Software Defined Networks (SDN) offer dynamic reconfigurability and scalability, revolutionizing traditional networking. However, countering Distributed Denial of Service (DDoS) attacks remains a formidable challenge for both traditional and SDN-based networks. The integration of Machine Learning (ML) into SDN holds promise for addressing these threats. While recent research demonstrates ML’s accuracy in distinguishing legitimate from malicious traffic, it faces difficulties in handling emerging, low-rate, and zero-day DDoS attacks due to limited feature scope for training. The ever-evolving DDoS landscape, driven by new protocols, necessitates continuous ML model retraining. In response to these challenges, we propose an ensemble online machine-learning model designed to enhance DDoS detection and mitigation. This approach utilizes online learning to adapt the model with expected attack patterns. The model is trained and evaluated using SDN simulation (Mininet and Ryu). Its dynamic feature selection capability overcomes conventional limitations, resulting in improved accuracy across diverse DDoS attack types. Experimental results demonstrate a remarkable 99.2% detection rate, outperforming comparable models on our custom dataset as well as various benchmark datasets, including CICDDoS2019, InSDN, and slow-read-DDoS. Moreover, the proposed model undergoes comparison with industry-standard commercial solutions. This work establishes a strong foundation for proactive DDoS threat identification and mitigation in SDN environments, reinforcing network security against evolving cyber risks.

Software defined networking (SDN) is an emerging network paradigm that decouples the control plane from the data plane. The data plane is composed of forwarding elements called switches and the control plane is composed of controllers. SDN is gaining popularity from industry and academics due to its advantages such as centralized, flexible, and programmable network management. The increasing number of traffics due to the proliferation of the Internet of Thing (IoT) devices may result in two problems: (1) increased processing load of the controller, and (2) insufficient space in the switches’ flow table to accommodate the flow entries. These problems may cause undesired network behavior and unstable network performance, especially in large-scale networks. Many solutions have been proposed to improve the management of the flow table, reducing controller processing load, and mitigating security threats and vulnerabilities on the controllers and switches. This paper provides comprehensive surveys of existing schemes to ensure SDN meets the quality of service (QoS) demands of various applications and cloud services. Finally, potential future research directions are identified and discussed such as management of flow table using machine learning.

Software-defined networking (SDN) is a new networking paradigm that provides centralized control, programmability, and a global view of topology in the controller. SDN is becoming more popular due to its high audibility, which also raises security and privacy concerns. SDN must be outfitted with the best security scheme to counter the evolving security attacks. A Distributed Denial-of-Service (DDoS) attack is a network attack that floods network links with illegitimate data using high-rate packet transmission. Illegitimate data traffic can overload network links, causing legitimate data to be dropped and network services to be unavailable. Low-rate Distributed Denial-of-Service (LDDoS) is a recent evolution of DDoS attack that has been emerged as one of the most serious vulnerabilities for the Internet, cloud computing platforms, the Internet of Things (IoT), and large data centers. Moreover, LDDoS attacks are more challenging to detect because this attack sends a large amount of illegitimate data that are disguised as legitimate traffic. Thus, traditional security mechanisms such as symmetric/asymmetric detection schemes that have been proposed to protect SDN from DDoS attacks may not be suitable or inefficient for detecting LDDoS attacks. Therefore, more research studies are needed in this domain. There are several survey papers addressing the detection mechanisms of DDoS attacks in SDN, but these studies have focused mainly on high-rate DDoS attacks. Alternatively, in this paper, we present an extensive survey of different detection mechanisms proposed to protect the SDN from LDDoS attacks using machine learning approaches. Our survey describes vulnerability issues in all layers of the SDN architecture that LDDoS attacks can exploit. Current challenges and future directions are also discussed. The survey can be used by researchers to explore and develop innovative and efficient techniques to enhance SDN’s protection against LDDoS attacks.

Internet of Things is emerging as an incredible future technology to improve the existing lifestyle from the research community, industry, and the public sector. The main intention of IoT is to create an efficient, interactive, and autonomous infrastructure for a safer and healthier world. Moreover, it grows faster day-to-day with the support of many other technologies, i.e. Cloud computing, Blockchain, Wireless Body Sensor Networks, Nanotechnology, and Artificial Intelligence for smart applications, including healthcare, environment, automotive industries, transportation, agriculture, etc. Nevertheless, managing big data is one of the challenging tasks due to the increased number of devices leading to various serious issues like security, privacy, accuracy, latency, scheduling, etc. Further, specific infrastructures with remarkable techniques are required to analyze the bulk of raw data to progress the quality of life and allow timely intervention through various capabilities, i.e., data capture, unique identification, actuation, communication, data mining, etc. In past literature, numerous reviews/surveys are presented that explore the technologies mentioned above as standalone and application specific. However, this paper aims to integrate all the mentioned technologies and deliver a clear vision to future researchers (newcomers) as a kick-start article to boost up and understand the status of the existing research through a comprehensive review of the Internet of Things and its evolution in wireless telecommunications from a general perspective. The most significant challenges and issues are highlighted to research further in these evolving domains.

With the rapid development of Internet of Things (IoT) technology, Terminal Devices (TDs) are more inclined to offload computing tasks to higher-performance computing servers, thereby solving the problems of insufficient computing capacity and rapid battery consumption of TD. The emergence of Multi-access Edge Computing (MEC) technology provides new opportunities for IoT task offloading. It allows TDs to access computing networks through multiple communication technologies and supports more mobility of terminal devices. Review studies on IoT task offloading and MEC have been extensive, but none of them focus on IoT task offloading in MEC. To fill this gap, this paper provides a comprehensive and in-depth understanding of the algorithms and mechanisms of multiple IoT task offloading in the MEC network. For each paper, the main problems solved by the mechanism, technical classification, evaluation methods, and supported parameters are extracted and analyzed. Furthermore, shortcomings of current research and future research trends are discussed. This review will help potential and new researchers quickly understand the panorama of IoT task offloading approaches in MEC and find appropriate research paths.

Software Defined Networks (SDN) is a new network paradigm that emerged to offer better network management through the separation of network control logic and data forwarding element. This separation speed up network innovation without the need to rely on the vendor-proprietary interface for network element configuration to forward packets. However, SDN is flow driven network, for each arrived flow, a feasible path is computed to forward the flow to its destination. Afterward, the SDN control logic process the corresponding routing and instruct the set of data forwarding elements to install them on their Flowtable to guide the routing process. Unfortunately, the network changes more frequently in dynamic large-scale networks and the Flowtable is a constraint with limited space. These challenges require the SDN controller to compute paths more often which may also need a large number of flow routing rules placement. In addition, the frequency of communication link failures increases lately. The successful deployment of SDN heavily depends on how it satisfies the reliability requirement with uninterrupted services. Several studies were conducted to compute the optimal path for data forward to meet their Quality of Service demand. Other studies focus on reducing the frequency of link failure. Some studies were conducted to manage the constraint Flowtable resources. This survey focuses on Routing rules placement, unoptimized routing, link, and switch load balancing, failure detection, and recovery. The paper extensively discusses each issue and analyzes the weakness of the current solutions. Finally, it highlights potential challenges that need future research attention.