The Internet of Things (IoT) has recently received a lot of attention from the information and communication technology community. It has turned out to be a crucial development for harnessing the incredible power of wireless media in the real world. The nature of IoT-Fog networks requires the use of defense techniques who are light and mobile-aware. The edge resources in such a distributed environment are open to various safety hazards. DDoS UDP flooding attacks are the most frequent threats to edge resources in IoT-Fog networks. It is crucial for sabotaging fog gateways and can overcome traditional data filtering techniques. This paper introduces M-RL, a lightweight intrusion detection system with mobility awareness that can detect DDoS UDP flooding attacks while taking into account adversarial IoT devices that engage in IP spoofing. To this end, this paper analyzes the malicious behaviors that result in anonymity against Rate Limiting and Received Signal Strength (RSS)-based approaches, combines their advantages, and addresses their vulnerabilities. We test our method in different contexts to achieve that goal, and we find that it may decrease the accuracy of the RL, RSS, and RSS-RL methods to 70%, 48.9%, and 64.3%, respectively. The outcomes demonstrate the proposed approach's resistance to software-based source address forgery, impersonation, and signal modification. It offers more than 99% accuracy and supports node mobility. In this case, the best possible accuracy of the previous methods is 77%.

Smart devices improve the quality of life by collecting, analyzing, and transmitting data across different channels. Unfortunately, public media are prone to adversaries, and such devices must protect the privacy and confidentiality of users’ data. Although authentication and key agreement protocols achieve the goal, they may suffer from hidden vulnerabilities; detecting them requires solid mathematical knowledge. Informal methods can discover protocol vulnerabilities, but most of them are limited to analyzing a specific protocol. Therefore, it is essential to provide a way for analyzing arbitrary ones. This paper proposes several algorithms to perform informal analyzes inO(n3×log(n)). Also, the article offers some ideas for optimizing these algorithms to achieveO(n2), wherenis the number of involved parameters in the protocol. Additionally, this paper introduces three graphical representations for vulnerability detection and examines their strengths and weaknesses. The compact version of such an expression has a better performance compared to the others. This method enables students with a weak mathematical background to analyze complex protocols on a piece of paper. The article exploresfourprotocols published in recent years and describes how adversaries can obtain session keys in different scenarios.

Authentication protocols are powerful tools to ensure confidentiality as an important feature of Internet of Things (IoT). The Denial-of-Service (DoS) attack is one of the significant threats to availability, as another essential feature of IoT, which deprives users of services by consuming the energy of IoT nodes. On the other hand, computational intelligence algorithms can be applied to solve such issues in the network and cyber domains. Motivated by this, this article links these concepts. To do so, we analyze two lightweight authentication protocols, present a DoS attack inspired by users' misbehavior and suggest a solution called received signal strength, which is easy to compute, applicable for resisting against different kinds of vulnerabilities in Internet protocols, and feasible for practical implementations. We implement it on two scenarios for locating attackers, investigate the effects of IoT devices' internal error on locating, and propose an optimization problem to finding the exact location of attackers, which is efficiently solvable for computational intelligence algorithms, such as TLBO. Besides, we analyze the solutions for unreliable results of accurate devices and provide a solution to detect attackers with less than 12-cm error and the false alarm probability of 0.7%.