Wireless Local Area Networks (WLANs) have become an increasingly popular mode of communication and networking, with a wide range of applications in various fields. However, the increasing popularity of WLANs has also led to an increase in security threats, including denial of service (DoS) attacks. In this study, management-frames-based DoS attacks, in which the attacker floods the network with management frames, are particularly concerning as they can cause widespread disruptions in the network. Attacks known as denial of service (DoS) can target wireless LANs. None of the wireless security mechanisms in use today contemplate defence against them. At the MAC layer, there are multiple vulnerabilities that can be exploited to launch DoS attacks. This paper focuses on designing and developing an artificial neural network (NN) scheme for detecting management-frames-based DoS attacks. The proposed scheme aims to effectively detect fake de-authentication/disassociation frames and improve network performance by avoiding communication interruption caused by such attacks. The proposed NN scheme leverages machine learning techniques to analyse patterns and features in the management frames exchanged between wireless devices. By training the NN, the system can learn to accurately detect potential DoS attacks. This approach offers a more sophisticated and effective solution to the problem of DoS attacks in wireless LANs and has the potential to significantly enhance the security and reliability of these networks. According to the experimental results, the proposed technique exhibits higher effectiveness in detection compared to existing methods, as evidenced by a significantly increased true positive rate and a decreased false positive rate.
Network function virtualization (NFV) is a rapidly growing technology that enables the virtualization of traditional network hardware components, offering benefits such as cost reduction, increased flexibility, and efficient resource utilization. Moreover, NFV plays a crucial role in sensor and IoT networks by ensuring optimal resource usage and effective network management. However, adopting NFV in these networks also brings security challenges that must promptly and effectively address. This survey paper focuses on exploring the security challenges associated with NFV. It proposes the utilization of anomaly detection techniques as a means to mitigate the potential risks of cyber attacks. The research evaluates the strengths and weaknesses of various machine learning-based algorithms for detecting network-based anomalies in NFV networks. By providing insights into the most efficient algorithm for timely and effective anomaly detection in NFV networks, this study aims to assist network administrators and security professionals in enhancing the security of NFV deployments, thus safeguarding the integrity and performance of sensors and IoT systems.
The Internet of Things has a bootloader and applications responsible for initializing the device's hardware and loading the operating system or firmware. Ensuring the security of the bootloader is crucial to protect against malicious firmware or software being loaded onto the device. One way to increase the security of the bootloader is to use digital signature verification to ensure that only authorized firmware can be loaded onto the device. Additionally, implementing secure boot processes, such as a chain of trust, can prevent unauthorized access to the device's firmware and protect against tampering during the boot process. This research is based on the firmware bootloader and application dataflow taint analysis and security assessment of IoT devices as the most critical step in ensuring the security and integrity of these devices. This process helps identify vulnerabilities and potential attack vectors that attackers could exploit and provides a foundation for developing effective remediation strategies.
Software-defined networking (SDN) is a networking architecture with improved efficiency achieved by moving networking decisions from the data plane to provide them critically at the control plane. In a traditional SDN, typically, a single controller is used. However, the complexity of modern networks due to their size and high traffic volume with varied quality of service requirements have introduced high control message communications overhead on the controller. Similarly, the solution found using multiple distributed controllers brings forth the 'controller placement problem' (CPP). Incorporating switch roles in the CPP modelling during network partitioning for controller placement has not been adequately considered by any existing CPP techniques. This article proposes the controller placement algorithm with network partition based on critical switch awareness (CPCSA). CPCSA identifies critical switch in the software defined wide area network (SDWAN) and then partition the network based on the criticality. Subsequently, a controller is assigned to each partition to improve control messages communication overhead, loss, throughput, and flow setup delay. The CPSCSA experimented with real network topologies obtained from the Internet Topology Zoo. Results show that CPCSA has achieved an aggregate reduction in the controller's overhead by 73%, loss by 51%, and latency by 16% while improving throughput by 16% compared to the benchmark algorithms.
The proliferation of the Internet of Things (IoT) devices has led to a surge in Internet traffic characterized by variabilities in Quality of Service (QoS) demands. Managing these devices and traffic effectively proves challenging, particularly within conventional IoT network architectures lacking centralized management. However, the advent of Software-Defined Networking (SDN) presents intriguing opportunities for network management, capable of addressing challenges in traditional IoT architectures. SDN's ability to provide centralized network management through a programmable controller, separate from data forwarding elements, has led researchers to incorporate SDN features with IoT (SDIoT) and Wireless Sensor Networks (SDWSN) ecosystems. However, despite the SDN support, these networks encounter challenges related to load-imbalance routing issues, as the SDN controller may be constrained while certain access points serving end users become overloaded. In response to these challenges, various load-balancing routing solutions have been proposed, each with distinct objectives. However, a comprehensive study that classifies and analyzes these solutions based on their weaknesses and postmortem challenges is currently lacking. This paper fills this gap by providing an in-depth classification of existing solutions. The study categorizes the problems addressed by different schemes and summarizes their findings. Furthermore, it discusses the shortcomings of current studies, and postmortem challenges associated with integrating SDN with IoT, and suggests future research directions.