prioritize and select the most relevant set of information security controls to satisfy the information security requirements of an organization. We argue that the prioritization of the information security controls using fuzzy AHP leads to an efficient and cost-effective assessment and evaluation of information security controls for an organization in order to select the most appropriate ones. The proposed formalized approach and prioritization processes are based on International Organization for Standardization and the International Electrotechnical Commission (ISO/IEC) 27001:2013. But in practice, organizations may apply this approach to any information security baseline manual.

We introduce two mechanisms for interference mitigation, inspired by evolutionary game theory and machine learning to support the coexistence of a macrocell network underlaid with self-organized femtocell networks. In the first approach, stand-alone femtocells choose their strategies, observe the behavior of other players, and make the best decision based on their instantaneous payoff, as well as the average payoff of all other femtocells. We formulate the interactions among selfish femtocells using evolutionary games and demonstrate how the system converges to an equilibrium. In contrast, in the Reinforcement-Learning (RL) approach, information exchange among femtocells is no longer possible and hence each femtocell adapts its strategy and gradually learns by interacting with its environment (i.e., neighboring interferers) through trials-and-errors. Our investigations reveal that through learning, femtocells are able to self-organize by relying only on local information, while mitigating the interference towards the macrocell network. Besides, a trade-off exists where faster convergence is obtained in the evolutionary case as compared to the RL approach, at the expense of more side information. Finally, it is shown that femtocells face an interesting tradeoff of exploration versus exploitation in their learning processes.