The evolution of mobile malware poses a serious threat to smartphone security. Today, sophisticated attackers can adapt by maximally sabotaging machine-learning classifiers via polluting training data, rendering most recent machine learning-based malware detection tools (such as Drebin, DroidAPIMiner, and MaMaDroid) ineffective. In this paper, we explore the feasibility of constructing crafted malware samples; examine how machine-learning classifiers can be misled under three different threat models; then conclude that injecting carefully crafted data into training data can significantly reduce detection accuracy. To tackle the problem, we propose KuafuDet, a two-phase learning enhancing approach that learns mobile malware by adversarial detection. KuafuDet includes an offline training phase that selects and extracts features from the training set, and an online detection phase that utilizes the classifier trained by the first phase. To further address the adversarial environment, these two phases are intertwined through a self-adaptive learning scheme, wherein an automated camouflage detector is introduced to filter the suspicious false negatives and feed them back into the training phase. We finally show that KuafuDet can significantly reduce false negatives and boost the detection accuracy by at least 15 . Experiments on more than 250,000 mobile applications demonstrate that KuafuDet is scalable and can be highly effective as a standalone system.
Most recently, Chen @cite_0 suggest the use of semantic features of mobile apps to retain classifier value over time, building on the intuition that certain semantic attributes of mobile malware are invariant. Experiments verify that the incorporation of semantic features can significantly improve the performance of Android malware classification. Deo @cite_1 propose to assess the quality of binary classification by using probabilistic predictors. Although they both consider retraining, adversarial environment is missing.
@cite_0 @cite_1
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Automatic malware classifiers often perform badly on the detection of new malware, i.e., their robustness is poor. We study the machine-learning-based mobile malware classifiers and reveal one reason: the input features used by these classifiers can't capture general behavioural patterns of malware instances. We extract the best-performing syntax-based features like permissions and API calls, and some semantics-based features like happen-befores and unwanted behaviours, and train classifiers using popular supervised and semi-supervised learning methods. By comparing their classification performance on industrial datasets collected across several years, we demonstrate that using semantics-based features can dramatically improve robustness of malware classifiers. Malware evolves perpetually and relies on increasingly so- phisticated attacks to supersede defense strategies. Data-driven approaches to malware detection run the risk of becoming rapidly antiquated. Keeping pace with malware requires models that are periodically enriched with fresh knowledge, commonly known as retraining. In this work, we propose the use of Venn-Abers predictors for assessing the quality of binary classification tasks as a first step towards identifying antiquated models. One of the key benefits behind the use of Venn-Abers predictors is that they are automatically well calibrated and offer probabilistic guidance on the identification of nonstationary populations of malware. Our framework is agnostic to the underlying classification algorithm and can then be used for building better retraining strategies in the presence of concept drift. Results obtained over a timeline-based evaluation with about 90K samples show that our framework can identify when models tend to become obsolete.