The Internet of Vehicles (IoV) has become a fundamental component of intelligent transportation systems, enabling advanced services such as traffic management, collision avoidance, and driver assistance. However, the high level of openness and connectivity in IoV makes it highly vulnerable to Distributed Denial of Service (DDoS) attacks. Such attacks can severely disrupt vehicular communications and compromise user safety. Meanwhile, existing intrusion detection systems (IDSs) suffer from limitations in adapting to IoV’s dynamic topology and resource-constrained environment. To address these challenges, this paper proposes a novel ensemble learning–based IDS framework for accurate detection of multiple DDoS attack types. The framework integrates two optimized convolutional neural network (CNN) models to enhance detection performance. In addition, a dynamic deployment algorithm is designed to determine the optimal placement of the IDS among fog servers, unmanned aerial vehicles (UAVs), and roadside units (RSUs) based on real-time network conditions. To further improve resource efficiency, a game-theoretic strategy is employed to minimize energy consumption while maintaining high detection accuracy. Extensive experiments conducted on three benchmark datasets—VDoS-LRS, CICDDoS2019, and VDDD—demonstrate that the proposed IDS achieves over 99% detection accuracy and significantly outperforms existing methods in terms of precision and computational efficiency. These results confirm the effectiveness of combining ensemble deep learning with game-theoretic strategy for securing IoV against DDoS attacks.

Using unmanned aerial vehicles (UAVs) to cover users in wireless networks has increased in recent years. Deploying UAVs in appropriate positions is important to cover users and nodes properly. In this article, we propose an efficient approach to determine the minimum number of required UAVs and their optimal positions. To this end, we use an iterative algorithm that updates the number of required UAVs at each iteration. To determine the optimal position for the UAVs, we present a mathematical model and solve it accurately after linearizing. One of the inputs of the mathematical model is a set of candidate points for UAV deployments in 2-D space. The mathematical model selects a set of points among candidate points and determines the altitude of each UAV. To provide a suitable set of candidate points, we also propose a candidate point selection method: the MergeCells method. The simulation results show that the proposed approach performs better than the 3-D P-median approach introduced in the literature. We also compare different candidate point selection approaches, and we show that the MergeCells method outperforms other methods in terms of the number of UAVs, user data rates, and simulation time.

This article investigates the performance of multicriteria-based load-balancing scheme among gateways in fog-assisted Internet of Things (IoT). We employ a queueing model of the IoT system to calculate the latency of data streams from the IoT devices to the applications. However, a gateway node may easily become congested since all the traffic to IP networks are directed toward the gateway. A congested point can affect system performance and may cause reliability problems in the system. Thus, we employ multiple gateways to alleviate the performance degradation of the network, along with a multicriteria decision-making (MCDM)-based load-balancing policy among the gateways to achieve a global load fairness. The proposed model is evaluated in single-hop IPv6 over low-power wireless personal area networks (6LoWPANs). The evaluation results show the effectiveness of the proposed load-balancing model in providing fast and reliable responses to user queries.

Nowadays, several digital forensic tools extract a lot of low‐level information from different parts of the system. Constructing high‐level information from low‐level ones is very challenging. This study reconstructs high‐level events by using the traces of applications that are found in the file system metadata. In this regard, an event reconstruction framework is proposed that determines which applications have been run on a compromised system. The proposed framework works in two phases. In the training phase, the signatures of various applications are constructed. The signature of an application is the temporal pattern of file system modification of the application. In the detection phase, at first, the temporal pattern of file system modification of the hard disk (TPFSM‐D) of the compromised system is constructed. Then in order to determine whether a particular application has been run on the compromised system, the distance between the signature of the application and the TPFSM‐D of the hard disk is calculated by using a proposed distance measure. Finally, a decision engine decides whether the application has been run on the compromised system. The proposed event reconstruction framework has been tested on different scenarios. The empirical results suggest that the framework is effective in reconstructing events.

This paper presents a constrained application protocol enabled multithreaded Internet of Things (IoT) gateway that allows reliable and quick access to big data from IoT domains. We consider a proxy caching policy to accelerate the content delivery rate of sensor data while reducing the energy consumption of corresponding nodes. We propose a selective caching policy in which resource validation requests are sent based on user requests, besides updates from IoT nodes. We also describe an analytical model to evaluate the performance of multithreaded IoT gateway under various traffic conditions to meet the quality of service constraints. The proposed model is evaluated over single hop IPv6 over low-power wireless personal area networks/IEEE 802.15.4 cluster. The performance evaluation clearly shows the effectiveness of our model in providing fast response and reduced energy consumption of the resources.