Applying machine learning techniques to malware detection is a common approach to try to
overcome the limitations of signature-based methods. However, it is difficult to engineer a …

New malware detection techniques are highly needed due to the increasing threat posed by
mobile malware. Machine learning techniques have provided promising results in this …

The ever-growing malware threat in the cyber space calls for techniques that are more
effective than widely deployed signature-based detection systems and more scalable than …

Malicious software in form of Internet worms, computer viruses, and Trojan horses poses a
major threat to the security of networked systems. The diversity and amount of its variants …

When training a machine learning model, there is likely to be a tradeoff between accuracy
and the diversity of the dataset. Previous research has shown that if we train a model to …

Malware detection methods are divided in two groups: static and dynamic. While methods
based on static analysis might be lightweight and suitable for constrained resources of …

Current commercial antivirus detection engines still rely on signature-based methods.
However, with the huge increase in the number of new malware, current detection methods …

Cyberattacks and the use of malware are more and more omnipresent nowadays. Targets
are as varied as states or publicly traded companies. Malware analysis has become a very …

Malware is a menace to computing. The lag between malware landing on a user's system
and the development of signatures to detect the same malware can prove catastrophic for …

The most common defenses against malware threats involves the use of signatures derived
from instances of known malware. However, the constant evolution of the malware threat …