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With the increase of IoT devices and technologies coming into service, Malware has risen as a challenging threat with increased infection rates and levels of sophistication. Without strong security mechanisms, a huge amount of sensitive…
The constant growth in the number of malware - software or code fragment potentially harmful for computers and information networks - and the use of sophisticated evasion and obfuscation techniques have seriously hindered classic…
This study conducts a thorough examination of malware detection using machine learning techniques, focusing on the evaluation of various classification models using the Mal-API-2019 dataset. The aim is to advance cybersecurity capabilities…
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…
The pervasiveness of the Android operating system, with the availability of applications almost for everything, is readily accessible in the official Google play store or a dozen alternative third-party markets. Additionally, the vital role…
Mobile malware has been growing in scale and complexity spurred by the unabated uptake of smartphones worldwide. Android is fast becoming the most popular mobile platform resulting in sharp increase in malware targeting the platform.…
The persistent threat of Android malware presents a serious challenge to the security of millions of users globally. While many machine learning-based methods have been developed to detect these threats, their reliance on large labeled…
Vulnerability discovery and exploits detection are two wide areas of study in software engineering. This preliminary work tries to combine existing methods with machine learning techniques to define a metric classification of vulnerable…
The performance of a machine learning-based malware classifier depends on the large and updated training set used to induce its model. In order to maintain an up-to-date training set, there is a need to continuously collect benign and…
This technical report presents a comprehensive analysis of malware classification using OpCode sequences. Two distinct approaches are evaluated: traditional machine learning using n-gram analysis with Support Vector Machine (SVM), K-Nearest…
We consider the use of machine learning for hypothesis testing with an emphasis on target detection. Classical model-based solutions rely on comparing likelihoods. These are sensitive to imperfect models and are often computationally…
The vulnerability of machine learning-based malware detectors to adversarial attacks has prompted the need for robust solutions. Adversarial training is an effective method but is computationally expensive to scale up to large datasets and…
Identifying the tasks a given piece of malware was designed to perform (e.g. logging keystrokes, recording video, establishing remote access, etc.) is a difficult and time-consuming operation that is largely human-driven in practice. In…
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,…
The digital transformation faces tremendous security challenges. In particular, the growing number of cyber-attacks targeting Internet of Things (IoT) systems restates the need for a reliable detection of malicious network activity. This…
One of the major and serious threats that the Internet faces today is the vast amounts of data and files which need to be evaluated for potential malicious intent. Malicious software, often referred to as a malware that are designed by…
Feature selection (FS) remains essential for building accurate and interpretable detection models, particularly in high-dimensional malware datasets. Conventional FS methods such as Extra Trees, Variance Threshold, Tree-based models,…
Predicting the probability of default (PD) of prospective loans is a critical objective for financial institutions. In recent years, machine learning (ML) algorithms have achieved remarkable success across a wide variety of prediction…
With a growing increase in botnet attacks, computer networks are constantly under threat from attacks that cripple cyber-infrastructure. Detecting these attacks in real-time proves to be a difficult and resource intensive task. One of the…
Android malware is one of the most dangerous threats on the internet, and it's been on the rise for several years. Despite significant efforts in detecting and classifying android malware from innocuous android applications, there is still…