Amr Munshi^1,2,*, Majid Alotaibi^1,2, Saud Alotaibi^2,3, Wesam Al-Sabban^2,3, Nasser Allheeib⁴

Computer Systems Science and Engineering, Vol.47, No.1, pp. 891-902, 2023, DOI:10.32604/csse.2023.039374

Abstract The anonymity of the darknet makes it attractive to secure communication lines from censorship. The analysis, monitoring, and categorization of Internet network traffic are essential for detecting darknet traffic that can generate a comprehensive characterization of dangerous users and assist in tracing malicious activities and reducing cybercrime. Furthermore, classifying darknet traffic is essential for real-time applications such as the timely monitoring of malware before attacks occur. This paper presents a two-stage deep network chain for detecting and classifying darknet traffic. In the first stage, anonymized darknet traffic, including VPN and Tor traffic related to hidden services provided by darknets, is… More >

Muhammad Basit Umair¹, Zeshan Iqbal¹, Muhammad Bilal², Jamel Nebhen⁴, Tarik Adnan Almohamad³, Raja Majid Mehmood^5,*

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 407-422, 2022, DOI:10.32604/cmc.2022.020727

Abstract Internet of Things (IoT) defines a network of devices connected to the internet and sharing a massive amount of data between each other and a central location. These IoT devices are connected to a network therefore prone to attacks. Various management tasks and network operations such as security, intrusion detection, Quality-of-Service provisioning, performance monitoring, resource provisioning, and traffic engineering require traffic classification. Due to the ineffectiveness of traditional classification schemes, such as port-based and payload-based methods, researchers proposed machine learning-based traffic classification systems based on shallow neural networks. Furthermore, machine learning-based models incline to misclassify internet traffic due to improper… More >