A review of threat modelling approaches for APT-style attacks

M Tatam, B Shanmugam, S Azam, K Kannoorpatti - Heliyon, 2021 - cell.com
Threats are potential events, intentional or not, that compromise the confidentiality, integrity,
and/or availability of information systems. Defending against threats and attacks requires …

APT beaconing detection: A systematic review

MA Talib, Q Nasir, AB Nassif, T Mokhamed… - Computers & …, 2022 - Elsevier
Abstract Advanced Persistent Threat (APT) is a type of threat that has grabbed the attention
of researchers, particularly in the industrial security field. APTs are cyber intrusions carried …

Graph neural networks for intrusion detection: A survey

T Bilot, N El Madhoun, K Al Agha, A Zouaoui - IEEE Access, 2023 - ieeexplore.ieee.org
Cyberattacks represent an ever-growing threat that has become a real priority for most
organizations. Attackers use sophisticated attack scenarios to deceive defense systems in …

SBI model for the detection of advanced persistent threat based on strange behavior of using credential dumping technique

N Mohamed, B Belaton - IEEE Access, 2021 - ieeexplore.ieee.org
This study investigated the shift from the manual approach of processing data to the digitized
method making organizational data prone to attack by cybercriminals. The latest threat …

A multi-model ensemble learning framework for imbalanced android malware detection

H Zhu, Y Li, L Wang, VS Sheng - Expert Systems with Applications, 2023 - Elsevier
The continuous malicious software (malware) attacks on smartphones pose a serious threat
to the security of users, especially the dominant platform Android. Data-driven methods …

FewM-HGCL: Few-shot malware variants detection via heterogeneous graph contrastive learning

C Liu, B Li, J Zhao, Z Zhen, X Liu… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Malware variant attacks have been becoming serious threats in the Internet ecosystem.
However, prior arts on malware variants detection over-rely on the supervised learning …

[HTML][HTML] Unraveled—A semi-synthetic dataset for Advanced Persistent Threats

S Myneni, K Jha, A Sabur, G Agrawal, Y Deng… - Computer Networks, 2023 - Elsevier
U nraveled is a novel cybersecurity dataset capturing Advanced Persistent Threat (APT)
attacks not available in the public domain. Existing cybersecurity datasets lack coherent …

APT-KGL: An intelligent APT detection system based on threat knowledge and heterogeneous provenance graph learning

T Chen, C Dong, M Lv, Q Song, H Liu… - … on Dependable and …, 2022 - ieeexplore.ieee.org
APTs (Advanced Persistent Threats) have caused serious security threats worldwide. Most
existing APT detection systems are implemented based on sophisticated forensic analysis …

A State-of-the-Art Review of Malware Attack Trends and Defense Mechanism.

J Ferdous, R Islam, A Mahboubi, MZ Islam - IEEE Access, 2023 - ieeexplore.ieee.org
The increasing sophistication of malware threats has led to growing concerns in the anti-
malware community, as malware poses a significant danger to online users despite the …

Poirot: Causal Correlation Aided Semantic Analysis for Advanced Persistent Threat Detection

J Yang, Q Zhang, X Jiang, S Chen… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
The volatile, covert and slow multistage attack patterns of Advanced Persistent Threat (APT)
present a tricky challenge of APT detection, which are vital for organisations to protect their …