Although malicious software (malware) has been around since the early days of computers,
the sophistication and innovation of malware has increased over the years. In particular, the …

Since the mid-1990s, researchers have been trying to use machine-learning-based
approaches to solve a number of different compiler optimization problems. These …

Among the many software testing techniques available today, fuzzing has remained highly
popular due to its conceptual simplicity, its low barrier to deployment, and its vast amount of …

Recently, hybrid fuzzing has been proposed to address the limitations of fuzzing and
concolic execution by combining both approaches. The hybrid approach has shown its …

Coverage-guided fuzzing is a widely used and effective solution to find software
vulnerabilities. Tracking code coverage and utilizing it to guide fuzzing are crucial to …

Automated software testing based on fuzzing has experienced a revival in recent years.
Especially feedback-driven fuzzing has become well-known for its ability to efficiently …

Fuzzing is an effective software testing technique to find bugs. Given the size and complexity
of real-world applications, modern fuzzers tend to be either scalable, but not effective in …

To stop side channel attacks on CPU caches that have allowed attackers to leak secret
information and break basic security mechanisms, the security community has developed a …

Serverless computing is a rapidly growing cloud application model, popularized by
Amazon's Lambda platform. Serverless cloud services provide fine-grained provisioning of …

Privacy-preserving multi-party machine learning allows multiple organizations to perform
collaborative data analytics while guaranteeing the privacy of their individual datasets …