A Systematic Literature Review on Machine Learning and Deep Learning Approaches for Detecting DDoS Attacks in Software-Defined Networking.

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.

A Systematic Literature Review on Machine and Deep Learning Approaches for Detecting Attacks in RPL-Based 6LoWPAN of Internet of Things.

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.

A Secure Pseudonym-Based Conditional Privacy-Preservation Authentication Scheme in Vehicular Ad Hoc Networks.

Existing identity-based schemes utilized in Vehicular Ad hoc Networks (VANETs) rely on roadside units to offer conditional privacy-preservation authentication and are vulnerable to insider attacks. Achieving rapid message signing and verification for authentication is challenging due to complex operations, such as bilinear pairs. This paper proposes a secure pseudonym-based conditional privacy-persevering authentication scheme for communication security in VANETs. The Elliptic Curve Cryptography (ECC) and secure hash cryptographic function were used in the proposed scheme for signing and verifying messages. After a vehicle receives a significant amount of pseudo-IDs and the corresponding signature key from the Trusted Authority (TA), it uses them to sign a message during the broadcasting process. Thus, the proposed scheme requires each vehicle to check all the broadcasting messages received. Besides, in the proposed scheme, the TA can revoke misbehaving vehicles from continuously broadcasting signed messages, thus preventing insider attacks. The security analysis proved that the proposed scheme fulfilled the security requirements, including identity privacy-preservation, message integrity and authenticity, unlinkability, and traceability. The proposed scheme also withstood common security attacks such as man-in-the-middle, impersonation, modification, and replay attacks. Besides, our scheme was resistant against an adaptive chosen-message attack under the random oracle model. Furthermore, our scheme did not employ bilinear pairing operations; therefore, the performance analysis and comparison showed a lower resulting overhead than other identity-based schemes. The computation costs of the message signing, individual signature authentication, and batch signature authentication were reduced by 49%, 33.3%, and 90.2%, respectively.

SE-CPPA: A Secure and Efficient Conditional Privacy-Preserving Authentication Scheme in Vehicular Ad-Hoc Networks.

Communications between nodes in Vehicular Ad-Hoc Networks (VANETs) are inherently vulnerable to security attacks, which may mean disruption to the system. Therefore, the security and privacy issues in VANETs are entitled to be the most important. To address these issues, the existing Conditional Privacy-Preserving Authentication (CPPA) schemes based on either public key infrastructure, group signature, or identity have been proposed. However, an attacker could impersonate an authenticated node in these schemes for broadcasting fake messages. Besides, none of these schemes have satisfactorily addressed the performance efficiency related to signing and verifying safety traffic-related messages. For resisting impersonation attacks and achieving better performance efficiency, a Secure and Efficient Conditional Privacy-Preserving Authentication (SE-CPPA) scheme is proposed in this paper. The proposed SE-CPPA scheme is based on the cryptographic hash function and bilinear pair cryptography for the signing and verifying of messages. Through security analysis and comparison, the proposed SE-CPPA scheme can accomplish security goals in terms of formal and informal analysis. More precisely, to resist impersonation attacks, the true identity of the vehicle stored in the tamper-proof device (TPD) is frequently updated, having a short period of validity. Since the MapToPoint hash function and a large number of cryptography operations are not employed, simulation results show that the proposed SE-CPPA scheme outperforms the existing schemes in terms of computation and communication costs. Finally, the proposed SE-CPPA scheme reduces the computation costs of signing the message and verifying the message by 99.95% and 35.93%, respectively. Meanwhile, the proposed SE-CPPA scheme reduces the communication costs of the message size by 27.3%.