Adversarial machine learning and the robustness of machine learning is gaining attention,
especially in image classification. Attacks based on data poisoning, with the aim to lower the …

Machine Learning (ML) models are being adopted as state of the art tools to defend systems
against cybersecurity threats. Despite their high accuracy, such models remain vulnerable to …

Researchers are looking into solutions to support the enormous demand for wireless
communication, which has been exponentially increasing along with the growth of …

The growing popularity of Internet-of-Things devices makes them a desired target for
malicious actors. Most attacks start with a reconnaissance phase where the attacker gathers …

Deep learning-based vulnerability detection models have received widespread attention;
however, these models are susceptible to adversarial attack, and adversarial examples are …

In recent years, machine learning technology has been extensively utilized, leading to
increased attention to the security of AI systems. In the field of image recognition, an attack …

Malware analysis and detection is a rat race between malware designer and anti-malware
community. Most of the current Smartphone antivirus (s) are based on the signature …

Machine learning has already been exploited as a useful tool for detecting malicious
executable files. Data retrieved from malware samples, such as header fields, instruction …

The last decade has seen phenomenal growth in the application of machine learning. At this
point, it won't be wrong to claim that most technological change is directly or indirectly …

The predictive capabilities of machine learning models have improved significantly in recent
years, leading to their widespread use in various fields. However, these models remain …