Malware classification is a specific and refined task within the broader malware detection problem. Effective classification aids in understanding attack techniques and developing robust defenses, ensuring application security and timely mitigation of software vulnerabilities. The dynamic nature of malware demands adaptive classification techniques that can handle the continuous emergence of new families. Traditionally, this is done by retraining models on all historical samples, which requires significant resources in terms of time and storage. An alternative approach is Class-Incremental Learning (CIL), which focuses on progressively learning new classes (malware families) while preserving knowledge from previous training steps. However, CIL assumes that each class appears only once in training and is not revisited, an assumption that does not hold for malware families, which often persist across multiple time intervals. This leads to shifts in the data distribution for the same family over time, a challenge that is not addressed by traditional CIL methods. We formulate this problem as Temporal-Incremental Malware Learning (TIML), which adapts to these shifts and effectively classifies new variants. To support this, we organize the MalNet dataset, consisting of over a million entries of Android malware data collected over a decade, in chronological order. We first adapt state-of-the-art CIL approaches to meet TIML’s requirements, serving as baseline methods. Then, we propose a novel multimodal TIML approach that leverages multiple malware modalities for improved performance. Extensive evaluations show that our TIML approaches outperform traditional CIL methods and demonstrate the feasibility of periodically updating malware classifiers at a low cost. This process is efficient and requires minimal storage and computational resources, with only a slight dip in performance compared to full retraining with historical data.

Recent advancements in ML and DL have significantly improved Android malware detection, yet many methodologies still rely on basic static analysis, bytecode, or function call graphs that often fail to capture complex malicious behaviors. DexBERT, a pre-trained BERT-like model tailored for Android representation learning, enriches class-level representations by analyzing Smali code extracted from APKs. However, its functionality is constrained by its inability to process multiple Smali classes simultaneously. This paper introduces DetectBERT, which integrates correlated Multiple Instance Learning (c-MIL) with DexBERT to handle the high dimensionality and variability of Android malware, enabling effective app-level detection. By treating class-level features as instances within MIL bags, DetectBERT aggregates these into a comprehensive app-level representation. Our evaluation demonstrates that DetectBERT not only surpasses existing state-of-the-art detection methods but also adapts to evolving malware threats. Moreover, the versatility of the DetectBERT framework holds promising potential for broader applications in app-level analysis and other software engineering tasks, offering new avenues for research and development.

The popularity of Android OS has made it an appealing target for malware developers. To evade detection, including by ML-based techniques, attackers invest in creating malware that closely resemble legitimate apps, challenging the state of the art with difficult-to-detect samples. In this paper, we propose Guided Retraining, a supervised representation learning-based method for boosting the performance of malware detectors. To that end, we first split the experimental dataset into subsets of “easy” and “difficult” samples, where difficulty is associated to the prediction probabilities yielded by a malware detector. For the subset of “easy” samples, the base malware detector is used to make the final predictions since the error rate on that subset is low by construction. Our work targets the second subset containing “difficult” samples, for which the probabilities are such that the classifier is not confident on the predictions, which have high error rates. We apply our Guided Retraining method on these difficult samples to improve their classification. Guided Retraining leverages the correct predictions and the errors made by the base malware detector to guide the retraining process. Guided Retraining learns new embeddings of the difficult samples using Supervised Contrastive Learning and trains an auxiliary classifier for the final predictions. We validate our method on four state-of-the-art Android malware detection approaches using over 265k malware and benign apps. Experimental results show that Guided Retraining can boost state-of-the-art detectors by eliminating up to 45.19% of the prediction errors that they make on difficult samples. We note furthermore that our method is generic and designed to enhance the performance of binary classifiers for other tasks beyond Android malware detection.

Abstract Research on Android malware detection based on Machine learning has been prolific in recent years. In this paper, we show, through a large-scale evaluation of four state-of-the-art approaches that their achieved performance fluctuates when applied to different datasets. Combining existing approaches appears as an appealing method to stabilise performance. We therefore proceed to empirically investigate the effect of such combinations on the overall detection performance. In our study, we evaluated 22 methods to combine feature sets or predictions from the state-of-the-art approaches. Our results showed that no method has significantly enhanced the detection performance reported by the state-of-the-art malware detectors. Nevertheless, the performance achieved is on par with the best individual classifiers for all settings. Overall, we conduct extensive experiments on the opportunity to combine state-of-the-art detectors. Our main conclusion is that combining state-of-the-art malware detectors leads to a stabilisation of the detection performance, and a research agenda on how they should be combined effectively is required to boost malware detection. All artefacts of our large-scale study (i.e., the dataset of $\sim $ ∼ 0.5 million apks and all extracted features) are made available for replicability.

Every year, the VISion Understanding and Machine intelligence (VISUM) summer school runs a competition where participants can learn and share knowledge about Computer Vision and Machine Learning in a vibrant environment. 2021 VISUM's focused on applying those methodologies in fashion. Recently, there has been an increase of interest within the scientific community in applying computer vision methodologies to the fashion domain. That is highly motivated by fashion being one of the world's largest industries presenting a rapid development in e-commerce mainly since the COVID-19 pandemic. Computer Vision for Fashion enables a wide range of innovations, from personalized recommendations to outfit matching. The competition enabled students to apply the knowledge acquired in the summer school to a real-world problem. The ambition was to foster research and development in fashion outfit complementary product retrieval by leveraging vast visual and textual data with domain knowledge. For this, a new fashion outfit dataset (acquired and curated by FARFETCH) for research and benchmark purposes is introduced. Additionally, a competitive baseline with an original negative sampling process for triplet mining was implemented and served as a starting point for participants. The top 3 performing methods are described in this paper since they constitute the reference state-of-the-art for this particular problem. To our knowledge, this is the first challenge in fashion outfit complementary product retrieval. Moreover, this joint project between academia and industry brings several relevant contributions to disseminating science and technology, promoting economic and social development, and helping to connect early-career researchers to real-world industry challenges.

Native code is now commonplace within Android app packages where it co-exists and interacts with Dex bytecode through the Java Native Interface to deliver rich app functionalities. Yet, state-of-the-art static analysis approaches have mostly overlooked the presence of such native code, which, however, may implement some key sensitive, or even malicious, parts of the app behavior. This limitation of the state of the art is a severe threat to validity in a large range of static analyses that do not have a complete view of the executable code in apps. To address this issue, we propose a new advance in the ambitious research direction of building a unified model of all code in Android apps. The JuCify approach presented in this paper is a significant step towards such a model, where we extract and merge call graphs of native code and bytecode to make the final model readily-usable by a common Android analysis framework: in our implementation, JuCify builds on the Soot internal intermediate representation. We performed empirical investigations to highlight how, without the unified model, a significant amount of Java methods called from the native code are "unreachable" in apps' call-graphs, both in goodware and malware. Using JuCify, we were able to enable static analyzers to reveal cases where malware relied on native code to hide invocation of payment library code or of other sensitive code in the Android framework. Additionally, JuCify's model enables state-of-the-art tools to achieve better precision and recall in detecting data leaks through native code. Finally, we show that by using JuCify we can find sensitive data leaks that pass through native code.

Machine learning advances have been extensively explored for implementing large-scale malware detection. When reported in the literature, performance evaluation of machine learning based detectors generally focuses on highlighting the ratio of samples that are correctly or incorrectly classified, overlooking essential questions on why/how the learned models can be demonstrated as reliable. In the Android ecosystem, several recent studies have highlighted how evaluation setups can carry biases related to datasets or evaluation methodologies. Nevertheless, there is little work attempting to dissect the produced model to provide some understanding of its intrinsic characteristics. In this work, we fill this gap by performing a comprehensive analysis of a state-of-the-art Android malware detector, namely DREBIN, which constitutes today a key reference in the literature. Our study mainly targets an in-depth understanding of the classifier characteristics in terms of (1) which features actually matter among the hundreds of thousands that DREBIN extracts, (2) whether the high scores of the classifier are dependent on the dataset age, and (3) whether DREBIN’s explanations are consistent within malware families, among others. Overall, our tentative analysis provides insights into the discriminatory power of the feature set used by DREBIN to detect malware. We expect our findings to bring about a systematisation of knowledge for the community.

Abstract A well-known curse of computer security research is that it often produces systems that, while technically sound, fail operationally. To overcome this curse, the community generally seeks to assess proposed systems under a variety of settings in order to make explicit every potential bias. In this respect, recently, research achievements on machine learning based malware detection are being considered for thorough evaluation by the community. Such an effort of comprehensive evaluation supposes first and foremost the possibility to perform an independent reproduction study in order to sharpen evaluations presented by approaches’ authors. The question Can published approaches actually be reproduced? thus becomes paramount despite the little interest such mundane and practical aspects seem to attract in the malware detection field. In this paper, we attempt a complete reproduction of five Android Malware Detectors from the literature and discuss to what extent they are “reproducible”. Notably, we provide insights on the implications around the guesswork that may be required to finalise a working implementation. Finally, we discuss how barriers to reproduction could be lifted, and how the malware detection field would benefit from stronger reproducibility standards—like many various fields already have.