Software-Defined Networking (SDN) is a groundbreaking technology that has transformed network management significantly. By integrating data and control, SDN offers unparalleled flexibility and responsiveness, thereby overcoming the limitations of conventional network architectures. However, a centralized controller, which is a hallmark of SDN, is a double-edged security sword that offers easy control. This also becomes a dangerous point of failure for the entire network. To the best of our knowledge, this is the first comprehensive study to explore traditional-based, artificial intelligence (AI)-based, and moving target defense (MTD) approaches to securing SDN. The study begins with a survey of traditional security solutions for SDN, encompassing authentication, authorization, encryption, security protocols, firewalls, and flow verification, by addressing security threats and vulnerabilities in both data and control planes. The study then investigates the application of AI-based security solutions in an SDN environment, focusing on how Machine Learning (ML) and Deep Learning (DL) techniques are leveraged to address advanced security threats. Additionally, the survey examines MTD mechanisms within data and control plane security. Several in-depth techniques, including the randomization of Internet Protocol (IP) and Media Access Control (MAC) addresses, port numbers, and flow tables, and delving into the relationship between security threats, MTD strategies, and the specific controllers employed in experimental implementations. We utilized the widely recognized STRIDE cybersecurity framework to systematically identify and evaluate the potential threats to SDN security. Our analysis resulted in a comprehensive list of security challenges, and we propose future research directions aimed at addressing emerging threats in both the data and control planes.

Software-Defined Networking (SDN) is a promising solution for large-scale network management that offers extensive opportunities for optimization. However, the centralized control inherent in SDN also exposes networks to security threats, notably Distributed Denial of Service (DDoS) attacks. To address these challenges, machine learning (ML) techniques have emerged as potent tools for anomaly detection and mitigation. This paper proposes a novel approach for traffic classification within SDN environments that distinguishes between benign and malicious traffic using supervised ML techniques. This study introduces a unique dataset tailored for DDoS attack detection, overcoming the limitations of existing datasets, such as unrealistic topologies and lack of public availability. Benchmarking against the CICDDoS2019 dataset validated the efficacy and relevance of the custom dataset. This research has significant implications for real-world applications, offering improved capabilities for detecting and mitigating DDoS attacks in SDN infrastructure. Experimental results demonstrated the effectiveness of the proposed random forest model, achieving a remarkable accuracy of 98.97% and a minimal False Alarm Rate (FAR) of 0.023. These findings underscore the potential of ML-based approaches in enhancing network security and resilience against DDoS attacks in SDN environments, paving the way for future advancements in network-defense strategies.

Software-Defined Networking (SDN) offers significant advantages for modern networks, including flexibility, centralized control, and reduced dependency on vendor-specific hardware. However, these benefits introduce security vulnerabilities, particularly from Distributed Denial-of-Service (DDoS) attacks, which represent some of the most disruptive threats to SDN environments. A review of the literature shows that while various techniques have been proposed to counteract DDoS threats, many studies have focused on single detection methods, with only a few utilizing multiple approaches. This fragmented focus limits a comprehensive approach to addressing DDoS threats across the SDN layers. To bridge this gap, this paper presents the first comprehensive review of DDoS anomaly detection in SDN, examining over 165 primary research articles published between 2020 and 2024. A novel taxonomy of DDoS attacks is introduced, categorizing them by distinct characteristics, and mapping each attack type to relevant detection methods within specific SDN layers. The survey provides a layer-by-layer analysis of DDoS detection techniques, covering the application, control, and infrastructure layers, and offers a structured overview that clarifies the applicability and effectiveness of each method. The paper concludes by synthesizing key findings, identifying unresolved challenges, and outlining future research directions to advance DDoS detection mechanisms in SDN. This roadmap is designed to guide researchers in addressing security vulnerabilities and enhancing SDN resilience against evolving DDoS threats.

With the increasing interconnectivity of devices, the Internet of Things (IoT) has revolutionized the industry and daily life. However, the proliferation of IoT devices has also increased security risks, necessitating robust protection mechanisms for sensitive data and critical infrastructure. The Advanced Encryption Standard (AES) remains the benchmark for securing IoT systems while balancing low power consumption, minimal area usage, and moderate throughput with high security. This paper offers a comprehensive review of the latest lightweight AES architectural designs, including optimizations to the Substitution Box (S-Box), Sub-Bytes, Shift Rows, Mix Columns, and Add Round Key steps, assessing their impact on gate count, area, maximum frequency, power consumption, and throughput in field programable gate arrays (FPGA) and Application-specific integrated circuit (ASIC) implementations. In addition, this study addresses vulnerabilities in lightweight AES cryptographic hardware to side-channel attacks (SCA), specifically focusing on Differential Fault Analysis (DFA). Furthermore, the analysis explored fault scenarios, rounds, and injection positions to evaluate fault severity. Countermeasures to DFA are reviewed, emphasizing fault detection methods, error detection levels, protection positions, and associated design overheads such as area, frequency, and throughput penalties, with special consideration for resource-constrained IoT devices. This study identifies critical gaps in lightweight AES and security challenges while discussing countermeasures that balance security with design efficiency. Finally, this study provides valuable insights for finding research directions to strengthen the robustness of AES in lightweight IoT environments.

Software-Defined Networking (SDN) offers flexibility, but introduces security challenges, particularly in detecting Distributed Denial of Service (DDoS) attacks. This study enhances DDoS anomaly detection in SDNs by integrating machine-learning (ML) algorithms. The efficacy of three models, namely, Support Vector Machine (SVM), Decision Tree (DT), and Knearest neighbor (KNN), was evaluated for classifying network traffic and predicting anomalies. A dataset of legitimate and illegitimate traffic patterns was created using Mininet, which is an SDN emulation tool. The models were trained on this dataset, leading to an improved accuracy. A comparative analysis reveals the strengths and weaknesses of each algorithm. The SVM model achieved 97.31% accuracy with a precision of 0.91, recall of 0.89, and F1-Score of 0.95. The DT model attained 99.28% accuracy with a precision of 0.97, recall of 0.96, and F1-Score of 0.97. The KNN model demonstrated 96.26% accuracy with a precision of 0.95, recall of 0.95, and F1-Score of 0.95. These findings highlight the effectiveness of ML for enhancing DDoS detection and improving SDN security.

Software-defined networking (SDN) has revolutionized network management by centralizing control through software, thereby enabling dynamic traffic adjustments that are independent of the data plane. However, this innovation introduces significant security vulnerabilities because the existing solutions are largely adaptations of traditional methods and fail to address the unique challenges of SDN environments. To address this issue, this study proposes a machine-learning (ML)-based intrusion detection framework tailored specifically for SDN. In particular, the framework utilizes a hybrid model that combines a Support Vector Machine (SVM) and Random Forest (RF) classifiers (HSF), which significantly improves intrusion detection accuracy. Specifically, the proposed solution is structured as a three-layer protection mechanism. First, the Data Plane Monitoring layer examines features, such as packet count and byte count, to detect anomalies. Second, the Control Plane Monitoring layer evaluates attributes such as the source IP, destination IP, and protocols to identify suspicious activity. Finally, the Detection Layer leverages the hybrid ML approach to further strengthen detection capabilities and ensure timely responses. Importantly, the experimental results reveal that the HSF technique achieves an anomaly detection rate exceeding 99% across both data and control planes. This highlights its efficacy in securing the next-generation SDN networks.