Vehicular ad hoc networks (VANETs) have become increasingly common in recent years due to their critical role in the field of smart transportation by supporting Vehicle-to-Vehicle and Vehicle-to-Infrastructure communication. The security and privacy of VANET are of the utmost importance due to the use of an open wireless communication medium where messages are exchanged in plain text, something which allows attackers to intercept, tamper, replay, and delete them. Hence, there is a high probability that the safety of a VANET-based smart transportation system could be compromised. Nowadays, securing and safeguarding the exchange of messages in VANETs is the focus of many security research teams, as reflected by the number of authentication and privacy schemes that have been proposed. However, these schemes have not fulfilled all aspects of the security and privacy requirements. The present paper is an effort to provide a thorough background on VANETs and their components; various types of attacks on them; and all the security and privacy requirements for authentication and privacy schemes for VANETs. This paper is among the first to provide a comprehensive survey of the existing authentication and privacy schemes and compare them based on all security and privacy requirements, computational and communicational overheads, and the level of resistance to different types of attacks. It also provides a qualitative comparison with the existing surveys. This paper could serve as a guide and reference in the design and development of any new security and privacy techniques for VANETs.

The proliferation of Internet of Things (IoT) devices has brought about an increased threat of botnet attacks, necessitating robust security measures. In response to this evolving landscape, deep learning (DL)-based intrusion detection systems (IDS) have emerged as a promising approach for detecting and mitigating botnet activities in IoT environments. Therefore, this paper thoroughly reviews existing literature on botnet detection in the IoT using DL-based IDS. It consolidates and analyzes a wide range of research papers, highlighting key findings, methodologies, advancements, shortcomings, and challenges in the field. Additionally, we performed a qualitative comparison with existing surveys using author-defined metrics to underscore the uniqueness of this survey. We also discuss challenges, limitations, and future research directions, emphasizing the distinctive contributions of our review. Ultimately, this survey serves as a guideline for future researchers, contributing to the advancement of botnet detection methods in IoT environments and enhancing security against botnet threats.

Software-defined networking (SDN) is a revolutionary innovation in network technology with many desirable features, including flexibility and manageability. Despite those advantages, SDN is vulnerable to distributed denial of service (DDoS), which constitutes a significant threat due to its impact on the SDN network. Despite many security approaches to detect DDoS attacks, it remains an open research challenge. Therefore, this study presents a systematic literature review (SLR) to systematically investigate and critically analyze the existing DDoS attack approaches based on machine learning (ML), deep learning (DL), or hybrid approaches published between 2014 and 2022. We followed a predefined SLR protocol in two stages on eight online databases to comprehensively cover relevant studies. The two stages involve automatic and manual searching, resulting in 70 studies being identified as definitive primary studies. The trend indicates that the number of studies on SDN DDoS attacks has increased dramatically in the last few years. The analysis showed that the existing detection approaches primarily utilize ensemble, hybrid, and single ML-DL. Private synthetic datasets, followed by unrealistic datasets, are the most frequently used to evaluate those approaches. In addition, the review argues that the limited literature studies demand additional focus on resolving the remaining challenges and open issues stated in this SLR.

The wide proliferation of telecommunication technologies in the last decade gives rise to the number of more sophisticated security threats. Software-Defined Networking (SDN) is a new networking architecture that isolates the network control plane from the data plane that incidentally provides better features and functionalities to detect and deal with those security threats. Its elastic programmable feature permits efficient network management and provides network operators with the flexibility to monitor and fine-tune their network. However, the new technology also created many new security concerns, and the threat of Distributed Denial of Service (DDoS) attack is one of the major concerns. This paper presents a comprehensive review of state-of-the-art techniques to detect DDoS attacks on SDN controller. It first describes the SDN technology and then elaborates on the mechanism of DDoS attacks on SDN. Additionally, this paper also describes all major DDoS detection techniques and classifies them at a very high level according to the techniques or methods used. The current survey is qualitatively compared with the existing surveys using various author-defined metrics. Finally, this paper provides a guideline for future research related to detection techniques of DDoS against the SDN controller.

The IETF Routing Over Low power and Lossy network (ROLL) working group defined IPv6 Routing Protocol for Low Power and Lossy Network (RPL) to facilitate efficient routing in IPv6 over Low-Power Wireless Personal Area Networks (6LoWPAN). Limited resources of 6LoWPAN nodes make it challenging to secure the environment, leaving it vulnerable to threats and security attacks. Machine Learning (ML) and Deep Learning (DL) approaches have shown promise as effective and efficient mechanisms for detecting anomalous behaviors in RPL-based 6LoWPAN. Therefore, this paper systematically reviews and critically analyzes the research landscape on ML, DL, and combined ML-DL approaches applied to detect attacks in RPL networks. In addition, this study examined existing datasets designed explicitly for the RPL network. This work collects relevant studies from five major databases: Google Scholar, Springer Link, Scopus, Science Direct, and IEEE Xplore^® digital library. Furthermore, 15,543 studies, retrieved from January 2016 to mid-2021, were refined according to the assigned inclusion criteria and designed research questions resulting in 49 studies. Finally, a conclusive discussion highlights the issues and challenges in the existing studies and proposes several future research directions.

Vehicular Ad hoc Networks (VANET) broadcast messages regarding road and environmental conditions. Due to its design, VANET inadvertently introduced security and privacy issues. Many researchers have suggested various approaches to address these shortcomings as the deployment of VANET becomes more widespread. Nevertheless, these solutions could not address all the security and privacy shortcomings in VANET. Furthermore, the proposed approaches incur high costs in terms of computation due to the complexity involved in doing so sequentially. One of the significant approaches used in VANET security and privacy mitigation is identity-based schemes. This paper provides a comprehensive survey on VANETs and the entities involved, attack models, and an analysis of the security and privacy requirements for identity-based security and privacy schemes for VANETs.