Distributed Denial of Service (DDoS) attack is known to be one of the most lethal attacks in traditional network architecture. In this attack, the attacker uses botnets to overwhelm network resources. Botnets can be randomly compromised computers or IoT devices that are used to generate excessive traffic towards the victim, and as a result, legitimate users cannot access the services. In this research, software-defined networking (SDN) has been suggested as a solution to fight DDoS attacks. SDN uses the idea of centralized control and segregation of the data plane from the control plane. SDN is more flexible, and policy implementation on the centralized controller is easy. SDN is now being widely used in modern network paradigms because it has enhanced security. In this work, an entropy-based statistical approach has been suggested to detect and mitigate TCP SYN flood DDoS attacks. The proposed algorithm uses a three-phased detection scheme to minimize the false-positive rate. Entropy, standard deviation, and weighted moving average have been used for intrusion detection. Multiple experiments were performed, and the results show that the suggested approach is more reliable and lightweight and has a minimal false-positive rate.

In vehicular ad hoc networks (VANETs), helpful information dissemination establishes the foundation of communication. One of the significant difficulties in developing a successful dissemination system for VANETs is avoiding traffic fatalities. Another essential success metric is the transfer of reliable and secure warning messages through the shortest path, particularly on highways with high mobility. Clustering vehicles is a general solution to these challenges, as it allows warning alerts to be re-broadcast to nearby clusters by fewer vehicles. Hence, trustworthy cluster head (CH) selections are critical to decreasing the number of retransmissions. In this context, we suggest a clustering technique called Optimal Path Routing Protocol for Warning Messages (OPRP) for dissemination in highway VANETs. OPRP relies on mobility measured to reinforce cluster creation, evade transmission overhead, and sustain message authenticity in a high mobility environment. Moreover, we consider communication between the cluster heads to reduce the number of transmissions. Furthermore, the cluster head is chosen using the median technique based on an odd or even number of vehicles for a stable and lengthy cluster life. By altering traffic densities and speeds, OPRP is compared with prominent schemes. Simulation results revealed that OPRP offers enhanced throughput, end-to-end delay, maximizing packet delivery ratio, and message validity.

Internet of Things (IoT) is a promising technology enabling physical devices like cameras, home appliances, and other devices to communicate and interoperate with each other. The next wave transforms our homes, society, enterprises, and cities with the massive presence of IoT devices. The devices in the Internet of Things (IoT) may exchange sensitive data, and an important issue for any organization is to get the data secured and protected. The preliminary requirement for this is a mechanism detecting and reporting anomalies automatically to some central controller. Therefore, this mechanism should be able to classify legit IoT devices from unauthorized ones. Malicious IoT devices, non‐IoT devices, and other types of man‐in‐the‐middle traffic sources must be quarantined for noncompliance. This helps formulate administrative policies and regulate/police traffic in the network for better QoS management. This work proposed a framework‐based hierarchical deep neural network (HDNNs) to distinguish IoT devices from non‐IoT devices using a feature set of IoT‐specific traffic. A system has been designed based on HDNN that classifies IoT devices to their specific categories and identifies new entrants with reasonable accuracy. The results show that HDNN can distinguish IoT and non‐IoT devices with higher accuracy and as well as classify IoT devices into the respective classes with the required accuracy.

Advancement in technology has led to innovation in equipment, and the number of devices is increasing every day. Industries are introducing new devices every day and predicting 50 billion connected devices by 2022. These devices are deployed through the Internet, called the Internet of Things (IoT). Applications of IoT devices are weather prediction, monitoring surgery in hospitals, identification of animals using biochips, providing tracking connectivity in automobiles, smart home appliances, etc. IoT devices have limitations related to security at both the software and hardware ends. Secure user interfaces can overcome software‐level limitations like front‐end‐user interfaces are accessed easily through public and private networks. The front‐end interfaces are connected to the localized storage to contain data produced by the IoT devices. Localized storage deployed in a closed environment connected to IoT devices is more efficient than online servers from a security perspective. Blockchain has emerged as a technology or technique with capabilities to achieve secure administrational authentication and accessibility to IoT devices and their computationally produced data in a decentralized way with high reliability, interrogation, and resilience. In this paper, we propose device, end‐user, and transactional authentication techniques using blockchain‐embedded algorithms. The localized server interacts with the user interface to authenticate IoT devices, end‐users, and their access to IoT devices. The localized server provides efficiency by reducing the load on the IoT devices by carrying out end‐user heavy computational data, including end‐user, IoT device authentication, and communicational transactions. Authentication data are placed on the public ledger in block form, distributed over the system nodes through blockchain algorithms.

This paper concerns Quality of Service (QoS) provisioning in Optical Packet Switching (OPS) networks. We address the topic by proposing Dynamic Wavelength Grouping (DWG) for OPS, in this technique, available wavelengths are dynamically partitioned and allocated at each network link to particular classes of service. DWG adapts to traffic fluctuations by tracking the load status of every class of traffic and then schedules optical packets over group of wavelengths assigned for each class of service. Performance is examined using an own developed simulator. This simulator models various aspects of OPS in far detail. Two network topologies are studied under various parameter settings and traffic scenarios. DWG clearly outperforms its earlier static counterpart (SWG). It appears as flexible approach, efficiently functional even when the share of the high-priority load is temporarily low. Consequently, it minimizes the total blocking probability while preserving the desired service differentiation. We report also significant improvements in the network throughput.