Analyzing CNN Based Behavioural Malware Detection Techniques on Cloud IaaS

Online Malware Classification with System-Wide System Calls in Cloud IaaS

Accurately classifying malware in an environment allows the creation of better response and remediation strategies by cyber analysts. However, classifying malware in a live environment is a difficult task due to the large number of system data sources. Collecting statistics from these separate sources and processing them together in a form that can be used by a machine learning model is difficult. Fortunately, all of these resources are mediated by the operating system's kernel. User programs, malware included, interacts with system resources by making requests to the kernel with system calls.…

Behavioural Reports of Multi-Stage Malware

The extensive damage caused by malware requires anti-malware systems to be constantly improved to prevent new threats. The current trend in malware detection is to employ machine learning models to aid in the classification process. We propose a new dataset with the objective of improving current anti-malware systems. The focus of this dataset is to improve host based intrusion detection systems by providing API call sequences for thousands of malware samples executed in Windows 10 virtual machines. A tutorial on how to create and expand this dataset is provided along with a benchmark demonstr…

Analyzing Machine Learning Approaches for Online Malware Detection in Cloud

The variety of services and functionality offered by various cloud service providers (CSP) have exploded lately. Utilizing such services has created numerous opportunities for enterprises infrastructure to become cloud-based and, in turn, assisted the enterprises to easily and flexibly offer services to their customers. The practice of renting out access to servers to clients for computing and storage purposes is known as Infrastructure as a Service (IaaS). The popularity of IaaS has led to serious and critical concerns with respect to the cyber security and privacy. In particular, malware is …

Early Stage Malware Prediction Using Recurrent Neural Networks

Static malware analysis is well-suited to endpoint anti-virus systems as it can be conducted quickly by examining the features of an executable piece of code and matching it to previously observed malicious code. However, static code analysis can be vulnerable to code obfuscation techniques. Behavioural data collected during file execution is more difficult to obfuscate, but takes a relatively long time to capture - typically up to 5 minutes, meaning the malicious payload has likely already been delivered by the time it is detected. In this paper we investigate the possibility of predicting wh…

基于K均值的哈希恶意软件聚类比较分析

Comparative Analysis of Hash-based Malware Clustering via K-Means

本文通过K均值算法比较了SSDeep、TLSH和IMPHash在恶意软件聚类中的性能，发现TLSH和IMPHash能产生更语义明确的聚类，而SSDeep更适合广泛分类任务。

Symbolic analysis meets federated learning to enhance malware identifier

Over past years, the manually methods to create detection rules were no longer practical in the anti-malware product since the number of malware threats has been growing. Thus, the turn to the machine learning approaches is a promising way to make the malware recognition more efficient. The traditional centralized machine learning requires a large amount of data to train a model with excellent performance. To boost the malware detection, the training data might be on various kind of data sources such as data on host, network and cloud-based anti-malware components, or even, data from different…