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DroidMD: An efficient and scalable Android malware detection approach at source code level

International Journal of information and Computer security 2021年第2-3期15卷 299-321页

作者： Akram, Junaid Mumtaz, Majid Jabeen, Gul Luo, Ping The Key State Laboratory of Information Security School of Software Engineering Tsinghua University 100086 China

security researchers and anti-virus industries have speckled stress on an Android malware, which can actually damage your phones and threatens the Android markets. In this paper, we propose and develop DroidMD, a scalable self-improvement based tool, based on auto optimisation of signature set, which detect malicious apps in the market at source code level. A prototype has been developed tested and implemented to detect malware in applications. We implement and evaluate our approach on almost 30,000 applications including 27,000 benign and 3,670 malware applications. DroidMD detects malware in different applications at partial level and full level. It analyses only the applications code, which increase its reliability. Our evaluation of DroidMD demonstrates that our approach is very efficient in detecting malware at large scale with high accuracy of 95.5%. © 2021 Inderscience Enterprises Ltd.

关键词： Mobile security

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An improved cryptanalysis of large RSA decryption exponent with constrained secret key

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International Journal of information and Computer security 2021年第2期14卷 102-117页

作者： Mumtaz, Majid Ping, Luo Key Laboratory for Information Systems Security School of Software Tsinghua University Beijing100084 China

In this study, we revisit the RSA public key cryptosystem in some special case of Boneh and Durfee's attack when the private key d assumes to be larger than the public key e. The attack in this study is the variation of an approach adopted by Luo et al. (2009) based on large decryption exponent. They had chosen a large private key (d > e) and found the weak keys in some specific range between N0.258 ≤ e ≤ N0.857. We highlight the shortcomings and new improvements in our study with more refined bound analysis up to the range between N0.104 ≤ e ≤ N0.923. Our experimental results revealed more refined bounds using lattice-based Coppersmith's method. In our experimental yield, we find the small roots of the devised polynomial, which helps to factorise the RSA modulus of size up to 1,024-bits. We also measure the probability of a specific range of weak keys, which further certify our results about weak keys in an RSA constrained secret key environment. Copyright © 2021 Inderscience Enterprises Ltd.

关键词： Public key cryptography

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FreqSense: Universal and Low-Latency Adversarial Example Detection for Speaker Recognition with Interpretability in Frequency Domain

FreqSense: Universal and Low-Latency Adversarial Example Det...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Huang, Yihuan Li, Yuanzhe Ren, Yanzhen Tu, Weiping Yang, Yuhong Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education China School of Cyber Science and Engineering Wuhan University China National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University China

ISBN: (纸本)9798350368741

Speaker recognition (SR) systems are particularly vulnerable to adversarial example (AE) attacks. To mitigate these attacks, AE detection systems are typically integrated into SR systems. To overcome the limitations of low detection accuracy, poor generalization, and high latency in existing schemes, this paper proposes FreqSense, an AE detection scheme based on frequency distribution features. FreqSense detects a variety of unknown AE attacks with low latency, and provides interpretability in its detection process. The basic idea of FreqSense is that AE typically introduce carefully designed noise in specific frequency bands that are associated with highly distinctive speaker identities. Therefore, leveraging the distributional variations in these frequency bands can effectively distinguish between AE and benign audio. FreqSense models frequency distribution features by integrating time-frequency transformation technology with a self-attention mechanism and employs a neural network-based classifier to distinguish between AE and benign audio. Experimental results show that FreqSense achieves an overall detection accuracy of 99.2%, surpassing state-of-the-art (SOTA) schemes by 27.2%. When confronting unknown AE attacks, FreqSense achieves a detection accuracy of 98.3% with a latency of just 0.0014 seconds. © 2025 IEEE.

关键词： Adversarial Attack Adversarial Example Detection Speaker Recognition

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A Reversible Data Hiding in Encryption Domain for JPEG Image Based on Controllable Ciphertext Range of Paillier Homomorphic Encryption Algorithm 21st

A Reversible Data Hiding in Encryption Domain for JPEG Ima...

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21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Ma, Bin Zhao, Chunxin Ma, Ruihe Xian, Yongjin Wang, Chunpeng Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China School of Economics Jilin University Changchun130015 China

ISBN: (纸本)9789819601219

With the rapid advancement of cloud computing, significant security concerns regarding data storage have been continuously raised. When the encrypted images are stored in the cloud, reversible data hiding in encrypted domain is used to embed additional data to manage the storage resources in the cloud. Most of the current works are applied to uncompressed images rather than JPEG images. Therefore, this paper proposes a reversible data hiding algorithm for JPEG images based on an enhanced Paillier homomorphic encryption algorithm. In the proposed scheme, the AC coefficients are altered by using a ciphertext distribution constrained paillier homomorphic encryption algorithm, so as to reduce the size of the data-embedded encrypted image. Whereas, the DC coefficients are encrypted by using the one-dimensional tangent chaos encryption algorithm, thereby ensuring high security of the encrypted image. Moreover, according to the characteristics of the homomorphic encryption algorithm, the confidential data can be recovered from both the encryption domain and from the plain domain. Experimental results demonstrate that the ciphertext distribution-constrained Paillier homomorphic encryption algorithm obtains higher data embedding capacity and superior security in comparison with other advanced algorithms. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Encryption algorithms

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Detecting Double Mixed Compressed Images Based on Quaternion Convolutional Neural Network

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Chinese Journal of Electronics 2024年第3期33卷 657-671页

作者： Hao WANG Jinwei WANG Xuelong HU Bingtao HU Qilin YIN Xiangyang LUO Bin MA Jinsheng SUN Department of Automation Nanjing University of Science and Technology Engineering Research Center of Digital Forensics Ministry of Education Department of Computer and Software Nanjing University of Information Science and Technology School of Management Nanjing University of Posts and Telecommunications Department of Computer Science and Engineering Guangdong Province Key Laboratory of Information Security Technology Sun Yat-sen University State Key Laboratory of Mathematical Engineering and Advanced Computing Qilu University of Technology

Detection of color images that have undergone double compression is a critical aspect of digital image *** the existence of various methods capable of detecting double Joint Photographic Experts Group(JPEG) compression,they are unable to address the issue of mixed double compression resulting from the use of different compression *** particular,the implementation of Joint Photographic Experts Group 2000(JPEG2000)as the secondary compression standard can result in a decline or complete loss of performance in existing *** tackle this challenge of JPEG+JPEG2000 compression,a detection method based on quaternion convolutional neural networks(QCNN) is *** QCNN processes the data as a quaternion,transforming the components of a traditional convolutional neural network(CNN) into a quaternion *** relationships between the color channels of the image are preserved,and the utilization of color information is ***,the method includes a feature conversion module that converts the extracted features into quaternion statistical features,thereby amplifying the evidence of double *** results indicate that the proposed QCNN-based method improves,on average,by 27% compared to existing methods in the detection of JPEG+JPEG2000 compression.

关键词： Color image forensics JPEG JPEG2000 Mixed double compression Quaternion convolutional neural network

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Robust Network For Segmenting Roof Planes From Sparse Point Clouds 36

Robust Network For Segmenting Roof Planes From Sparse Point ...

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36th International Conference on software Engineering and Knowledge Engineering, SEKE 2024

作者： Fu, Hang Zeng, Cheng Key Laboratory of Intelligent Sensing System and Security Hubei University Ministry of Education Wuhan China School of Computer and Information Engineering Hubei University Wuhan China School of Artificial Intelligence Hubei University Wuhan China

ISBN: (纸本)1891706594

The plane is the fundamental characteristic for describing the shape of polyhedral buildings. Roof plane segmentation of airborne LiDAR point clouds is an important step in 3D building model reconstruction. Existing methods are either non-automatic and efficient traditional methods, or hindered by the lack of datasets for roof plane segmentation and therefore fail to achieve good training for deep learning methods. To address the above issues, we proposes an end-to-end network called PUTr for roof plane segmentation of sparse point clouds. This network integrates a Point-cloud Upsampling module and Transformer to ensure effective generalization even when trained on low-density datasets, while also capturing dependency relationships within point cloud neighborhoods. Our model overcomes the low automation and manual intervention of existing methods, and the limited generalization of deep learning due to scarce high-quality datasets. Evaluations also demonstrate its effectiveness in roof plane segmentation of laser point cloud data. © 2024 Knowledge systems Institute Graduate school. All rights reserved.

关键词： 3D reconstruction

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Smart Contract Vulnerability Detection Based on Mixed Channel Attention 36

Smart Contract Vulnerability Detection Based on Mixed Channe...

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36th International Conference on software Engineering and Knowledge Engineering, SEKE 2024

作者： Wang, Jiao Ran Zeng, Cheng Ba, Kai Yang Quan, Qing Yu Wang, Yi Feng School of Computer Science and Information Engineering Hubei University Wuhan China School of Artificial Intelligence Hubei University Wuhan China School of Cyberspace Security Hubei University Wuhan China Key Laboratory of Intelligence Sensing System and Security Hubei University Ministry of Education Wuhan China

ISBN: (纸本)1891706594

As blockchain technology progresses, the deployment of smart contracts has become increasingly prevalent. Despite their growing popularity, smart contracts introduce substantial security risks and vulnerabilities. Present detection methodologies predominantly analyze the entirety of a contract’s source code, a process that tends to indiscriminately extract uniform features, leading to inadequate detection capabilities. Addressing these concerns, we propose a novel vulnerability detection method for smart contracts, predicated on a mixed channel attention mechanism. This approach focuses on extracting multifaceted features from pivotal code segments of smart contracts, utilizing a combination of spatial and frequency domain channel attention mechanisms. These mechanisms dynamically modulate the weights of feature channels during visualization, allowing the model to allocate greater emphasis to features of higher significance and enhance feature differentiation. This strategy is instrumental in capturing salient information within the data, thereby augmenting the model’s performance and accuracy. Empirical results substantiate that our proposed method substantially surpasses the baseline model, evidencing improvements of 5.55%, 5.42%, and 5.49% in the detection of timestamp vulnerabilities, and 4.12%, 4.29%, and 4.20% in the identification of reentrancy vulnerabilities, respectively. Our method offers a significant advancement in the efficacy of smart contract vulnerability detection and presents valuable implications for future research. © 2024 Knowledge systems Institute Graduate school. All rights reserved.

关键词： channel attention key code slicing multiple features smart contracts vulnerability detection

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Blockchain-Based Ciphertext Policy-Hiding Access Control Scheme 1

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18th EAI International Conference on security and Privacy in Communication Networks, SecureComm 2022

作者： Du, Ruizhong Zhang, Tianhe School of Cyber Security and Computer Hebei University Baoding071002 China Hebei Provincial Key Laboratory of High Credibility Information System Baoding071000 China

ISBN: (数字)9783031255380

ISBN: (纸本)9783031255373

Ciphertext policy attribute encryption(CP-ABE) can realize one-to-many fine-grained access control, which can effectively solve the security problem of shared data. However, most existing CP-ABE protocols exhibit a single point of failure and access policy privacy disclosure problems. To resolve these issues, a blockchain-based ciphertext policy-hiding access control scheme is proposed. First, we propose a vector generation algorithm using vector compression techniques that can improve the efficiency of our scheme. Then, the privacy policy is protected by combining CP-ABE and inner product encryption. We then solve the user attribute revocation problem and single point of failure in the cloud-based access control models using ethereum. Finally, security analysis, the theoretical results, and experimental results show that the proposed solution is secure and efficient. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Access control

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Research on active defense decision-making method for cloud boundary networks based on reinforcement learning of intelligent agent

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High-Confidence Computing 2024年第2期4卷 50-61页

作者： Huan Wang Yunlong Tang Yan Wang Ning Wei Junyi Deng Zhiyan Bin Weilong Li School of Computer Science and Technology Guangxi University of Science and TechnologyLiuzhou 545000China Liuzhou Key Laboratory of Big Data Intelligent Processing and Security Liuzhou 545000China Guangxi Education System Network Security Monitoring Center Liuzhou 545000China Center of Graphics and Information Guangxi ECO-Engineering Vocational&Technical CollegeLiuzhou 545004China

The cloud boundary network environment is characterized by a passive defense strategy,discrete defense actions,and delayed defense feedback in the face of network attacks,ignoring the influence of the external environment on defense decisions,thus resulting in poor defense ***,this paper proposes a cloud boundary network active defense model and decision method based on the reinforcement learning of intelligent agent,designs the network structure of the intelligent agent attack and defense game,and depicts the attack and defense game process of cloud boundary network;constructs the observation space and action space of reinforcement learning of intelligent agent in the non-complete information environment,and portrays the interaction process between intelligent agent and environment;establishes the reward mechanism based on the attack and defense gain,and encourage intelligent agents to learn more effective defense *** designed active defense decision intelligent agent based on deep reinforcement learning can solve the problems of border dynamics,interaction lag,and control dispersion in the defense decision process of cloud boundary networks,and improve the autonomy and continuity of defense decisions.

关键词： Active defense decision-making Cloud boundary network security Intelligent agent reinforcement learning Offensive and defensive game

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An Enhanced Fusion Model for Android Malware Detection Leveraging Multi-Code Fragment Features and Fuzzy Hashing 20

An Enhanced Fusion Model for Android Malware Detection Lever...

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20th International Computer Conference on Wavelet Active Media Technology and information Processing, ICCWAMTIP 2023

作者： Lingrong, Han Shibin, Zhang Jiazhong, Lu Yuanyuan, Huang Zhi, Qin School of Cybersecurity Chengdu University of Information Technology Chengdu610225 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610225 China

ISBN: (纸本)9798350318982

Traditional methods for detecting Android malware require large-scale datasets and complex feature engineering, which are unable to adapt to complex malicious features. Additionally, feature engineering based on fuzzy hash algorithms is widely used but susceptible to bypass attacks by adversaries. To address these issues, this paper proposes an Android malware detection model that multiple Android code snippet features with fuzzy hash features. Simulation experiments demonstrate that using fused fuzzy hash features improves the accuracy of Android malware detection by 7.03%, precision by 6.23%, recall by 13.78%, and F1 score by 9.88% compared to using single fuzzy hash features. The proposed detection method not only focuses on partial features of function call sequences and behaviors of malicious applications but also considers the correlation between call sequences and behaviors, enabling it to adapt to more complex Android malware detection scenarios. © 2023 IEEE.

关键词： Android malware

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