Software-Defined Network (SDN) has become a promising network architecture in current days that provide network operators more control over the network infrastructure. The controller, also called as the operating system of the SDN, is responsible for running various network applications and maintaining several network services and functionalities. Despite all its capabilities, the introduction of various architectural entities of SDN poses many security threats and potential targets. Distributed Denial of Services (DDoS) is a rapidly growing attack that poses a tremendous threat to the Internet. As the control layer is vulnerable to DDoS attacks, the goal of this paper is to detect the attack traffic, by taking the centralized control aspect of SDN. Nowadays, in the field of SDN, various machine learning (ML) techniques are being deployed for detecting malicious traffic. Despite these works, choosing the relevant features and accurate classifiers for attack detection is an open question. For better detection accuracy, in this work, Support Vector Machine (SVM) is assisted by kernel principal component analysis (KPCA) with genetic algorithm (GA). In the proposed SVM model, KPCA is used for reducing the dimension of feature vectors, and GA is used for optimizing different SVM parameters. In order to reduce the noise caused by feature differences, an improved kernel function (N-RBF) is proposed. The experimental results show that compared to single-SVM, the proposed model achieves more accurate classification with better generalization. Moreover, the proposed model can be embedded within the controller to define security rules to prevent possible attacks by the attackers.

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In recent years, the Internet of Things (IoT) has evolved as a research field that transforms human lifestyle from traditional to sophisticated. In IoT, the network plays a crucial role in collecting data from sensors and moving to the sink. Increasing the network lifetime is a challenging task in IoT, which is connected to devices that are limited by resource. Clustering is one of the effective methods of increasing the network lifetime. However, improper cluster head (CH) selection easily drains the energy early in network nodes. With the aim to overcome the issue, this paper proposes the Type-2 Fuzzy Logic-based Particle Swarm Optimization (T2FL-PSO) algorithm to select the optimal CH to extend the network lifetime. The T2FL is highly useful in providing the accurate solution in uncertain network environments. Hence, T2FL is applied on the network parameters, residual energy, and the distance between sensor node and base station to determine the fitness value. Later, virtual clusters are formed on the basis of distance between sensor node and CH and between node and base station. To validate the performance of the proposed T2FL-PSO algorithm, extensive simulations are carried out using MATLAB 2019a. Furthermore, the proposed T2FL-PSO algorithm is compared with Particle Swarm Optimization Clustering (PSO-C) and Particle Swarm Optimization Wang Zhang (PSO-WZ). The result confirms that the proposed T2FL-PSO increases the network lifetime by 10%-15% and the packet transmission ratio by 10%. Compared with similar algorithms, the proposed T2FL-PSO also causes a higher increase of network lifetime.

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