Enabling Non-Expert Analysis OF Large Volumes OF Intercepted Network Traffic

Wiel, Erwin van de; Scanlon, Mark; Le-Khac, Nhien-An

doi:10.1007/978-3-319-99277-8_11

Cited by 5 publications

(1 citation statement)

References 11 publications

(13 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Each of the mainstream PC operating systems, such as Mac OS, Windows, and Linux, provide built-in hard disk encryption. Meanwhile, network communications, both wired and wireless, commonly employ strong packet encryption mechanisms [5]. Modern computer hardware has made the automated handling of encrypted data an everyday possibility in consumer, industrial and military applications [6].…”

Section: Introductionmentioning

confidence: 99%

A survey of electromagnetic side-channel attacks and discussion on their case-progressing potential for digital forensics

Sayakkara

Le-Khac

Scanlon

2019

Digital Investigation

Self Cite

View full text Add to dashboard Cite

The increasing prevalence of Internet of Things (IoT) devices has made it inevitable that their pertinence to digital forensic investigations will increase into the foreseeable future. These devices produced by various vendors often posses limited standard interfaces for communication, such as USB ports or WiFi/Bluetooth wireless interfaces. Meanwhile, with an increasing mainstream focus on the security and privacy of user data, built-in encryption is becoming commonplace in consumer-level computing devices, and IoT devices are no exception. Under these circumstances, a significant challenge is presented to digital forensic investigations where data from IoT devices needs to be analysed.This work explores the electromagnetic (EM) side-channel analysis literature for the purpose of assisting digital forensic investigations on IoT devices. EM side-channel analysis is a technique where unintentional electromagnetic emissions are used for eavesdropping on the operations and data handling of computing devices. The non-intrusive nature of EM side-channel approaches makes it a viable option to assist digital forensic investigations as these attacks require, and must result in, no modification to the target device. The literature on various EM side-channel analysis attack techniques are discussed -selected on the basis of their applicability in IoT device investigation scenarios. The insight gained from the background study is used to identify promising future applications of the technique for digital forensic analysis on IoT devices -potentially progressing a wide variety of currently hindered digital investigations.

show abstract

Section: Introductionmentioning

confidence: 99%

A survey of electromagnetic side-channel attacks and discussion on their case-progressing potential for digital forensics

Sayakkara

Le-Khac

Scanlon

2019

Digital Investigation

Self Cite

View full text Add to dashboard Cite

show abstract

Deep Learning Based Network Intrusion Detection System for Resource-Constrained Environments

Rizvi

Scanlon

McGibney

et al. 2023

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

View full text Add to dashboard Cite

Application of Artificial Intelligence to Network Forensics: Survey, Challenges and Future Directions

et al. 2022

View full text Add to dashboard Cite

Network forensics focuses on the identification and investigation of internal and external network attacks, the reverse engineering of network protocols, and the uninstrumented investigation of networked devices. It lies at the intersection of digital forensics, incident response and network security. Network attacks exploit software and hardware vulnerabilities and communication protocols. The scope of a network forensic investigation can range from Internet-wide down to a single device's network traffic. Network analysis tools (NATs) aid security professionals and law enforcement in the capturing, identification and analysis of network traffic. However, in most instances, the sheer volume of data to be analyzed is enormous and, despite some built-in NAT automation, the investigation of network traffic is often an arduous process. Furthermore, significant expert time remains wasted in the investigation of a high frequency of false positive alerting from automated systems. To address this globally impacting problem, artificial intelligence based approaches are becoming increasingly employed to automatically detect attacks and increase network traffic classification accuracy. This paper provides a comprehensive survey of the state-of-the-art in network forensics and the application of expert systems, machine learning, deep learning, and ensemble/hybrid approaches to a range of application areas in the field. These include network traffic analysis, intrusion detection systems, Internetof-Things devices, cloud forensics, DNS tunneling, smart grid forensics, and vehicle forensics. In addition, the current challenges and future research directions for each of the aforementioned application areas is discussed.

show abstract

Enabling Non-Expert Analysis OF Large Volumes OF Intercepted Network Traffic

Cited by 5 publications

References 11 publications

A survey of electromagnetic side-channel attacks and discussion on their case-progressing potential for digital forensics

A survey of electromagnetic side-channel attacks and discussion on their case-progressing potential for digital forensics

Deep Learning Based Network Intrusion Detection System for Resource-Constrained Environments

Application of Artificial Intelligence to Network Forensics: Survey, Challenges and Future Directions

Contact Info

Product

Resources

About