Journal of High Speed Networks - Volume 26, issue 3

Authors: Ampririt, Phudit | Liu, Yi | Ikeda, Makoto | Matsuo, Keita | Barolli, Leonard | Takizawa, Makoto

Article Type: Research Article

Abstract: The Fifth Generation (5G) networks are expected to be flexible to satisfy demands of high-quality services such as high speed, low latencies and enhanced reliability from customers. Also, the rapidly increasing amount of user devices and high user’s requests becomes a problem. Thus, the Software-Defined Network (SDN) will be the key function for efficient management and control. To deal with these problems, we propose a Fuzzy-based SDN approach. This paper presents and compares two Fuzzy-based Systems for Admission Control (FBSAC) in 5G wireless networks: FBSAC1 and FBSAC2. The FBSAC1 considers for admission control decision three parameters: Grade of Service (GS), …User Request Delay Time (URDT) and Network Slice Size (NSS). In FBSAC2, we consider as an additional parameter the Slice Priority (SP). So, FBSAC2 has four input parameters. The simulation results show that the FBSAC2 is more complex than FBSAC1, but it has a better performance for admission control. Show more

Keywords: 5G, SDN, admission control, Network Slicing

DOI: 10.3233/JHS-200637

Citation: Journal of High Speed Networks, vol. 26, no. 3, pp. 169-183, 2020

Authors: Singh, Dinesh | Ranvijay, | Yadav, Rama Shankar

Article Type: Research Article

Abstract: The safety event information sharing among the vehicles in motion is the primary goal to design the vehicular ad hoc network (VANET). The shared safety event information assists vehicles to avoid road accidents and driving inconvenience. The advantages of safety event information sharing in VANET has become blunt due to the misbehavior of vehicles. The vehicle’s misbehavior like dissemination of false information, reply of bogus messages, etc., can create traffic hazards on the road and may result in the loss of property and human lives. In VANET, the detection of such misbehaving vehicles along with minimum time delay in flooding …safety event information (i.e., incident delay) to others is challenging due to the high speed of vehicles. The formation of stable VANET topology is a feasible solution among many to improve the performance of misbehavior detection and reducing incident delay even with high speed of vehicles. In this paper, we propose an information based misbehavior detection algorithm (IBMDA) that effectively works in stable cluster based VANET. Our proposed IBMDA algorithm that runs on the selected cluster head vehicles is used to verify the content of received safety event messages. The identification of vehicles as malicious or non malicious depends on the result of verification at cluster heads. An illustrative example is given to explore our proposed algorithm easily and effectively. The highway scenario is considered to test the performance of our proposed IBMDA algorithm. The simulation is performed with a detailed comparative analysis using ns-3 simulator. It is observed that under the considered scenario, our proposed algorithm improves the misbehavior detection accuracy up to 6.46% and reduces average incident delay approximately up to 14.78% as compared to existing algorithms. Show more

Keywords: Vehicular Ad Hoc Network (VANET), safety event information, IBMDA, misbehavior of vehicles

DOI: 10.3233/JHS-200638

Citation: Journal of High Speed Networks, vol. 26, no. 3, pp. 185-207, 2020

Authors: Madhiarasan, M. | Tipaldi, M. | Siano, P.

Article Type: Research Article

Abstract: Artificial neural network (ANN)-based methods belong to one of the most growing research fields within the artificial intelligence ecosystem, and many novel contributions have been developed over the last years. They are applied in many contexts, although some “influencing factors” such as the number of neurons, the number of hidden layers, and the learning rate can impact the performance of the resulting artificial neural network-based applications. This paper provides a deep analysis about artificial neural network performance based on such factors for real-world temperature forecasting applications. An improved back propagation algorithm for such applications is also presented. By using the …results of this paper, researchers and practitioners can analyse the encountered issues when applying ANN-based models for their own specific applications with the aim of achieving better performance indexes. Show more

Keywords: Artificial neural network, optimization, modeling and simulation, improved back propagation neural network, temperature forecasting applications

DOI: 10.3233/JHS-200639

Citation: Journal of High Speed Networks, vol. 26, no. 3, pp. 209-223, 2020

Authors: Maimour, Moufida

Article Type: Research Article

Abstract: Multipath routing has been considered in the literature to provide bandwidth aggregation for high data rate applications. Because of the phenomena of inter-path interference in wireless networks, the overall throughput is far from being the summation of the provided ones by the different paths individually. One promising solution to this problem is to make use of interference aware metrics to build multiple paths such that the inter-path interference is minimised. In this work, we are interested in assessing the impact of a set of interference aware metrics on the performance of incremental multipath routing in Wireless Sensor Networks (WSN). Incremental …multipath routing builds one path per request/reply session and hence is more suitable to be combined to interference aware metrics. We review, implement and evaluate both active and passive monitoring metrics and show that for constrained networks such as WSNs, the latter are more adapted. Additionally, we show that within the passive monitoring metrics, the best one is not necessarily the most expensive to measure. Show more

Keywords: Wireless sensor networks (WSN), iterference aware metrics, multipath routing rrotocols

DOI: 10.3233/JHS-200640

Citation: Journal of High Speed Networks, vol. 26, no. 3, pp. 225-240, 2020

Authors: Alothman, Zainab | Alkasassbeh, Mouhammd | Al-Haj Baddar, Sherenaz

Article Type: Research Article

Abstract: The numerous security loopholes in the design and implementation of many IoT devices have rendered them an easy target for botnet attacks. Several approaches to implement behavioral IoT botnet attacks detection have been explored, including machine learning. The main goal of previous studies was to achieve the highest possible accuracy in distinguishing normal from malicious IoT traffic, with minimal regard to the identification of the particular type of attack that is being launched. In this study, we present a machine learning based approach for detecting IoT botnet attacks that not only helps distinguish normal from malicious traffic, but also detects …the type of the IoT botnet attack. To achieve this goal, the Bot-IoT dataset, in which instances have main attack and sub-attack categories, was utilized after performing the Synthetic Minority Over-sampling Technique (SMOTE), among other preprocessing techniques. Moreover, multiple classifiers were tested and the results from the best three, namely: J48, Random Forest (RF), and Multilayer Perceptron (MLP) networks were reported. The results showed the superiority of the RF and J48 classifiers compared to the MLP networks and other state-of-the-art solutions. The accuracy of the best binary classifier reported in this study reached 0.999, whereas the best accuracies of main attack and subcategories classifications reached 0.96 and 0.93, respectively. Only few studies address the classification errors in this domain, yet, it was assessed in this study in terms of False Negative (FN) rates. J48 and RF classifiers, here also, outperformed the MLP network classifier, and achieved a maximum micro FN rate for subcategories classification of 0.076. Show more

Keywords: IoT botnets, Intrusion Detection, Bot-IoT dataset, SMOTE, machine learning, malicious IoT traffic

DOI: 10.3233/JHS-200641

Citation: Journal of High Speed Networks, vol. 26, no. 3, pp. 241-254, 2020