Related papers: Can We Trust Your Explanations? Sanity Checks for …

Explainability, i.e. the ability of a system to explain its behavior to users, has become an important quality of software-intensive systems. Recent work has focused on methods for generating explanations for various algorithmic paradigms…

Machine learning models are increasingly being adopted across various fields, such as medicine, business, autonomous vehicles, and cybersecurity, to analyze vast amounts of data, detect patterns, and make predictions or recommendations. In…

Malware detection in Android systems requires both cybersecurity expertise and machine learning (ML) techniques. Automated Machine Learning (AutoML) has emerged as an approach to simplify ML development by reducing the need for specialized…

As the popularity of Android smart phones has increased in recent years, so too has the number of malicious applications. Due to the potential for data theft mobile phone users face, the detection of malware on Android devices has become an…

To cope with the increasing variability and sophistication of modern attacks, machine learning has been widely adopted as a statistically-sound tool for malware detection. However, its security against well-crafted attacks has not only been…

The widespread use of smartphones in daily life has raised concerns about privacy and security among researchers and practitioners. Privacy issues are generally highly prevalent in mobile applications, particularly targeting the Android…

With over 50 billion downloads and more than 1.3 million apps in the Google official market, Android has continued to gain popularity amongst smartphone users worldwide. At the same time there has been a rise in malware targeting the…

The battle to mitigate Android malware has become more critical with the emergence of new strains incorporating increasingly sophisticated evasion techniques, in turn necessitating more advanced detection capabilities. Hence, in this paper…

Coping with malware is getting more and more challenging, given their relentless growth in complexity and volume. One of the most common approaches in literature is using machine learning techniques, to automatically learn models and…

Smartphones have become an intrinsic part of human's life. The smartphone unifies diverse advanced characteristics. It enables users to store various data such as photos, health data, credential bank data, and personal information. The…

Mobile malware has continued to grow at an alarming rate despite on-going efforts towards mitigating the problem. This has been particularly noticeable on Android due to its being an open platform that has subsequently overtaken other…

This paper introduces a malware detection system for smartphones based on studying the dynamic behavior of suspicious applications. The main goal is to prevent the installation of the malicious software on the victim systems. The approach…

Malware detection and analysis are active research subjects in cybersecurity over the last years. Indeed, the development of obfuscation techniques, as packing, for example, requires special attention to detect recent variants of malware.…

The rapid growth of mobile applications has escalated Android malware threats. Although there are numerous detection methods, they often struggle with evolving attacks, dataset biases, and limited explainability. Large Language Models…

In recent years, there has been a significant surge in malware attacks, necessitating more advanced preventive measures and remedial strategies. While several successful AI-based malware classification approaches exist categorized into…

Recently many efforts have been devoted to interpreting the black-box NMT models, but little progress has been made on metrics to evaluate explanation methods. Word Alignment Error Rate can be used as such a metric that matches human…

There is little information from independent sources in the public domain about mobile malware infection rates. The only previous independent estimate (0.0009%) [12], was based on indirect measurements obtained from domain name resolution…

The rising use of Large Language Models (LLMs) to create and disseminate malware poses a significant cybersecurity challenge due to their ability to generate and distribute attacks with ease. A single prompt can initiate a wide array of…

It is needed to ensure the integrity of systems that process sensitive information and control many aspects of everyday life. We examine the use of machine learning algorithms to detect malware using the system calls generated by…

Evaluating explanations of image classifiers regarding ground truth, e.g. segmentation masks defined by human perception, primarily evaluates the quality of the models under consideration rather than the explanation methods themselves.…