Donghui Zhao

Due to the rapid development and widespread presence of malware, deep-learning-based malware detection methods have become a pivotal approach used by researchers to protect private data. Behavior-based malware detection is effective, but changes in the running environment and malware evolution can alter API calls used for detection. Most existing methods ignore API call parameters while analyzing them separately, which loses important semantic information. Therefore, considering API call parameters and their combinations can improve behavior-based malware detection. To improve the effectiveness of behavior-based malware detection systems, this paper proposes a novel API feature engineering method. The proposed method employs parameter-augmented semantic chains to improve the system’s resilience to unknown parameters and elevate the detection rate. The method entails semantically decomposing the API to derive a behavior semantic chain, which provides an initial representation of the behavior exhibited by samples. To further refine the accuracy of the behavior semantic chain in depicting the behavior, the proposed method integrates the parameters utilized by the API into the aforementioned semantic chain. Furthermore, an information compression technique is employed to minimize the loss of critical actions following truncation of API sequences. Finally, a deep learning model consisting of gated CNN, Bi-LSTM, and an attention mechanism is used to extract semantic features embedded within the API sequences and improve the overall detection accuracy. Additionally, we evaluate the proposed method on a competition dataset Datacon2019. Experiments indicate that the proposed method outperforms baselines employing vocabulary-based methods in both robustness to unknown parameters and detection rate.

Machine learning-based malware (malicious software) detection methods have a wide range of real-world applications. However, these types of approaches suffer from the fatal problem of “model aging”, in which the validity of the model decreases rapidly as the malware continues to evolve and variants emerge continuously. The model aging problem is usually solved by model retraining, which relies on lots of labeled samples obtained at great expense. To address this challenge, this paper proposes a semi-supervised continuous learning malware detection model based on Transformer. Firstly, this model improves the lifelong semi-supervised mixture algorithm to dynamically adjust the weighted combination of new sample sequences and historical ones to solve the imbalance problem. Secondly, the Learning with Local and Global Consistency algorithm is used to iteratively compute similarity scores for the unlabeled samples in the mixed samples to obtain pseudo-labels. Lastly, the Multilayer Perceptron is applied for malware classification. To validate the effectiveness of the model, this paper conducts experiments on the CICMalDroid2020 dataset. The experimental results show that the proposed model performs better than existing deep learning detection models. The F1 score has an average improvement of 1.27% compared to other models when conducting binary classification. And, after inputting hybrid samples, including historical data and new data, four times, the F1 score is still 1.96% higher than other models.