Nazarii Lutsiv¹, Taras Maksymyuk^1,*, Mykola Beshley¹, Orest Lavriv¹, Volodymyr Andrushchak¹, Anatoliy Sachenko², Liberios Vokorokos³, Juraj Gazda³

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 413-431, 2022, DOI:10.32604/cmc.2022.018773

Abstract The extensive proliferation of modern information services and ubiquitous digitization of society have raised cybersecurity challenges to new levels. With the massive number of connected devices, opportunities for potential network attacks are nearly unlimited. An additional problem is that many low-cost devices are not equipped with effective security protection so that they are easily hacked and applied within a network of bots (botnet) to perform distributed denial of service (DDoS) attacks. In this paper, we propose a novel intrusion detection system (IDS) based on deep learning that aims to identify suspicious behavior in modern heterogeneous information systems. The proposed approach… More >

Jiaming Mao^1,*, Mingming Zhang¹, Mu Chen², Lu Chen², Fei Xia¹, Lei Fan¹, ZiXuan Wang³, Wenbing Zhao⁴

Computer Systems Science and Engineering, Vol.39, No.3, pp. 373-390, 2021, DOI:10.32604/csse.2021.018086

Abstract The rapidly increasing popularity of mobile devices has changed the methods with which people access various network services and increased network traffic markedly. Over the past few decades, network traffic identification has been a research hotspot in the field of network management and security monitoring. However, as more network services use encryption technology, network traffic identification faces many challenges. Although classic machine learning methods can solve many problems that cannot be solved by port- and payload-based methods, manually extract features that are frequently updated is time-consuming and labor-intensive. Deep learning has good automatic feature learning capabilities and is an ideal… More >