Abstract Application Programming Interfaces (APIs) are widely considered a useful data
source for dynamic malware analysis to understand the behavioral characteristics of …

Application Programming Interfaces (APIs) are still considered the standard accessible data
source and core wok of the most widely adopted malware detection and classification …

In this paper, a new deep learning and machine learning combined model is proposed for
malware behavior analysis. One part of it analyzes the dependency relation in API …

Dynamic malware detection executes the software in a secured virtual environment and
monitors its run-time behavior. This technique widely uses API sequence analysis to identify …

Analyzing malware based on API call sequence is an effective approach as the sequence
reflects the dynamic execution behavior of malware. Recent advancements in deep learning …

Learning on execution behaviour, ie, sequences of API calls, is proven to be effective in
malware detection. In this paper, we present CruParamer, a deep neural network based …

It is a wide-spread way to detect malware by analyzing its behavioral characteristics based
on API call sequences. However, previous studies usually just focus on its static or dynamic …

Malware classification plays an important role in tracing the attack sources of computer
security. However, existing static analysis methods are fast in classification, but they are …

Malware detection plays a crucial role in computer security. Recent researches mainly use
machine learning based methods heavily relying on domain knowledge for manually …

Identifying the family of malware can determine their malicious intent and attack patterns,
which helps to efficiently analyze large numbers of malware variants. Methods based on …