Knowledge mining in cybersecurity: From attack to defense

KA Akbar, SM Halim, Y Hu, A Singhal, L Khan… - IFIP Annual Conference …, 2022 - Springer
In the fast-evolving world of Cybersecurity, an analyst often has the difficult task of
responding to new threats and attack campaigns within a limited amount of time. If an …

A framework for modeling cyber attack techniques from security vulnerability descriptions

H Binyamini, R Bitton, M Inokuchi, T Yagyu… - Proceedings of the 27th …, 2021 - dl.acm.org
Attack graphs are one of the main techniques used to automate the cybersecurity risk
assessment process. In order to derive a relevant attack graph, up-to-date information on …

Cyber-all-intel: An ai for security related threat intelligence

S Mittal, A Joshi, T Finin - arXiv preprint arXiv:1905.02895, 2019 - arxiv.org
Keeping up with threat intelligence is a must for a security analyst today. There is a volume
of information present inthe wild'that affects an organization. We need to develop an artificial …

Semi-automated information extraction from unstructured threat advisories

RR Ramnani, K Shivaram, S Sengupta - Proceedings of the 10th …, 2017 - dl.acm.org
One of the fundamental challenges for information officers of most organizations today is the
growing number of cyber security threats. This has led to an emerging field of Cyber Threat …

Cyber threat intelligence mining for proactive cybersecurity defense: a survey and new perspectives

N Sun, M Ding, J Jiang, W Xu, X Mo… - … Surveys & Tutorials, 2023 - ieeexplore.ieee.org
Today's cyber attacks have become more severe and frequent, which calls for a new line of
security defenses to protect against them. The dynamic nature of new-generation threats …

[图书][B] Data science in cybersecurity and cyberthreat intelligence

LF Sikos, KKR Choo - 2020 - Springer
Obtaining accurate information about online activities in near-real time is becoming
increasingly difficult, particularly because of the constantly increasing data volume …

A deep-dive on machine learning for cyber security use cases

R Vinayakumar, KP Soman… - Machine Learning for …, 2019 - taylorfrancis.com
Conventional methods, such as static and binary analysis of malware, are inefficient in
addressing the escalation of malware because of the time taken to reverse engineer the …

An automated, end-to-end framework for modeling attacks from vulnerability descriptions

H Binyamini, R Bitton, M Inokuchi, T Yagyu… - arXiv preprint arXiv …, 2020 - arxiv.org
Attack graphs are one of the main techniques used to automate the risk assessment
process. In order to derive a relevant attack graph, up-to-date information on known attack …

Using contextual information to identify cyber-attacks

A AlEroud, G Karabatis - Information fusion for cyber-security analytics, 2017 - Springer
A recent trend is toward utilizing knowledge-based intrusion detection systems (IDSs).
Knowledge-based IDSs store knowledge about cyber-attacks and possible vulnerabilities …

Attack hypotheses generation based on threat intelligence knowledge graph

FK Kaiser, U Dardik, A Elitzur… - … on Dependable and …, 2023 - ieeexplore.ieee.org
Cyber threat intelligence on past attacks may help with attack reconstruction and the
prediction of the course of an ongoing attack by providing deeper understanding of the tools …