Network Attacks Classification for Network Forensics Investigation: Literature Reviews
Dublin Core
Title
Network Attacks Classification for Network Forensics Investigation: Literature Reviews
Subject
network attacks; classification; machine learning; investigation
Description
Every branch of technology must constantly be on guard and anticipate the possibility of numerous cybercrimes due to the
ongoing cyber-attacks. Crimes committed in this era of digitalization will undoubtedly have a negative impact on individuals
or groups. In order to allow any cybercriminal to operate freely without worrying about getting caught, mitigation after a
cyber-attack is often considered a trivial problem. Digital forensics not only plays an important role in the digitization cycle
but is also critical to the digital industry's ability to respond to events as they occur. The standard methods used to support the
pace of progress in digital forensics are very time-consuming and ineffective given the frequency of cybercrime. It is expected
that collaboration between technology disciplines, such as the application of machine learning to digital forensics, will improve
the efficiency of the forensic analysis and investigation process. These recommendations propose the application of machine
learning techniques for automated attack classification using network logs. Specifically, machine learning algorithms would
be trained to detect DDoS, SQL Injection, and XSS attacks based on the traffic logs on the router. The chosen method for this
classification task is Support Vector Machine (SVM), which has been extensively employed in data-driven classification tasks
according to previous research. By leveraging machine learning, the goal is to streamline the investigation of computer
network attacks, making it faster and more efficient
ongoing cyber-attacks. Crimes committed in this era of digitalization will undoubtedly have a negative impact on individuals
or groups. In order to allow any cybercriminal to operate freely without worrying about getting caught, mitigation after a
cyber-attack is often considered a trivial problem. Digital forensics not only plays an important role in the digitization cycle
but is also critical to the digital industry's ability to respond to events as they occur. The standard methods used to support the
pace of progress in digital forensics are very time-consuming and ineffective given the frequency of cybercrime. It is expected
that collaboration between technology disciplines, such as the application of machine learning to digital forensics, will improve
the efficiency of the forensic analysis and investigation process. These recommendations propose the application of machine
learning techniques for automated attack classification using network logs. Specifically, machine learning algorithms would
be trained to detect DDoS, SQL Injection, and XSS attacks based on the traffic logs on the router. The chosen method for this
classification task is Support Vector Machine (SVM), which has been extensively employed in data-driven classification tasks
according to previous research. By leveraging machine learning, the goal is to streamline the investigation of computer
network attacks, making it faster and more efficient
Creator
Muhamad Maulana, Ahmad Luthfi, Dwi Kurnia Wibowo
Source
http://jurnal.iaii.or.id
Publisher
Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association
Date
October 2023
Contributor
Sri Wahyuni
Rights
ISSN Media Electronic: 2580-0760
Format
PDF
Language
English
Type
Text
Files
Collection
Citation
Muhamad Maulana, Ahmad Luthfi, Dwi Kurnia Wibowo, “Network Attacks Classification for Network Forensics Investigation: Literature Reviews,” Repository Horizon University Indonesia, accessed January 11, 2026, https://repository.horizon.ac.id/items/show/10088.