A new Intelligent Satellite Deep Learning Network Forensic framework for smart satellite networks. (April 2022)

• Record Type: Journal Article
• Authors: Koroniotis, Nickolaos; Moustafa, Nour; Slay, Jill
• Is Part Of: Computers & electrical engineering. Volume 99(2022)
• Publication Date: 2022-04
• Subjects: Internet of Things (IoT) -- Network forensics -- Deep Learning -- Smart satellite -- Cyber attacks; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2022.107745
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: By combining the Internet of Things (IoT) and Artificial Intelligence (AI), new augmentations and enhancements are realized, resulting in environment-aware systems that can enable intelligent decision making, with one such example being smart satellite networks. However, smart satellite networks attract the attention of hackers, resulting in them being targeted by severe cyber-attacks that can compromise their integrity, affect their availability, or breach the confidentiality of the data they generate, collect or rout. Lately, several prominent cyber-attacking scenarios have been observed to target IoT-enabled systems, resulting in loss, alteration, or exfiltration of data, disabling of devices, (distributed) denial of services, the formation of botnets, and lateral movement into otherwise secured networks. In this paper, we propose a network forensic framework based on Deep Learning (DL), the so-called Intelligent Satellite Deep Learning Network Forensic (INSAT-DLNF), for the detection and tracing of cyber-attack activities targeting smart satellite networks. For this framework, we trained a Long Short-term Memory Recurrent Neural Network (LSTM-RNN) and Gated Recurrent Unit (GRU) and compared their performances to five supervised and two unsupervised Machine Learning (ML) algorithms. The experimental results indicate that ML and DL algorithms can be employed effectively for the discovery and tracing of cyber-attacks, resulting in more adaptive and resilient cyber-security solutions, compared to legacy forensic tools that cannot discover zero-day attack surfaces and vectors. Graphical abstract: Highlights: Smart Satellites vulnerable to IoT-centric cyber attacks. Intelligent deep learning network forensic framework for smart satellite networks. Big data analysis, for investigating attack events in smart satellite IoT networks. Assess and compare network forensic methods using deep/machine learning.