In this Modern era, Software Defined Network (SDN), Network Function Virtualization (NFV), and cloud computing participating of Fifth Generation (5G) network emergence. This paper presents a robust security scheme to provide fortification against major threats along with user privacy in 5G network, two additional entities are introduced. For mobile users, initial authentication is provided at access points by an inventive Highly Secured Authentication and Handover Mechanism (HS-AOHM) scheme which minimizes handover latency without loss of user privacy. Then the authorized user packets are arrived at dispatcher in which a novel Tree Based Switch Assignment (TBSA) algorithm is incorporated. TBSA mitigates the flow table overloading attack by assigning packets to underloaded switches. In controller, DDoS attack is detected with the assist of entropy analysis. Then the suspicious packets are redirected to scrubbing Virtual Network Function (sVNF) in cloud. In sVNF, suspicious packets are classified into normal packets and malicious packets by using Hybrid Fuzzy with Artificial Neural Network (HF-ANN) classifier based on packet features. Normal packets are allowed to access applications whereas malicious packets are dropped at sVNF. Extensive simulation shows security improvement in 5G network in terms of handover latency, holding time, switch failure rate, detection accuracy, and delay.

Lightweight security provisioning is a recent topic in resource-constrained software-defined networking (SDN). Integration between SDN, internet of things (IoT), network function virtualization (NFV) provides massive application services for 5G/B5G communications. Current state-of-the-art security solutions were caused by higher resource consumption, weaker key distribution, and forged credentials. In this paper, we proposed a blockchain-based lightweight security architecture (Bloc-Sec) in SDN/NFV that enabled cloud of IoT networks with 5G/B5G communication. Firstly, we authenticate all IoT devices to the blockchain server using multiple factors Blake-256 hashing algorithm. Secondly, we select the optimal virtual network function (VNF) using the cuttlefish optimization algorithm. Thirdly, blockchain is invoked to keep store the hashed flow rules that are deployed in VNF. Fourthly, the controller is involved in the packet classification by proposing the packet header inspection and packet content inspection using spiking dual fuzzy neural networks. For testing, NS3.26 is implemented and the performance evaluated.

Self-reconfiguration in electrical power grids is a significant tool for their planning and operation during both normal and abnormal conditions. The increasing in employment of Intelligent Electronic Devices (IEDs), as well as the rapid growth of the new communication technologies have increased the application of Feeder Automation (FA) in Distribution Networks (DNs). In a Smart Grid (SG), automation equipment, such as a Smart Breaker (SB), is used. Using either a wired or a wireless network or even a combination of both, communication between the Control Center (CC) and SBs can be made. Nowadays, wireless technology is widely used in the communication of DNs. This may cause several security vulnerabilities in the power system, such as remote attacks, with the goal of cutting off the electrical power provided to significant consumers. Therefore, to preserve the cybersecurity of the system, there is a need for a secure scheme. The available literature investments proposed a heavyweight level in security schemes, while the overhead was not considered. To overcome this drawback, this paper presents an efficient lightweight authentication mechanism with the necessary steps to ensure real-time automatic reconfiguration during a fault. As a first stage, authentication will be made between CC and SB, SB then sends the information about its status. To ensure the integrity of the authentication exchange, a hash function is used, while the symmetric algorithm is used to ensure privacy. The applicability of the suggested scheme has been proved by conducting security performance and analysis. The proposed scheme will be injected on ABB medium voltage breaker with the REF 542plus controller. Therefore, the probable benefit of the suggested scheme is the contribution to provide more flexibility for electrical utilities in terms of reducing the overall computational overhead and withstanding to various types of attacks, while also opening new prospects in FA of SGs.

This paper aimed to identify the various kinds of distributed denial of service attacks (DDoS) attacks, their destructive capabilities, and most of all, how best these issues could be counter attacked and resolved for the benefit of all stakeholders along the cloud continuum, preferably as permanent solutions. A compilation of the various types of DDoS is done, their strike capabilities and most of all, how best cloud computing environment issues could be addressed and resolved for the benefit of all stakeholders along the cloud continuum. The key challenges against effective DDoS defense mechanism are also explored.

False data injection (FDI) attacks are a major security threat to smart grid (SG) communication systems. In FDI attacks, the attacker has the ability of modifying the measurements transmitted by smart grid entities such as smart meters, buses, etc. Many solutions have been proposed to mitigate FDI attacks in the SG. However, most of these solutions rely on centralized state estimation (SE), which is computationally expensive. To engulf this problem in FDI attack detection and to improve security of SGs, this paper proposes novel two-tier secure smart grid (T²S²G) architecture with distributed SE. In T²S²G, measurement collection and SE are involved in first tier while compromised meter detection is involved in second tier. Initially the overall SG system is divided into four sections by the weighted quad tree (WQT) method. Each section is provided with an aggregator, which is responsible to perform SE. Measurements from power grids are collected by remote terminal units (RTUs) and encrypted using a parallel enhanced elliptic curve cryptography (PEECC) algorithm. Then the measurements are transmitted to the corresponding aggregator. Upon collected measurements, aggregator estimates state using the amended particle swarm optimization (APSO) algorithm in a distributed manner. To verify authenticity of aggregators, each aggregator is authenticated by a logical schedule based authentication (LSA) scheme at the control server (CS). In the CS, fuzzy with a neural network (FNN) algorithm is employed for measurements classification. Our proposed T²S²G shows promising results in the following performance metrics: Estimation error, number of protected measurements, detection probability, successful detection rate, and detection delay.

Cloud computing provides scalable, on-demand, and highly available computing services over the Internet to both the public and organizations on a pay-per-use basis. It provides a variety of services such as networking, storage space, and applications. The key issue for cloud computing is ensuring the confidentiality and privacy of cloud resources and data. Enticing the user to purchase cloud services requires their trust which cannot be guaranteed unless the infrastructure is effectively protected because attacks at this level will threaten the whole system. To this end, we propose the Integrated Intrusion Prevention and Detection System (IIPDS) to prevent and detect different types of attacks to the infrastructure level of the cloud system. The proposed design uses Trusted Third Party (TTP) services and SSL/TLS protocols as intrusion prevention methods to secure the communication between the cloud provider and the user. It also uses multiple Intrusion Detection Systems (IDS) distributed over multiple cloud regions. The IDS system is capable of detecting known and unknown attacks using anomaly and rule-based (signature) intrusion detection techniques. The simulation results proved the efficiency of the system in detecting a wide range of attacks with low false positive alerts and low computational overhead.