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Malware detection with artificial intelligence: A systematic literature review
In this survey, we review the key developments in the field of malware detection using AI and
analyze core challenges. We systematically survey state-of-the-art methods across five …
analyze core challenges. We systematically survey state-of-the-art methods across five …
The METRIC-framework for assessing data quality for trustworthy AI in medicine: a systematic review
D Schwabe, K Becker, M Seyferth, A Klaß… - NPJ Digital …, 2024 - nature.com
The adoption of machine learning (ML) and, more specifically, deep learning (DL)
applications into all major areas of our lives is underway. The development of trustworthy AI …
applications into all major areas of our lives is underway. The development of trustworthy AI …
The role of machine learning in cybersecurity
Machine Learning (ML) represents a pivotal technology for current and future information
systems, and many domains already leverage the capabilities of ML. However, deployment …
systems, and many domains already leverage the capabilities of ML. However, deployment …
Helphed: Hybrid ensemble learning phishing email detection
Phishing email attack is a dominant cyber-criminal strategy for decades. Despite its
longevity, it has evolved during the COVID-19 pandemic, indicating that adversaries exploit …
longevity, it has evolved during the COVID-19 pandemic, indicating that adversaries exploit …
The evolution of ransomware attacks in light of recent cyber threats. How can geopolitical conflicts influence the cyber climate?
This article aims to analyze the current unpredictable cyber climate. In particular, Russia's
invasion of Ukraine has heightened concerns about security incidents, and ransomware …
invasion of Ukraine has heightened concerns about security incidents, and ransomware …
SoK: The impact of unlabelled data in cyberthreat detection
Machine learning (ML) has become an important paradigm for cyberthreat detection (CTD)
in the recent years. A substantial research effort has been invested in the development of …
in the recent years. A substantial research effort has been invested in the development of …
Combining long-term recurrent convolutional and graph convolutional networks to detect phishing sites using URL and HTML
Phishing, a well-known cyber-attack practice has gained significant research attention in the
cyber-security domain for the last two decades due to its dynamic attacking strategies …
cyber-security domain for the last two decades due to its dynamic attacking strategies …
Machine learning for detecting data exfiltration: A review
Context: Research at the intersection of cybersecurity, Machine Learning (ML), and Software
Engineering (SE) has recently taken significant steps in proposing countermeasures for …
Engineering (SE) has recently taken significant steps in proposing countermeasures for …
A comparison of natural language processing and machine learning methods for phishing email detection
P Bountakas, K Koutroumpouchos… - Proceedings of the 16th …, 2021 - dl.acm.org
Phishing is the most-used malicious attempt in which attackers, commonly via emails,
impersonate trusted persons or entities to obtain private information from a victim. Even …
impersonate trusted persons or entities to obtain private information from a victim. Even …
Adversarial robustness of phishing email detection models
P Mehdi Gholampour, RM Verma - Proceedings of the 9th ACM …, 2023 - dl.acm.org
Develo** robust detection models against phishing emails has long been the main
concern of the cyber defense community. Currently, public phishing/legitimate datasets lack …
concern of the cyber defense community. Currently, public phishing/legitimate datasets lack …