Drift Forensics of Malware Classifiers
The widespread occurrence of mobile malware still poses a significant security threat to
billions of smartphone users. To counter this threat, several machine learning-based …
billions of smartphone users. To counter this threat, several machine learning-based …
Machine Learning for Windows Malware Detection and Classification: Methods, Challenges and Ongoing Research
D Gibert - arXiv preprint arXiv:2404.18541, 2024 - arxiv.org
In this chapter, readers will explore how machine learning has been applied to build
malware detection systems designed for the Windows operating system. This chapter starts …
malware detection systems designed for the Windows operating system. This chapter starts …
A Comparison of Neural-Network-Based Intrusion Detection against Signature-Based Detection in IoT Networks
M Schrötter, A Niemann, B Schnor - Information, 2024 - mdpi.com
Over the last few years, a plethora of papers presenting machine-learning-based
approaches for intrusion detection have been published. However, the majority of those …
approaches for intrusion detection have been published. However, the majority of those …
A Detailed Study of Advancements in Digital Forensics
Digital forensics is a complicated process with many variables. Every case tends to be
different, with varied levels of complexity. This heterogeneous nature of work comes from the …
different, with varied levels of complexity. This heterogeneous nature of work comes from the …
[PDF][PDF] DETECTION AGAINST SIGNATURE-BASED DETECTION IN IOT NETWORKS
M Schrötter, A Niemann, B Schnor - 2024 - techrxiv.org
Over the last few years, a plethora of papers presenting machine learning-based
approaches for intrusion detection has been published. However, the majority of those …
approaches for intrusion detection has been published. However, the majority of those …