Abstract: This paper proposes a machine learning model based on the co-existence of static features for Android malware detection. The proposed model assumes that Android malware requests an abnormal ...

Abstract: In the area of machine learning (ML) training data optimization through the construction of compact data, the focus of this paper is presented. The concept of compact data design, aimed at ...

Government agencies face increasingly sophisticated security challenges in a world driven by digital transformation.

Antivirus software used to hunt for known malware, but now it’s predicting suspicious behavior before an attack fully lands.

The previous article outlined how generative AI is fundamentally transforming the cybersecurity threat landscape, enabling attackers with unprecedented capabilities in phishing automation, deepfake ...

Tax Fraud Detection for Under-Reporting Declarations Using an Unsupervised Machine Learning Approach (KDD 2018) Daniel de Roux, Boris Perez, Andrés Moreno, María-Del-Pilar Villamil, César Figueroa ...

This week’s cybersecurity recap covers Firefox and Chrome bugs, EDR-killer tools, a TV botnet, an OpenBSD flaw, Android ...

Tapping into the power of collaboration, Johns Hopkins University and West Virginia University are launching a new partnership to bring together researchers from both institutions to address complex ...

The hunt is on to find protections against the coming generation of adaptive AI worm malware in order to head off a global incident on the scale of other famous worm events, such as NotPetya, Stuxnet, ...

More than half (56%) of Internet users faced fraud over the past year, and 45% became victims of attacks on their devices, accounts or data (social media accounts hacking, data leakage, malware ...

ThreatsDay Bulletin covers this week’s cyber threats, from phishing and ransomware to exposed AI systems, sandbox flaws, and ...