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Fog-assisted Internet-of-Things (Fog-IoT) systems are deployed in remote and unprotected environments, making them vulnerable to security, privacy, and trust challenges. Existing studies propose security schemes and trust models for these systems. However, mitigation of insider attacks, namely, blackhole, sinkhole, sybil, collusion, self-promotion, and privilege escalation, has always been a challenge and mostly carried out by the legitimate nodes. Compared to other studies, this article proposes a framework featuring attribute-based access control and trust-based behavioral monitoring to address the challenges mentioned above. The proposed framework consists of two components, the security component (SC) and the trust management component (TMC). SC ensures data confidentiality, integrity, authentication, and authorization. TMC evaluates Fog-IoT entities’ performance using a trust model based on a set of Quality of Service (QoS) and network communication features. Subsequently, trust is embedded as an attribute within SC’s access control policies, ensuring that only trusted entities are granted access to fog resources. Several attacking scenarios, namely, Denial of Service (DoS), Distributed DoS, probing, and data theft are designed to elaborate on how the change in trust triggers the change in access rights and, therefore, validates the proposed integrated framework’s design principles. The framework is evaluated on a Raspberry Pi 3 Model B+ to benchmark its performance in terms of time and memory complexity. Our results show that both SC and TMC are lightweight and suitable for resource-constrained devices.

Recently, D/DoS attacks have been launched by zombie IoT devices in smart home networks. They pose a great threat to network systems with Application Layer DDoS attacks being especially hard to detect due to their stealth and seemingly legitimacy. In this paper, we propose ForChaos, a lightweight detection algorithm for IoT devices, which is based on forecasting and chaos theory to identify flooding and DDoS attacks. For every time-series behaviour collected, a forecasting-technique prediction is generated, based on a number of features, and the error between the two values is calculated. In order to assess the error of the forecasting from the actual value, the Lyapunov exponent is used to detect potential malicious behaviour. In NS-3 we evaluate our detection algorithm through a series of experiments in flooding and slow-rate DDoS attacks. The results are presented and discussed in detail and compared with related studies, demonstrating its effectiveness and robustness.

—Short Message Service (SMS) is a widely used communication medium for mobile applications, such as banking, social\ud networking, and e-commerce. Applications of SMS services also include real-time broadcasting messages, such as notification of\ud natural disasters (e.g., bushfires and hurricane) and terrorist attacks, and sharing the current whereabouts to other users, such as\ud notifying urgent business meeting information, transmitting quick information in the battlefield to multiple users, notifying current\ud location to our friends and sharing market information. However, traditional SMS is not designed with security in mind (e.g., messages\ud are not securely sent). It is also possible to extract international mobile subscriber identity of the mobile user. In the literature, there is\ud no known protocol that could enable secure transmission of SMS from one user to multiple users simultaneously. In this paper, we\ud introduce a batch verification authentication and key agreement protocol, BVPSMS, which provides end-to-end message security over\ud an insecure communication channel between different mobile subscribers. Specifically, the proposed protocol securely transmits SMS\ud from one mobile user to multiple users simultaneously. The reliability of the protocol is discussed along with an algorithm to detect\ud malicious user request in a batch. We then evaluate the performance of the proposed protocol in terms of communication and\ud computation overheads, protocol execution time, and batch and re-batch verification times. The impacts of the user mobility, and the\ud time, space and cost complexity analysis are also discussed. We then present a formal security proof of the proposed protocol. To the\ud best of our knowledge, this is the first provably-secure batch verification protocol that delivers end-to-end SMS security using\ud symmetric keys.

This survey investigates the contributions of research into the detection of ransomware malware using machine learning and deep learning algorithms. The main motivations for this study are the destructive nature of ransomware, the difficulty of reversing a ransomware infection, and how important it is to detect it before infecting a system. Machine learning is coming to the forefront of combatting ransomware, so we attempted to identify weaknesses in machine learning approaches and how they can be strengthened. The threat posed by ransomware is exceptionally high, with new variants and families continually being found on the internet and dark web. Recovering from ransomware infections is difficult, given the nature of the encryption schemes used by them. The increase in the use of artificial intelligence also coincides with this boom in ransomware. The exploration into machine learning and deep learning approaches when it comes to detecting ransomware poses high interest because machine learning and deep learning can detect zero-day threats. These techniques can generate predictive models that can learn the behaviour of ransomware and use this knowledge to detect variants and families which have not yet been seen. In this survey, we review prominent research studies which all showcase a machine learning or deep learning approach when detecting ransomware malware. These studies were chosen based on the number of citations they had by other research. We carried out experiments to investigate how the discussed research studies are impacted by malware evolution. We also explored the new directions of ransomware and how we expect it to evolve in the coming years, such as expansion into IoT (Internet of Things), with IoT being integrated more into infrastructures and into homes.