The software-defined networking (SDN) paradigm has simplified the management of computer networks by decoupling data and control planes. Moreover, the separation of the data and control planes has transitioned network complexity from traditional devices to controllers; therefore, controllers have become indispensable entities in SDN. Controllers have multiple features and direct the network from a central point and respond to updates to topological changes. However, the supportive capability of these features is strong in one controller but weak in another. Due to several controllers and each controller having a set of features, selecting an optimal SDN controller can be considered to be a multi-criteria decision-making (MCDM) problem. Herein, a two-step approach is proposed for SDN controller selection. First, the controllers are ranked with analytical network process (ANP) according to their qualitative features which influence the performance of these controllers and then a performance comparison is performed to check for the QoS improvement. The controller with a high-weight value from the feature-based comparison is quantitatively analysed by experimental analysis. The main contribution of this paper is checking the applicability of the ANP for controller selection in SDN considering its features and performance analysis in real-world Internet and Brite topologies. The simulation results show that the controller computed through the proposed approach outperforms the controller selected with existing approaches. The selection of an optimum controller with ANP results in a reduction of topology discovery time and delay in the normal and traffic load scenario. Similarly, an increase in throughput with a reasonable utilization of the central processing unit (CPU) is observed for the proposed controller.

Software Defined Networking (SDN) has revolutionized the networking industry and also tactical communication due to its flexibility, innovation, and ease of programmability. However, modeling, evaluation, and testing of SDN based applications present several challenges. Mininet simulator has been developed for the study of SDN and used for SDN simulation in general. Although it is possible to prototype virtual SDN networks fast with Mininet, it is not for stress testing to validate the stability and reliability and simulation suitable for the real environment. The state-of-the-art network simulation tool OPNET recently OPNET has introduced the SDN Model, which can be used with a system in the loop (SITL). However, there is no research published yet which shows the use of OPNET for SDN. This paper rigorously compares the OPNET and Mininet for SDN experiments. we compare end-to-end delay, jitter, and RTT between controllers and switches. As a result, the Mininet environment cannot apply the network load, so it is desirable to use OPNET for simulation of the real environment or for the stability and reliability test.

Although network address translation (NAT) provides various advantages, it may cause potential threats to network operations. For network administrators to operate networks effectively and securely, it may be necessary to verify whether an assigned IP address is using NAT or not. In this paper, we propose a supervised learning-based active NAT device (NATD) identification using port response patterns. The proposed model utilizes the asymmetric port response patterns between NATD and non-NATD. In addition, to reduce the time and to solve the security issue that supervised learning approaches exhibit, we propose a fast and stealthy NATD identification method. The proposed method can perform the identification remotely, unlike conventional methods that should operate in the same network as the targets. The experimental results demonstrate that the proposed method is effective, exhibiting a F1 score of over 90%. With the efficient features of the proposed methods, we recommend some practical use cases that can contribute to managing networks securely and effectively.

The growing complexity and volume of modern software have led to an increase in source code vulnerabilities, posing significant security risks. In response, deep learning-based automated source code vulnerability detection methods, particularly those utilizing source code similarity analysis, have recently emerged as promising solutions. However, existing similarity-based source code vulnerability detection methods frequently fail to fully utilize information from the hierarchical structure of source code and are often computationally expensive, limiting their practicality in real-world scenarios. In this paper, we introduce XTransformer, a novel deep learning-based source code vulnerability detector tailored for comparing target source code against archived vulnerable codes across various levels of the source code’s hierarchical structure by leveraging extra cross-attention imposed on the transformer architecture. Additionally, we propose a specialized training strategy based on supervised contrastive learning to improve XTransformer’s ability to effectively learn and differentiate between vulnerable and non-vulnerable source codes. Comprehensive experiments demonstrate that XTransformer outperforms current state-of-the-art methods across different datasets and code lengths while significantly reducing the inference time compared to other similarity-based methods that utilize hierarchical information from source code.