Review of android malware detection based on deep learning

Z Wang, Q Liu, Y Chi - IEEE Access, 2020 - ieeexplore.ieee.org
At present, smartphones running the Android operating system have occupied the leading
market share. However, due to the Android operating system's open-source nature, Android …

Behavioral-based classification and identification of ransomware variants using machine learning

H Daku, P Zavarsky, Y Malik - … on trust, security and privacy in …, 2018 - ieeexplore.ieee.org
Due to the changing behavior of ransomware, traditional classification and detection
techniques do not accurately detect new variants of ransomware. Attackers use polymorphic …

Revisiting the detection of lateral movement through Sysmon

C Smiliotopoulos, K Barmpatsalou, G Kambourakis - Applied Sciences, 2022 - mdpi.com
This work attempts to answer in a clear way the following key questions regarding the
optimal initialization of the Sysmon tool for the identification of Lateral Movement in the MS …

Detecting lateral movement: A systematic survey

C Smiliotopoulos, G Kambourakis, C Kolias - Heliyon, 2024 - cell.com
Within both the cyber kill chain and MITRE ATT&CK frameworks, Lateral Movement (LM) is
defined as any activity that allows adversaries to progressively move deeper into a system in …

Real-time detection system against malicious tools by monitoring dll on client computers

W Matsuda, M Fujimoto… - 2019 IEEE Conference on …, 2019 - ieeexplore.ieee.org
The targeted attacks cause severe damage worldwide. Detecting targeted attacks are
challenging because the attack methods are very sophisticated. Network-based solutions …

Improvising the Malware Detection Accuracy in Portable Document Format (PDFs) through Machine Learning Classifiers

MA Shahid, M Safyan, Z Pervez - Review of Applied Management …, 2024 - ramss.spcrd.org
Every time a spike is observed in cyber-attacks, a huge financial loss is incurred that has
surpassed $2 trillion according to some estimates. Apart from monetary setbacks, the …

Feature based comparative analysis of online malware scanners (OMS)

AH Johar, A Gerard, N Athar, U Asgher - Advances in Neuroergonomics …, 2021 - Springer
Threat Intelligence is evidence-based knowledge that helps to understand, predict and
adapt the behavior of an existing or emerging threats. Threat Intelligence can be used to …

Threats Detection and Analysis Based on SYSMON Tool

N Bahniuk, L Oleksandr, B Kateryna… - 2023 13th …, 2023 - ieeexplore.ieee.org
In this work, an nalysis for the study of threats in a real environment with the possibility of
conducting a full-fledged analysis of threats, as well as their simulationhas been developed …

The testbed for definition of the exploit's execution features to detect and score cyber attacks

S Verevkin, E Fedorchenko - E3S Web of Conferences, 2024 - e3s-conferences.org
The paper considers the deployment of the testbed for definition of the exploit's execution
features to detect and score cyber-attacks. The paper describes the place of the proposed …

Leveraging Interpretable Machine Learning Approaches to Detect Anomaly Within UK Cloud Service Sector

CK Tong - 2024 - search.proquest.com
The increasing reliance on cloud-based platforms has saved time, money, and resources
but also has lured criminals in cyber-attacks. With financial losses averaging£ 3.4 million per …