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SMCD: Privacy-preserving deep learning based malicious code detection

COMPUTERS & SECURITY 2025年 150卷

作者： Mu, Gaoli Zhang, Hanlin Lin, Jie Kong, Fanyu Qingdao Univ Coll Comp Sci & Technol Qingdao 266071 Peoples R China Xi An Jiao Tong Univ Sch Comp Sci & Technol Xian 710049 Peoples R China Shandong Univ Sch Comp Sci & Technol Jinan 250100 Peoples R China

With the rapid development of the Internet, malicious code has been continuously exposing security issues, posing a significant threat to people's online lives. Deep learning has shown significant impact in the field of malicious code detection, multiple providers of malicious code data can offer more diverse data for deep learning, thereby improving the accuracy of malicious code detection models. However, this may raise privacy and security concerns regarding the training data and models. To address this challenge, our paper introduces an advanced, secure deep learning framework collaboratively trained across multiple parties. We first use privacy set intersection techniques to align the provided malicious code data from the participants, ensuring that they have the same attributes. The aligned data from each data provider is then securely shared with three cloud servers through secret sharing. The three cloud servers implemented a secure model training process through secure multiparty computation. Our experiment demonstrates that our secure malicious code detection protocol exhibits satisfactory performance.

关键词： malicious code detection Secret sharing Secure multi-party computation Privacy set intersection Deep learning

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A modified lightweight quantum convolutional neural network for malicious code detection

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QUANTUM SCIENCE AND TECHNOLOGY 2025年第1期10卷 015007-015007页

作者： Xiong, Qibing Fei, Yangyang Du, Qiming Zhao, Bo Di, Shiqin Shan, Zheng Informat Engn Univ Zhengzhou 450000 Peoples R China Henan Police Coll Zhengzhou 450000 Peoples R China

Quantum neural network fully utilize the respective advantages of quantum computing and classical neural network, providing a new path for the development of artificial intelligence. In this paper, we propose a modified lightweight quantum convolutional neural network (QCNN), which contains a high-scalability and parameterized quantum convolutional layer and a quantum pooling circuit with quantum bit multiplexing, effectively utilizing the computational advantages of quantum systems to accelerate classical machine learning tasks. The experimental results show that the classification accuracy (precision, F1-score) of this QCNN on DataCon2020, Ember and BODMAS have been improved to 96.65% (94.3%, 96.74%), 92.4% (91.01%, 92.53%) and 95.6% (91.99%, 95.78%), indicating that this QCNN has strong robustness as well as good generalization performance for malicious code detection, which is of great significance to cyberspace security.

关键词： quantum computing quantum machine learning quantum convolutional neural network malicious code detection

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Review of malicious code detection in data mining applications: challenges, algorithms, and future direction

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2025年第3期28卷 1-37页

作者： Razaque, Abdul Bektemyssova, Gulnara Yoo, Joon Hariri, Salim Khan, Meer Jaro Nalgozhina, Nurgul Hwang, Jaeryong Khan, M. Ajmal Gachon Univ Sch Comp Seongnam Gyeonggi Do South Korea Int Informat Technol Univ Dept Comp Engn Manas 34-1 Alma Ata Kazakhstan Univ Arizona Dept Elect & Comp Engn Tucson AZ USA ROK Naval Acad Dept Cyber Sci Chang Won South Korea Ohio Northern Univ Dept Elect & Comp Engn Ada OH 45810 USA Satbayev Univ Int Dept Alma Ata Kazakhstan

In an era where machine learning critically underpins business operations, detecting vulnerabilities introduced by malicious code has become increasingly essential. Although prior research has extensively explored malicious code within machine learning algorithms, a targeted analysis specifically designed to identify and address these threats remains necessary. This paper presents an exhaustive literature review, focusing on the key processes of insertion, recognition, decision-making, and selection of malicious codes. We aim to uncover architectural weaknesses in data mining applications that amplify system vulnerabilities. Leveraging an integrative review covering publications from 2008 to 2024, we synthesize insights from a diverse array of academic and digital sources, examining 167 pertinent articles. This rigorous approach reveals the nuanced effects of malicious code on feature selection algorithms, crucial for maintaining data integrity. Our findings indicate that malicious code can significantly disrupt various sectors, including industrial, telecommunications, and biological data mining, adversely affecting clustering, classification, and regression algorithms. However, an encouraging outcome is observed in advanced feature selection algorithms that demonstrate resilience by effectively filtering out irrelevant data inputs. The paper concludes with a strong call for the development of sophisticated detection methods, which are vital for mitigating the growing risks associated with malicious code. It stresses the importance of proactive algorithm identification and classification to preserve the efficacy of data mining. Current challenges in accurately classifying machine learning algorithms raise concerns about data privacy, security, and potential biases. Ongoing research is crucial for improving data interoperability and algorithm transparency, thereby strengthening the defense mechanisms of machine learning applications against the complex and evolving

关键词： malicious code detection Data mining Vulnerabilities Cyber threats Industrial applications

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A hybrid quantum ensemble learning model for malicious code detection

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QUANTUM SCIENCE AND TECHNOLOGY 2024年第3期9卷 035021-035021页

作者： Xiong, Qibing Ding, Xiaodong Fei, Yangyang Zhou, Xin Du, Qiming Feng, Congcong Shan, Zheng Informat Engn Univ Zhengzhou 450000 Peoples R China Henan Police Coll Zhengzhou 450000 Peoples R China

Quantum computing as a new computing model with parallel computing capability and high information carrying capacity, has attracted a lot of attention from researchers. Ensemble learning is an effective strategy often used in machine learning to improve the performance of weak classifiers. Currently, the classification performance of quantum classifiers is not satisfactory enough due to factors such as the depth of quantum circuit, quantum noise, and quantum coding method, etc. For this reason, this paper combined the ensemble learning idea and quantum classifiers to design a novel hybrid quantum machine learning model. Firstly, we run the Stacking method in classical machine learning to realize the dimensionality reduction of high-latitude data while ensuring the validity of data features. Secondly, we used the Bagging method and Bayesian hyperparameter optimization method applied to quantum support vector machine (QSVM), quantum K nearest neighbors (QKNN), variational quantum classifier (VQC). Thirdly, the voting method is used to ensemble the predict results of QSVM, QKNN, VQC as the final result. We applied the hybrid quantum ensemble machine learning model to malicious code detection. The experimental results show that the classification precision (accuracy, F1-score) of this model has been improved to 98.9% (94.5%, 94.24%). Combined with the acceleration of quantum computing and the higher precision rate, it can effectively deal with the growing trend of malicious codes, which is of great significance to cyberspace security.

关键词： quantum computing machine learning hybrid quantum ensemble learning malicious code detection

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A Full-Lifecycle malicious code detection Scheme Based on RASP and Random Forest 20th

A Full-Lifecycle Malicious Code Detection Scheme Based on RA...

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20th International Conference on Intelligent Computing (ICIC)

作者： Cheng, Jiameng Zhang, Chenhao Zhang, Hongwei Wang, Cong Wang, Jinsong Tianjin Univ Technol Sch Comp Sci & Engn Tianjin 300384 Peoples R China Tianjin Univ Sci & Technol Sch Comp Sci & Engn Tianjin 300457 Peoples R China

ISBN: (纸本)9789819756650;9789819756667

As cloud computing continues to evolve, computational power leasing has emerged as a novel web service model, providing users with access to computing resources or cloud computing capabilities. This enables users to offload their computational tasks to remote devices and retrieve results. However, with the exponential growth in data generation, cloud service providers are confronted with the critical challenge of differentiating malicious code within a vast array of computing tasks. To address this issue, this paper proposes a comprehensive lifecycle malicious code detection framework that integrates Runtime Application Self-Protection (RASP) with random forest technology, facilitating rapid and accurate identification of malicious code. Experimental results demonstrate that the intelligent detection process using random forest yields better performance compared to other machine learning algorithms. By training the intelligent detection model with features selected in this paper, a high accuracy rate of up to 95.10% is achieved on the collected G4 sample set. Additionally, the proposed framework achieves the highest accuracy rate among other schemes on the G1 sample set, reaching 98.07%. This research offers an effective security measure for computational power leasing providers in this domain.

关键词： malicious code detection Random forest RASP Computing power leasing

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malicious code detection based on many-objective transfer model

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2023年第22期35卷 e7728-e7728页

作者： Zhang, Binquan Wu, Di Lan, Zhuoxuan Cui, Zhihua Xie, Liping Taiyuan Univ Sci & Technol Shanxi Key Lab Big Data Anal & Parallel Comp Taiyuan 030024 Peoples R China Beijing Univ Technol Coll Comp Sci & Technol Beijing Peoples R China

With the rapid growth of malicious codes, personal privacy, and Internet security are seriously threatened. Existing transfer learning-based malicious code detection improves detection accuracy by transferring pre-trained neural networks. However, it cannot efficiently tune the structure and parameters of the neural networks. Here, we first propose a novel many-objective transfer model. It mainly focuses on the detection accuracy and the total number of parameters of the neural network model. The optimal structure and parameters are captured from the pre-trained neural network by many-objective optimization algorithm. Second, the partitioned crossover-mutation vector angle-based evolutionary algorithm for unconstrained many-objective optimization is proposed to solve the model. The algorithm performs crossover mutation operations in different ways on different regions of the candidate solution to improve population diversity. The simulation results show that the model can reduce the pre-trained neural network structure by 49% while maintaining the accuracy in malicious code detection.

关键词： deep learning malicious code detection many-objective optimization transfer learning

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malicious code detection Based on code Semantic Features

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IEEE ACCESS 2020年 8卷 176728-176737页

作者： Zhang, Yu Li, Binglong Informat Engn Univ Coll Cryptog Engn Zhengzhou 450000 Peoples R China

With the development of smart phones, malicious applications for the Android platform have increased dramatically. The existing Android malicious code analysis methods majorly focus on detection based on signatures, inter-component communication, and other configuration information features. Such methods ignore the effect of the semantic features of the malicious code. Even a few such studies that exist are based on the statistical features of the code for malicious code detection. To address these shortcomings, we (1) use the code semantic structure features to reflect deep semantic information, (2) propose a preprocessing method of APK files to generate graphics that reflect the code semantic features, and (3) introduce the advanced graphical semantics for a graph convolutional network (GCN) model to automatically identify and learn semantics and extract features for malicious code detection. Experiments on a dataset confirm that the proposed method can achieve 95.8% detection accuracy. Compared with the existing methods that adopt configuration information features or statistical features of codes, our method shows higher accuracy.

关键词： Feature extraction Malware Semantics Smart phones Data mining Machine learning Convolutional codes Deep learning malicious code detection semantic features

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malicious code detection for Trusted Execution Environment Based on Paillier Homomorphic Encryption

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IEICE TRANSACTIONS ON COMMUNICATIONS 2020年第3期E103B卷 155-166页

作者： Wang, Ziwang Zhuang, Yi Nanjing Univ Aeronaut & Astronaut Dept Comp Sci & Technol Nanjing Peoples R China

Currently, mobile terminals face serious security threats. A Trusted Execution Environment (TEE) which can provide an isolated execution environment for sensitive workloads, is seen as a trusted relay for providing security services for any mobile application. However, mobile TEE's architecture design and implementation strategy are not unbreakable at present. The existing researches lack of detect mechanisms for attack behaviour and malicious software. This paper proposes a malicious code detection scheme for Trusted Execution Environment based on Homomorphic Encryption (HE-TEEMD), which is a novel detection mechanism for data and code in the trusted execution environment. HE-TEEMD uses the Paillier additive homomorphic algorithm to implement the signature matching and transmits the ciphertext information generated in the TEE to the normal world for detection by the homomorphism and randomness of the homomorphic encryption ciphertext. An experiment and security analysis proves that our scheme can achieve malicious code detection in the secure world with minimal cost. Furthermore, evaluation parameters are introduced to address the known plaintext attack problem of privileged users.

关键词： trusted execution environment malicious code detection mobile security

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A malicious code detection Method Based on Stacked Depthwise Separable Convolutions and Attention Mechanism

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SENSORS 2023年第16期23卷 7084-7084页

作者： Huang, Hong Du, Rui Wang, Zhaolian Li, Xin Yuan, Guotao Sichuan Univ Sci & Engn Sch Comp Sci & Engn Yibin 644002 Peoples R China

To address the challenges of weak model generalization and limited model capacity adaptation in traditional malware detection methods, this article presents a novel malware detection approach based on stacked depthwise separable convolutions and self-attention, termed CoAtNet. This method combines the strengths of the self-attention module's robust model adaptation and the convolutional networks' powerful generalization abilities. The initial step involves transforming the malicious code into grayscale images. These images are subsequently processed using a detection model that employs stacked depthwise separable convolutions and an attention mechanism. This model effectively recognizes and classifies the images, automatically extracting essential features from malicious software images. The effectiveness of the method was validated through comparative experiments using both the Malimg dataset and the augmented Blended+ dataset. The approach's performance was evaluated against popular models, including XceptionNet, EfficientNetB0, ResNet50, VGG16, DenseNet169, and InceptionResNetV2. The experimental results highlight that the model surpasses other malware detection models in terms of accuracy and generalization ability. In conclusion, the proposed method addresses the limitations of traditional malware detection approaches by leveraging stacked depthwise separable convolutions and self-attention. Comprehensive experiments demonstrate its superior performance compared to existing models. This research contributes to advancing the field of malware detection and provides a promising solution for enhanced accuracy and robustness.

关键词： deep learning malicious code detection neural networks attention mechanism data augmentation CoAtNet model

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A malicious code detection Method Based on FF-MICNN in the Internet of Things

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SENSORS 2022年第22期22卷 8739页

作者： Zhang, Wenbo Feng, Yongxin Han, Guangjie Zhu, Hongbo Tan, Xiaobo Shenyang Ligong Univ Sch Informat Sci & Engn Shenyang 110159 Peoples R China Hohai Univ Dept Informat & Commun Syst Changzhou 213022 Peoples R China

It is critical to detect malicious code for the security of the Internet of Things (IoT). Therefore, this work proposes a malicious code detection algorithm based on the novel feature fusion-malware image convolutional neural network (FF-MICNN). This method combines a feature fusion algorithm with deep learning. First, the malicious code is transformed into grayscale image features by image technology, after which the opcode sequence features of the malicious code are extracted by the n-gram technique, and the global and local features are fused by feature fusion technology. The fused features are input into FF-MICNN for training, and an appropriate classifier is selected for detection. The results of experiments show that the proposed algorithm exhibits improvements in its detection speed, the comprehensiveness of features, and accuracy as compared with other algorithms. The accuracy rate of the proposed algorithm is also 0.2% better than that of a detection algorithm based on a single feature.

关键词： IoT malicious code detection classification detection of images improved convolutional neural network FF-MICNN

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