arXiv subjects

Ali Muzaffar

Publications and source records attributed to Ali Muzaffar.

4 recordsLinked to original sources

arXiv2026

Thermodynamic Human-Computer Interaction

Target acquisition is often modeled separately for desktop, mobile, and other interaction modalities. We present Thermodynamic HCI, a framework that splits interaction into thermal equilibrium and non-equilibrium regimes. The theory generalizes across interaction modalities by representing agent-target interaction using kinetic and potential energies. We derive the movement time of Fitts' law and the speed-accuracy tradeoff observed in Schmidt's law from the principles of thermal physics. Furthermore, we develop theorems that describe how target properties, such as the color of a button, affect user accuracy. The target acquisition model, derived from the theory, when evaluated on desktop and mobile website prefetching experiments, achieved an accuracy of 98% for both cursor and touchscreen based interaction. For every clicked link, it produced a fetch:click ratio of 1.37 for desktop and 1.75 for mobile.

cs.HC↗

arXiv2024

ActDroid: An active learning framework for Android malware detection

The growing popularity of Android requires malware detection systems that can keep up with the pace of new software being released. According to a recent study, a new piece of malware appears online every 12 seconds. To address this, we treat Android malware detection as a streaming data problem and explore the use of active online learning as a means of mitigating the problem of labelling applications in a timely and cost-effective manner. Our resulting framework achieves accuracies of up to 96\%, requires as little of 24\% of the training data to be labelled, and compensates for concept drift that occurs between the release and labelling of an application. We also consider the broader practicalities of online learning within Android malware detection, and systematically explore the trade-offs between using different static, dynamic and hybrid feature sets to classify malware.

cs.CR↗

arXiv2023

DroidDissector: A Static and Dynamic Analysis Tool for Android Malware Detection

DroidDissector is an extraction tool for both static and dynamic features. The aim is to provide Android malware researchers and analysts with an integrated tool that can extract all of the most widely used features in Android malware detection from one location. The static analysis module extracts features from both the manifest file and the source code of the application to obtain a broad array of features that include permissions, API call graphs and opcodes. The dynamic analysis module runs on the latest version of Android and analyses the complete behaviour of an application by tracking the system calls used, network traffic generated, API calls used and log files produced by the application.

cs.CR↗

arXiv2023

Reassessing feature-based Android malware detection in a contemporary context

We report the findings of a reimplementation of 18 foundational studies in feature-based machine learning for Android malware detection, published during the period 2013-2023. These studies are reevaluated on a level playing field using a contemporary Android environment and a balanced dataset of 124,000 applications. Our findings show that feature-based approaches can still achieve detection accuracies beyond 98%, despite a considerable increase in the size of the underlying Android feature sets. We observe that features derived through dynamic analysis yield only a small benefit over those derived from static analysis, and that simpler models often out-perform more complex models. We also find that API calls and opcodes are the most productive static features within our evaluation context, network traffic is the most predictive dynamic feature, and that ensemble models provide an efficient means of combining models trained on static and dynamic features. Together, these findings suggest that simple, fast machine learning approaches can still be an effective basis for malware detection, despite the increasing focus on slower, more expensive machine learning models in the literature.

cs.LG↗