Distributed Denial-of-Service (DDoS) attacks are widespread for Internet of Things (IoT) systems that aim to disrupt the availability of a system completely (high-rate DDoS) or partially (low-rate DDoS). Design and placement of intrusion detection systems (IDSs) for DDoS attacks on IoT systems are challenging due to the low power and lossy nature of networks. Existing IDSs are designed to handle either high-rate or low-rate DDoS but cannot handle both with good accuracy. Existing IDS placement techniques are mostly nontransparent, making malicious nodes aware of the presence of IDS nodes. Most of the IDS placement strategies are nonoptimal, making them energy inefficient. Accordingly, this work proposes a transparent, optimally placed, distributed IDS solution, namely, OPTIMIST, which can handle both high-rate and low-rate DDoS attacks with good accuracy. The placement problem is formulated as the weighted minimum vertex cover problem of a${K}$-uniform hypergraph and solved with an approximation algorithm. The IDS module is based on a long short-term memory (LSTM) model where a novel offline training method for LSTM is proposed using Wasserstein GAN-generated artificial flows. Extensive experimentation on simulation and testbed shows that the OPTIMIST can best achieve the balance between DDoS detection and energy overhead.

Video traffic demands for vehicle infotainment services have rapidly increased in recent years. To reduce bandwidth costs and delays, content distributors (CD) use edge vehicles to distribute video contents over vehicular networks. CDs usually offer multiple subscription tiers of varying video qualities for their clients. A CD’s business goal is to make a minimal edge vehicle selection to maximize Internet traffic offloading while satisfying heterogeneous video quality requirements of clients belonging to different subscription tiers. This work proposes an optimum cost-effective solution for edge vehicle selection for delay-constrained live video streaming over vehicular networks with multi-tier bitrate agreements. The edge vehicle selection problem is formulated as the minimum set covering problem for optimum results, and a distributed approximation solution is proposed to increase scalability. Simulation results show that the proposed solution is cost-effective for CDs and saves significant control message overhead while satisfying delay constraints and multi-tier bitrate agreements.

The volume of video traffic as infotainment service over vehicular ad hoc network (VANET) has rapidly increased for past few years. Providing video streaming as VANET infotainment service is very challenging because of high mobility and heterogeneity of vehicular networks. While the number of mobile gateways (vehicles connected to the Internet) needs to be minimized to reduce service cost, the streaming quality also needs to be satisfactory for end-users. Existing works either focus on gateway minimization or focus on enhancing user satisfaction. We propose a video streaming solution, namely, DGSVS, which does gateway minimization with the constrained time data delivery to end-users. We formulate our constrained gateway minimization problem as minimum set covering (MSC) problem and solve with a distributed approximation method for MSC. We assume that only a subset of vehicles in VANET run DGSVS application. Therefore, instead of application layer cooperation for gateway-client association, network layer cooperation is proposed. We propose a novel multicast protocol DSS-CAST for this purpose, which is specialized in streaming data distribution for dynamic scenarios. We compare the performance of DGSVS with other existing protocols and found that DGSVS is most effective in service cost minimization while it is able to achieve competitive QoE performance.