Computer networks are subject to an unprecedented number and variety of attack, the majority of which are distributed denial of service (DDoS). The nature and mechanisms employed in these DDoS attacks continually change, creating a significant challenge for detection and management. To address this evolving nature of attacks, approaches are required that can effectively detect and mitigate emerging attacks. In this paper, we provide a mechanism that not only detects the presence of a DDoS attacks but also identifies the route of attack and commences a process of mitigation at the initial stage of identification. The proposed research involves an optimized SVM classification algorithm integrated with SNORT IPS to provide prevention mechanisms for the entire network when subject to DDoS attack. The proposed IPS method allows traffic identified as legitimate to pass through the network, whereas suspect traffic is flagged and has to go through an identification system. We present the algorithm with experimental results that show better performance than simple Snort IPS, Probabilistic Neural Network (PNN), Back Propagation (BP), Chi-square, and PSO-SVM in terms of accuracy, exposure and specificity. These results show that the average accuracy rate of our method is 97 percent.

Traffic accidents have become an important problem for governments, researchers and vehicle manufacturers over the last few decades. However, accidents are unfortunate and frequently occur on the road and cause death, damage to infrastructure, and health injuries. Therefore, there is a need to develop a protocol to avoid or prevent traffic accidents at the extreme level in order to reduce human loss. The aim of this research is to develop a new protocol, named as the Traffic Accidents Reduction Strategy (TARS), for Vehicular Ad-hoc NETworks (VANETs) to minimize the number of road accidents, decrease the death rate caused by road accidents, and for the successful deployment of the Intelligent Transportation System (ITS). We have run multiple simulations and the results showed that our proposed scheme has outperformed DBSR and POVRP routing protocols in terms of the Message Delivery Ratio (MDR), Message Loss Ratio (MLR), Average Delay, and Basic Safety Message.

Traffic accidents have become a major issue for researchers, academia, government and vehicle manufacturers over the last few years. Many accidents and emergency situations frequently occur on the road. Unfortunately, accidents lead to health injuries, destruction of some infrastructure, bad traffic flow, and more importantly these events cause deaths of hundreds of thousands of people due to not getting treatment in time. Thus, we need to develop an efficient and smart emergency system to ensure the timely arrival of an ambulance service to the place of the accident in order to provide timely medical help to those injured. In addition, we also need to communicate promptly with other entities such as hospitals so that they can make appropriate arrangements and provide timely medical information to emergency personnel on the scene including alerting those related to the injured person(s). In this paper, we have developed an intelligent protocol that uses connected and autonomous vehicles' scenarios in Intelligent Transportation System (ITS) so that prompt emergency services can be provided to reduce the death rate caused. The proposed protocol smartly connects with all the relevant entitles during the emergency while maintaining a smooth traffic flow for the arrival of the ambulance service. Moreover, our protocol also mitigates the broadcasting of messages circulating over the network for delay sensitive tasks. The evaluation results, based on the performance metrics such as channel collision, average packet delay, packet loss, and routing-overhead demonstrate that our proposed protocol outperforms previously proposed protocols such as Emergency Message Dissemination for Vehicular (EMDV), Contention Based Broadcasting (CBB), and Particle Swarm Optimization Contention-based Broadcast (PCBB) protocols. Finally, we discuss several issues and challenges that need to be addressed in the network in order to achieve more a reliable, efficient, connected, and autonomous vehicular network.

Emergency never runs with earlier intimations and indications. In the real world and practical life, detecting and perceiving such emergencies and reporting them are a genuine test and tough challenge. Smartphones Systems for Emergency Management (SPSEM) provide details of existing emergency applications and provide a new direction to overcome the traditional problem having manual intercession and reporting emergencies. In this paper, we provide a comprehensive overview of SPSEM. We elaborate how embedded sensors automate the procedure of emergency crisis detection and responding. Furthermore, we critically evaluate the operations, benefits, limitations, emergency applications and responsiveness in any emergency crisis of different approaches. We provide an easy and concise view of the underlying model adapted by each SPSEM approach. In last, we estimate the future utility and provide an insight of upcoming trends in SPSEM

Vehicular Content-Centric Networks (VCCN) has been proposed and emerged as an upcoming Internet technology for the Vehicular Ad-hoc Networks (VANETs). However, VCCN architecture faces many challenges including Consumer/Provider mobility, Flooding, Interest/Data packet forwarding, Forwarding selection and Congestion control. Consumer vehicle broadcasts (i.e., requests) the interest packet. Relay nodes vehicles receiving this interest packet and performs search (Content Storage, Pending Interest Table and Forward Interest Base) operations on it and then rebroadcasts the interest message in case of no content match. These search (Content Storage, Pending Interest Table, and Forward Interest Base) operations cause delay and duplication's of interest packets. In our paper, we propose a routing protocol scheme named Intersection Based Forwarder Selection (IBFS) to mitigate the interest broadcast storm and disconnected link within the network. In IBFS, each vehicle shares its trajectory information with its neighboring vehicles. All vehicles store/save trajectory information in its Neighbor Table (NT), which helps to select the best interest relay/forwarder vehicle on the basis of matching trajectory information with the consumer vehicle. We have also considered the other metrics i.e. velocity and hop count. IBFS protocol overcomes the end-to-end delay (low latency), ISD (Interest Satisfaction Delay), communication overheads and minimum number of interest forwarded with the reliable interest and data packet transmission.