Like smart phones, the recent years have seen an increased usage of internet of things (IoT) technology. IoT devices, being resource constrained due to smaller size, are vulnerable to various security threats. Recently, many distributed denial of service (DDoS) attacks generated with the help of IoT botnets affected the services of many websites. The destructive botnets need to be detected at the early stage of infection. Machine-learning models can be utilized for early detection of botnets. This paper proposes one-class classifier-based machine-learning solution for the detection of IoT botnets in a heterogeneous environment. The proposed one-class classifier, which is based on one-class KNN, can detect the IoT botnets at the early stage with high accuracy. The proposed machine-learning-based model is a lightweight solution that works by selecting the best features leveraging well-known filter and wrapper methods for feature selection. The proposed strategy is evaluated over different datasets collected from varying network scenarios. The experimental results reveal that the proposed technique shows improved performance, consistent across three different datasets used for evaluation.

Vehicle ad hoc networks (VANETs) are vital towards the success and comfort of self-driving as well as semi-automobile vehicles. Such vehicles rely heavily on data management and the exchange of Cooperative Awareness Messages (CAMs) for external communication with the environment. VANETs are vulnerable to a variety of attacks, including Black Hole, Gray Hole, wormhole, and rush attacks. These attacks are aimed at disrupting traffic between cars and on the roadside. The discovery of Black Hole attack has become an increasingly critical problem due to widespread adoption of autonomous and connected vehicles (ACVs). Due to the critical nature of ACVs, delay or failure of even a single packet can have disastrous effects, leading to accidents. In this work, we present a neural network-based technique for detection and prevention of rushed Black and Gray Hole attacks in vehicular networks. The work also studies novel systematic reactions protecting the vehicle against dangerous behavior. Experimental results show a superior detection rate of the proposed system in comparison with state-of-the-art techniques.

Computation offloading transfers resource-intensive tasks from local Internet of Things (IoT) devices to powerful edge servers, which minimizes latency and reduces the computational load on IoT devices. Deep Reinforcement Learning (DRL) is widely utilized to optimize computation offloading decisions. However, previous studies fall short in two main ways: firstly, they do not collectively optimize the comprehensive state space, and secondly, their reliance on Q-learning and Deep Q Networks (DQN) makes it challenging for agents to discern the optimal action in large action spaces, as many actions may possess similar values. In this paper, we introduce a multi-branch Dueling Deep Q Network (MBDDQN) that tackles the challenges of high-dimensional state-action spaces and long-term cost optimizations in dynamic environments. The Dueling DQN alleviates the complexity of simultaneous offloading and resource allocation decisions, with each branch independently controlling a subset of the decision variables to scale efficiently with an increasing number of IoT devices, thereby avoiding the combinatorial explosion of potential actions. Furthermore, we implement a long short-term memory (LSTM) network with distinct advantage-value layers to enhance both short-term action selection and long-term system cost estimation, as well as improve the temporal learning capacity of the model. Finally, we propose an innovative adaptive cost-weighting mechanism within the reward function to dynamically balance competing objectives, including energy consumption, latency, and bandwidth utilization. Unlike prior works that use fixed reward structures, we leverage weighted state-action advantage values to dynamically adjust the optimization variables. This approach also enables the agent to self-tune, allowing it to prioritize delay minimization in delay-sensitive scenarios and energy conservation in resource-constrained environments. Simulation results demonstrate the superiority of the proposed scheme compared to benchmarks. For instance, MBDDQN reduces delay by 17.88% over DQN and 12.28% over DDPG. Additionally, regarding energy consumption, MBDDQN achieves a 10.1% improvement over DQN and a 7.64% enhancement over DDPG.

High-speed internet has boosted clients’ traffic needs. Content caching on mobile edge computing (MEC) servers reduces traffic and latency. Caching with MEC faces difficulties such as user mobility, limited storage, varying user preferences, and rising video streaming needs. The current content caching techniques consider user mobility and content popularity to improve the experience. However, no present solution addresses user preferences and mobility, affecting caching decisions. We propose mobility- and user-preferences-aware caching for MEC. Using time series, the proposed system finds mobility patterns and groups nearby trajectories. Using cosine similarity and CF, we predict and cache user-requested content. CF predicts the popularity of grouped-based content to improve the cache hit ratio and reduce delay compared to baseline techniques.

Vehicular edge networks (VENs) connect vehicles to share data and infotainment content collaboratively to improve network performance. Due to technological advancements, data growth is accelerating, making it difficult to always connect mobile devices and locations. For vehicle-to-vehicle (V2V) communication, vehicles are equipped with onboard units (OBU) and roadside units (RSU). Through back-haul, all user-uploaded data is cached in the cloud server’s main database. Caching stores and delivers database data on demand. Pre-caching the data on the upcoming predicted server, closest to the user, before receiving the request will improve the system’s performance. OBUs, RSUs, and base stations (BS) cache data in VENs to fulfill user requests rapidly. Pre-caching reduces data retrieval costs and times. Due to storage and computing expenses, complete data cannot be stored on a single device for vehicle caching. We reduce content delivery delays by using the cuckoo search optimization algorithm with cooperative content caching. Cooperation among end users in terms of data sharing with neighbors will positively affect delivery delays. The proposed model considers cooperative content caching based on popularity and accurate vehicle position prediction using K-means clustering. Performance is measured by caching cost, delivery cost, response time, and cache hit ratio. Regarding parameters, the new algorithm outperforms the alternative.

The rapid growth of unmanned aerial vehicles (UAVs) has huge potential in the oil and gas industry (OGI). These are especially helpful in situations in which human lives are at risk. OGI pipelines are threatened by natural and man-made disasters, which are harmful to both people and assets. Usually, oil and gas pipelines pass through extreme environments for which standard inspection, maintenance, and repairing approaches such as rope access, scaffolds, telescopic elevation platforms supported by cranes, and manned helicopters are not secure and difficult and expensive to implement. However, technological advancements like device miniaturization have boosted the performance of UAVs, offering cost-effective, efficient, and high mission flexibility. UAVs are capable of carrying sensors and cameras to perform monitoring. As pipelines span thousands of kilometers, multi-UAV systems, commonly referred to as flying ad-hoc networks (FANETs), can collaboratively complete monitoring missions more effectively and economically as compared with single UAV systems. Moreover, many issues must be resolved before the effective use of UAVs can provide stable and reliable context-specific networks. Several OGI-specific issues of UAVs such as architecture design, platform, sensors, networking architectures, and path planning models for different OGI pipeline surveillance scenarios must be resolved to use FANETs effectively for robust and sustainable networks. The prime objective of this research is a state-of-the-art review of UAVs in OGI. We first present OGI midstream challenges and parameters to give a brief overview of the challenges faced by the OGI for pipeline surveillance. Then we discuss OGI-specific scenarios for sensor readings, visual leak detection, and detection of any unusual activity that happens in the pipeline while monitoring through UAVs. We also present different OGI-specific UAV platforms, UAV networking architectures, and path planning models of FANET for efficient communication and collaboration. Finally, the challenges of UAVs and future research prospects in OGI-specific UAVs are highlighted.