检索结果-内蒙古大学图书馆

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

来源：评论

学校读者我要写书评

暂无评论

A modified lightweight quantum convolutional neural network for malicious code detection

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

malicious code detection Based on code Semantic Features

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

malicious code detection on Smart Meters - A Side-Channel Based Approach 3

Malicious Code Detection on Smart Meters - A Side-Channel Ba...

引用

IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)

作者： Wang, Haixiang Li, Jiyuan Zhang, Tianchen Ying, Huan Han, Jiajia Ji, Xiaoyu Grad Sch China Elect Power Res Inst Beijing Peoples R China State Grid Zhejiang Elect Power Res Inst Hangzhou Zhejiang Peoples R China Zhejiang Univ Coll Elect Engn Hangzhou Zhejiang Peoples R China China Elect Power Res Inst Informat & Commun Dept Beijing Peoples R China

ISBN: (纸本)9781538662434

With the continuous development of smart grids, smart meter, one of the typical embedded equipment, is also becoming more intellectualized. Smart meters nowadays are capable of not only recording power consumption, but also uploading information to the electricity supplier for monitoring and billing, which makes the electric power dispatching and usage more efficient and convenient. However, the enhanced functions and computation power of the smart meter renders it more valuable and vulnerable for the adversary to conduct a cyber attack. One of the most common and important security threats of the smart meter is the malicious code due to its widespread and the easy access for adversaries. Therefore, it is of great criticalness to detect malicious code in the smart meters to safeguard the security of smart terminals. In this paper, we propose a novel approach that detects malicious code in embedded terminals, e.g., smart meters. with a Long Short Term Memory (LSTM) network based on the power consumption of the CPU or MCU of the device. The evaluation results demonstrate that the proposed method can effectively detect the malicious code in smart meters with an accuracy as high as 92%.

关键词： smart meter side-channel malicious code detection neural network

来源：评论

学校读者我要写书评

暂无评论

A hybrid quantum ensemble learning model for malicious code detection

引用

QUANTUM SCIENCE AND TECHNOLOGY 2024年第3期9卷

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

来源：评论

学校读者我要写书评

暂无评论

A Hybrid malicious code detection Method based on Deep Learning

引用

INTERNATIONAL JOURNAL OF SECURITY AND ITS APPLICATIONS 2015年第5期9卷 205-215页

作者： Li, Yuancheng Ma, Rong Jiao, Runhai North China Elect Power Univ Sch Control & Comp Engn Beijing Peoples R China

In this paper;we propose a hybrid malicious code detection scheme based on AutoEncoder and DBN (Deep Belief Networks). Firstly, we use the AutoEncoder deep learning method to reduce the dimensionality of data. This could convert complicated high-dimensional data into low dimensional codes with the nonlinear mapping, thereby reducing the dimensionality of data, extracting the main features of the data;then using DBN learning method to detect malicious code. DBN is composed of multilayer Restricted Boltzmann Machines (RBM, Restricted Boltzmann Machine) and a layer of BP neural network. Based on unsupervised training of every layer of RBM, we make the output vector of the last layer of RBM as the input vectors of BP neural network, then conduct supervised training to the BP neural network, finally achieve the optimal hybrid model by fine-tuning the entire network. After inputting testing samples into the hybrid model, the experimental results show that the detection accuracy getting by the hybrid detection method proposed in this paper is higher than that of single DBN. The proposed method reduces the time complexity and has better detection performance.

关键词： malicious code detection AutoEncoder DBN RBM deep learning

来源：评论

学校读者我要写书评

暂无评论

malicious code detection based on many-objective transfer model

引用

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

来源：评论

学校读者我要写书评

暂无评论

Immunity-Based Model for malicious code detection

Immunity-Based Model for Malicious Code Detection

引用

6th International Conference on Intelligent Computing

作者： Zhang, Yu Wu, Lihua Xia, Feng Liu, Xiaowen Hainan Normal Univ Coll Informat Sci & Technol Haikou 571158 Peoples R China

ISBN: (纸本)9783642149214

More and more unknown malware that hide itself in the operating system kernel make the traditional antivirus difficult to detect Inspired by the biological immune system we proposed a novel immunity inspired model for malware detection-IMD The IMD model extracts the 110 Request Packets (IRPs) sequence produced by the process running in kernel mode as antigen de fines the normal benign programs as self programs and defines the malwares as nonself programs By the process behavior monitoring and the family gene analysis the model can monitor the evolution of malware The model generates the immature antibodies by vaccination produces mature antibodies by clonal selection and gene evolution and then learns and evolution try identifies the unknown malware by the m Lane antibodies Experiments show that the pro posed model for unknown malware detection has high detection rate, low false-positive rate and low omission late

关键词： malicious code detection artificial immune system IRPs sequence antivirus

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：