On-device deep learning is rapidly gaining popularity in mobile applications. Compared to
offloading deep learning from smartphones to the cloud, on-device deep learning enables …

In recent times, networks have been prone to several types of attacks, such as DDoS attacks,
volumetric attacks, replay attacks, eavesdropping, etc., which drastically degrade the …

Malware detection is an important area of cyber security. Computer systems rely on malware
detection applications to prevent malware attacks from succeeding. Malware detection is not …

Adversarial attacks have recently gained popularity due to their simplicity, impact, and
applicability to a wide range of machine learning scenarios. However, knowledge of a …

Adversarial attacks are more prevalent in Consumer Internet of Things (CIoT) devices (ie,
smart home devices, cameras, actuators, sensors, and micro-controllers) because of their …

Machine learning models serve as a powerful new technique for detecting malware.
However, they are extremely vulnerable to attacks using adversarial examples. Machine …

In recent years, machine learning–based techniques are used to prevent cyberattacks
caused by malware, and special attention is paid to the risks posed by such systems …

Targeted attacks via transient devices are not new. How-ever, the introduction of BadUSB
attacks has shifted the attack paradigm tremendously. Such attacks embed malicious code …

Concealing malicious components within widely used USB peripherals has become a
popular attack vector utilizing social engineering techniques and exploiting users' trust in …

Machine learning (ML) models are increasingly being used in the development of Malware
Detection Systems. Existing research in this area primarily focuses on developing new …