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Generic,efficient,and effective deobfuscation and semantic-aware attack detection for Power Shell scripts

Frontiers of Information Technology & Electronic engineering 2022年第3期23卷 361-381页

作者： Chunlin XIONG Zhenyuan LI Yan CHEN Tiantian ZHU Jian WANG Hai YANG Wei RUAN College of Computer Science and Technology Zhejiang UniversityHangzhou 310027China Department of Electrical Engineering and Computer Science Northwestern UniversityEvanstonIL 60208USA College of Computer Science and Technology Zhejiang University of TechnologyHangzhou 310023China Magic Shield Co. Ltd.Hangzhou 310027China College of Control Science and Engineering Zhejiang UniversityHangzhou 310027China

In recent years,Power Shell has increasingly been reported as appearing in a variety of cyber ***,because the PowerShell language is dynamic by design and can construct script fragments at different levels,state-of-the-art static analysis based Power Shell attack detection approaches are inherently vulnerable to *** this paper,we design the first generic,effective,and lightweight deobfuscation approach for PowerShell *** precisely identify the obfuscated script fragments,we define obfuscation based on the differences in the impacts on the abstract syntax trees of PowerShell scripts and propose a novel emulation-based recovery ***,we design the first semantic-aware PowerShell attack detection system that leverages the classic objective-oriented association mining algorithm and newly identifies 31 semantic *** experimental results on 2342 benign samples and 4141 malicious samples show that our deobfuscation method takes less than 0.5 s on average and increases the similarity between the obfuscated and original scripts from 0.5%to 93.2%.By deploying our deobfuscation method,the attack detection rates for Windows Defender and VirusTotal increase substantially from 0.33%and 2.65%to 78.9%and 94.0%,***,our detection system outperforms both existing tools with a 96.7%true positive rate and a 0%false positive rate on average.

关键词： PowerShell Abstract syntax tree Obfuscation and deobfuscation Malicious script detection

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ELOFS: An Extensible Low-Overhead Flash File System for Resource-Scarce Embedded Devices

ELOFS: An Extensible Low-Overhead Flash File System for Reso...

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作者： Zhang, Runyu Liu, Duo Chen, Xianzhang She, Xiongxiong Yang, Chaoshu Tan, Yujuan Shen, Zhaoyan Shao, Zili Qiao, Lei Chongqing400044 China Chongqing University College of Computer Science Chongqing400044 China Shandong University School of Computer Science and Technology Qingdao266237 China The Chinese University of Hong Kong Department of Computer Science and Engineering NT 999077 Hong Kong Beijing Institute of Control Engineering Beijing100190 China

Emerging applications like machine learning in embedded devices (e.g., satellites and vehicles) require huge storage space, which recently stimulates the widespread deployment of large-scale flash memory in IoT devices. However, existing embedded file systems fall short in managing large-capacity storage efficiently for two reasons. First, prior arts store data structures of file systems either in flash or in main memory, which severely magnifies the scarcity of computing and memory resources. Moreover, the fine-grained metadata management in the existing embedded file systems induces significant energy consumption for large-capacity storage. In this paper, we propose a novel embedded file system, ELOFS, to tackle the above issues and manage large-capacity NAND flash on resource-scarce devices. ELOFS is made efficient through three novel techniques. First, we redefine the space management granularity and streamline the metadata to speed up the mounting performance. In addition, we design hybrid file structures to adapt dissimilar access patterns of embedded devices. Furthermore, ELOFS provides opportunities for in-depth cooperation with application-specific systems. We implement ELOFS with Memory Technology Device (MTD) interfaces, and the experimental results show that ELOFS outperforms YAFFS and UBIFS in terms of write, read, and deletions with orders of magnitude reductions on memory footprint and mounting time. © 1968-2012 IEEE.

关键词： Metadata

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Triad Photosensitizers Based on Iodo-Aza-Bodipy and Naphthalenediimides (Ndi) with Broadband Absorption: Study of Resonance Energy Transfer and Electron Transfer

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SSRN 2024年

作者： Ma, Jie Zhao, Fangming Yin, Yanbing Li, Yingying Wu, Di Yin, Guangming Gao, Xiaohui Heilongjiang Provincial Key Laboratory of Catalytic Synthesis for Fine Chemicals School of Chemistry and Chemical Engineering China College of Computer and Control Engineering Qiqihaer University Qiqihar161006 China

Two organic photosensitizers based onresonance energy transfer (A-1 and A-2) are prepared. Naphthalenediimides was used as intramolecular energy donor and iodo-aza-Bodipy was used as intramolecular energy acceptor and spin converter. A-1(e = 42800 at 618 nm and e = 59600 at 674 nm) and A-2 (e = 33300 at 514 nm and e = 68300 at 674 nm) give the two strong absorption in visible region. Both the photosensitizers show broadband absorption in visible region. The photophysical properties of the compounds were studied with steady state and nanosecond transient absorption and emission spectroscopy in detail. Quantum yield of singlet oxygen of A-1 and A-2 is 39.7% and 40.9 %, respectively. The efficiency of intramolecular energy transfer is estimated to be 15.9% and 27.7% for A-1 and A-2 based on fluorescence lifetime quenching. PET is responsible for the relatively low efficiency, that is identified by the calculation of kcs / kFRET and the Gibbs free energy change(G0cs). With steady state and time-resolved spectroscopy, we found that photoexcitation into the energy donor was followed by singlet energy transfer, then via the intersystem crossing (ISC) of the energy acceptor. By nanosecond time-resolved transient difference absorption spectroscopy, triplet excited states are localized on the iodo-aza-Bodipy. A-1 and A-2 were used for singlet oxygen (1O2) mediated photooxidation of 1,5-dihydroxylnaphthalene and the photosensitizing ability are more efficient than the triplet photosensitizers with the mono-chromophore photosensitizer. © 2024, The Authors. All rights reserved.

关键词： Photosensitizers

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Classification of Images Based on a System of Hierarchical Features

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computers, Materials & Continua 2022年第7期72卷 1785-1797页

作者： Yousef Ibrahim Daradkeh Volodymyr Gorokhovatskyi Iryna Tvoroshenko Mujahed Al-Dhaifallah Department of Computer Engineering and Networks College of Engineering at Wadi AddawasirPrince Sattam Bin Abdulaziz UniversityAl-Kharj11991Saudi Arabia Department of Informatics Kharkiv National University of Radio ElectronicsKharkiv61166Ukraine Control and Instrumentation Engineering Department and Interdisciplinary Research Center(IRC)for Renewable Energy and Power SystemsKing Fahd University of Petroleum&MineralsDhahran31261Saudi Arabia

The results of the development of the new fast-speed method of classification images using a structural approach are *** method is based on the system of hierarchical features,based on the bitwise data distribution for the set of descriptors of image *** article also proposes the use of the spatial data processing apparatus,which simplifies and accelerates the classification *** have shown that the time of calculation of the relevance for two descriptions according to their distributions is about 1000 times less than for the traditional voting procedure,for which the sets of descriptors are *** introduction of the system of hierarchical features allows to further reduce the calculation time by 2–3 times while ensuring high efficiency of *** noise immunity of the method to additive noise has been experimentally *** to the results of the research,the marginal degree of the hierarchy of features for reliable classification with the standard deviation of noise less than 30 is the 8-bit *** costs increase proportionally with decreasing bit *** method can be used for application tasks where object identification time is critical.

关键词： Bitwise distribution computer vision descriptor hierarchical representation image classification keypoint noise immunity processing speed

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Reinforcement Learning-Based Fault-Tolerant control of Uncertain Strict-Feedback Nonlinear Systems With Intermittent Actuator Faults

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IEEE Transactions on Neural Networks and Learning Systems 2025年第6期36卷 10464-10478页

作者： Qinmin Yang Huaying Li Zhengwei Ruan Bo Fan Shuzhi Sam Ge State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China Hangzhou Huawei Communication Technology Company Ltd. Hangzhou China School of Automation Science and Engineering Xi’an Jiaotong University Xi’an China Department of Electrical and Computer Engineering Institute for Functional Intelligent Materials National University of Singapore Queenstown Singapore

In this work, a novel reinforcement learning-based adaptive fault-tolerant control (FTC) scheme with actuator redundancy is presented for a nonlinear strict-feedback system with nonlinear dynamics and uncertainties. A learning-based switching function technique is established to steer different groups of actuators automatically and successively to mitigate the impact of faulty actuators by observing a switching performance index. The optimal tracking control problem (OTCP) of strict-feedback nonlinear systems is transformed into an equivalent optimal regulation problem of each affine subsystem via adaptive feedforward controllers. Subsequently, the designed objective functions associated with Hamilton–Jacobi–Bellman (HJB) estimate errors caused by neural network (NN) approximations can be minimized by the reinforcement learning algorithm without value or policy iterations. It is proved that the tracking objective can be achieved and all signals in the closed-loop system can be guaranteed to be bounded, as long as the minimum time interval between two successive failures is bounded. Theoretical results are verified by simulations.

关键词： Actuators Switches Fault tolerant systems control systems Optimal control Redundancy Nonlinear dynamical systems Adaptive control Regulation Industrial control

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Quality Assessment of Electrocardiogram Signals Using Contrastive Learning

Quality Assessment of Electrocardiogram Signals Using Contra...

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IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Meiping Zhong Zuoyong Li ChuanSheng Wang Jiayan Huang WanQi Zhao Haoyi Fan College of Computer and Big Data Fuzhou University Fuzhou China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou China Department of Automatic Control Technical Polytechnic University of Catalonia Barcelona Spain New Engineering Industry College Putian University Putian China School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou China

Quality assessment of electrocardiogram (ECG) signals holds significant importance in reducing the high-frequency false alarms in automated arrhythmia diagnosis. Existing quality assessment methods primarily rely on empirical threshold setting or the calculation of relevant statistical values to formulate decision rules. However, the thresholds set in this manner are typically based on experience and knowledge, resulting in decision rules that lack flexibility and struggle to adapt to changes in the dataset. To address this issue, many researchers have turned to deep learning algorithms for ECG signal quality assessment. Nevertheless, most existing deep learning methods usually require large-scale and labeled ECG data for training, whose acquiring is typically challenging. Therefore, this paper proposes an efficient method for ECG signal quality assessment. First, we use a contrast learning framework to construct sample pairs through multilevel data augmentation. Second, a densely connected convolutional network (DenseNet) is used to improve the model's ability of extracting local features in ECG signals, and a Squeeze-and-Excitation attention module is introduced to make the model pay more attention to critical features. Finally, the feature representation is learned by minimizing the contextual contrast loss to bring similar samples closer and push dissimilar samples farther away. In this way, we can utilize the contrastive learning framework to extract temporal representations from unlabeled data and transfer them to downstream ECG signal quality assessment tasks. Extensive experiments on the Physionet/CinC Challenge 2011 dataset demonstrate that our approach outperforms most existing quality assessment methods.

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LoBee: Bidirectional Communication between LoRa and ZigBee based on Physical-Layer CTC

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IEEE Transactions on Wireless Communications 2025年

作者： Gao, Demin Wang, Haoyu Chen, YongRui Ye, Qiaolin Wang, Weizheng Guo, Xiuzhen Wang, Shuai Liu, Yunhuai He, Tian Nanjing Forestry University College of Information Science and Technology Artificial Intelligence Nanjing210037 China University of Minnesota The Department of Computer Science and Engineering 55414 United States University of Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering Beijing100049 China Southeast University The School of Computer Science and Engineering Nanjing211189 China City University of Hong Kong Department of Computer Science 999077 Hong Kong College of Control Science and Engineering Zhejiang 310027 China Peking University The Department of Computer Science and Engineering Beijing100871 China University of Minnesota The Department of Computer Science and Engineering Minneapolis55414 United States

LoRa networks operating in a star topology, this configuration creates a single point of failure and may limit scalability and reliability in areas that are large and geographically dispersed. In order to improve the overall transmission capabilities of the network, recent studies show that adding LoRa to the ZigBee devices effectively disseminates network management. By doing so, the strengths of both technologies can be leveraged, with LoRa serving as the long-range transmitter and ZigBee functioning as the mesh network. In this study, we present LoBee, a novel bidirectional communication method between LoRa and ZigBee that relies on Physical-Layer Cross-Technology Communication. Despite the fact that LoRa and ZigBee utilize different modulation techniques, ZigBee devices can detect and recognize LoRa chirps through the process of sampling the received signal strength. For the transmissions from ZigBee to LoRa devices, we carefully select the input chips to generate specific waveforms, where LoBee detects the preamble of a ZigBee frame based on the locations of the repeated peaks. Our evaluation, which was conducted using USRP and commodity devices, demonstrates that LoBee is capable of achieving concurrent bidirectional wireless communications, with a data rate of approximately 639.38 bits per second from LoRa to ZigBee and from ZigBee to LoRa with more than 90% frame reception rate in the 2.4 GHz frequency band. © 2002-2012 IEEE.

关键词： Zigbee

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Hierarchical Retrieval of High-Resolution Fingerprints Based on Pore Feature

Hierarchical Retrieval of High-Resolution Fingerprints Based...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jing Ma Yuanrong Xu Suyu Dong Wei Wang School of Computer Science and Technology Harbin Institute of Technology Shenzhen China School of Computer Science and Technology Harbin Institute of Technology Weihai China College of Computer and Control Engineering Northeast Forestry University Harbin China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Faced with an escalating number of fingerprint images, most existing retrieval approachs suffer from a common problem: diminishing computational efficiency. This paper presents a hierarchical retrieval system tailored for high-resolution fingerprint images that utilizes abundant pore features and robust recognizability to improve retrieval performance. The framework comprises two core components. Firstly, a CNN-based feature extraction network is established, incorporating an attention mechanism to capture pore features in fingerprint images comprehensively. Subsequently, a hierarchical fingerprint retrieval approach is introduced, involving connection graph construction and a hierarchy of jump table structures for efficient retrieval of query pores. Empirical experiments conducted on high-resolution fingerprint image datasets underscore the system’s effectiveness. Compared with other advanced pore-based fingerprint retrieval methods, the proposed method exhibits a notable rise in the hit rate with reduced penetration rates, significantly reducing the retrieval time.

关键词： Accuracy Image recognition Attention mechanisms Image matching Fingerprint recognition Feature extraction Robustness Computational efficiency Bioinformatics

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Deep Adaptively Feature Extracting Network for Cervical Squamous Lesion Cell Detection 4th

Deep Adaptively Feature Extracting Network for Cervical Squ...

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4th International Conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Peng, Zhonghua Hu, Rong Wang, Fuen Fan, Haoyi Eng, Yee Wei Li, Zuoyong Zhou, Liwei Fujian Provincial Key Laboratory of Big Data Mining and Applications School of Computer Science and Mathematics Fujian University of Technology Fujian Fuzhou350118 China School of Computer and Artificial Intelligence Zhengzhou University Henan Zhengzhou450001 China School of Computer Sciences Universiti Sains Malaysia Penang11800 Malaysia Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fujian Fuzhou350121 China Department of Nutrition The First Affiliated Hospital of Zhengzhou University Henan Zhengzhou450052 China

ISBN: (纸本)9783031200984

Cervical cancer is one of the most widespread malignancies affecting women’s health worldwide today. However, the task of detection is particularly difficult due to the complex background of the cervical smear, where cells are previously stacked in clusters. To address this problem, we utilize the YOLOv5 as the baseline and build on YOLOv5 by using the simple Transformer Block which only combines with multi-head self attention layers and MLP to better extract cell features as well as to obtain global information. In addition, we allow the model to refine features adaptively to assist with detection by using Convolutional Block Attention Module (CBAM), an attention module being simple and effective for feed-forward convolutional neural networks, in the complex background information. Finally, we compare the model with YOLOv5 as baseline. In CDetector dataset, our model obtains 52.5% mAP@.5, which is 6% better than baseline. In transfer learning, it is 62.2%, which outperforms baseline by 3.2%. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Cells

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Optimized Algebraic Decoding Algorithm for (47, 24, 11) QR Code

Optimized Algebraic Decoding Algorithm for (47, 24, 11) QR C...

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IEEE International Conference on Information Communication and Signal Processing (ICICSP)

作者： Chunlan Luo Xiaoxia Zhu College of Computer and Control Engineering Minjiang University Fuzhou Fujian China

A fast decoding algorithm scheme is proposed for the quadratic residue code with code length of 47 and large error-correcting capacity of 5 errors in this paper, called optimized algebraic decoding algorithm (OADA). The main contributions of this article are to deduce a method to quickly compute the related unknown syndrome of (47,24,11) QR code and a simpler discriminant condition to detect whether four errors occurred in the received word. The decoding algorithm scheme needs lower memory requirement compared with the algorithm based on the full hard decision algebraic decoding algorithm. For exhaustive simulation test of all the error patterns shows that the proposed algorithm of (47, 24, 11) QR code improves the decoding performance and decoding efficiency.

关键词： Analytical models Codes Simulation Computational modeling Memory management Signal processing algorithms QR codes

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