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IoT malware classification based on reinterpreted function-call graphs

computers and Security 2023年 125卷

作者： Wu, Chia-Yi Ban, Tao Cheng, Shin-Ming Takahashi, Takeshi Inoue, Daisuke Department of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei Taiwan National Institute of Information and Communications Technology Tokyo Koganei184-8795 Japan Research Center for Information Technology Innovation Academia Sinica Taipei Taiwan

Various malware and cyberattacks have arisen along with the proliferation of IoT devices. The evolving malware targeting IoT devices calls forth effective and efficient solutions to protect vulnerable IoT devices from being compromised. In this paper, we investigate the feasibility of a state-of-the-art graph embedding method, graph2vec, for performing family classification for IoT malware, with promising results reported. To further improve the generalization performance of the classifiers based on graph2vec-extracted features, we propose two new mechanisms to improve the quality of feature representation. First, we unify user-defined function calls by reinterpreting the opcode sequences therein to better capture the semantics of the function-call relationship in malware binaries. Then, we integrate literal information into the graph2vec embedding of the function call graph to achieve better discriminant ability. To prove the effectiveness of the proposed scheme, we carried out performance comparison on a large-scale dataset containing more than 108K malware binaries collected from seven CPU architectures. The accuracy rates obtained by five widely adopted classifiers on malware family classification are improved by 2%, on average, by adopting the two proposed mechanisms. Specifically, when combined with the proposed approach, the support vector machine classifier obtained an accuracy rate of 98.88% on malware family classification, outperforming known function-call-graph (FCG)-based methods and previous work on static malware analysis. © 2022 Elsevier Ltd

关键词： Static analysis

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A Priority-Aware AI-Generated Content Resource Allocation Method for Multi-UAV Aided Metaverse

A Priority-Aware AI-Generated Content Resource Allocation Me...

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2025 IEEE Wireless Communications and Networking Conference, WCNC 2025

作者： Zhang, Zheng Wang, Jingjing Chen, Jianrui Fang, Zhengru Jiang, Chunxiao Han, Zhu School of Cyber Science and Technology Beihang University Beijing China City University of Hong Kong Department of Computer Science Hong Kong Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China University of Houston Department of Electrical and Computer Engineering HoustonTX United States

ISBN: (纸本)9798350368369

With the advancement of large model technologies, AI -generated content is gradually emerging as a mainstream method for content creation. The metaverse, as a key application scenario for the next-generation communication technologies, heavily depends on advanced content generation technologies. Nevertheless, the diverse types of metaverse applications and their stringent real-time requirements constrain the full potential of AIGC technologies within this environment. In order to tackle with this problem, we construct a priority-aware multi-UAV aided metaverse system and formulate it as a Markov decision process (MDP). We propose a diffusion-based reinforcement learning algorithm to solve the resource allocation problem and demonstrate its superiority through enough comparison and ablation experiments. © 2025 IEEE.

关键词： Drones

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IQ-VFI: Implicit Quadratic Motion Estimation for Video Frame Interpolation

IQ-VFI: Implicit Quadratic Motion Estimation for Video Frame...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Mengshun Hu Kui Jiang Zhihang Zhong Zheng Wang Yinqiang Zheng National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Wuhan University Hubei Key Laboratory of Multimedia and Network Communication Engineering Harbin Institute of Technology Shanghai Artificial Intelligence Laboratory The University of Tokyo

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Advanced video frame interpolation (VFI) algorithms approximate intermediate motions between two input frames to synthesize intermediate frame. However, they struggle to handle complex scenarios with curvilinear motions since they overlook the latent acceleration information between the input frames. Moreover, the supervision of predicted motions is tricky because ground-truth motions are not available. To this end, we propose a novel frame-work for implicit quadratic video frame interpolation (IQ-VFI), which explores latent acceleration information and accurate intermediate motions via knowledge distillation. Specifically, the proposed IQ-VFI consists of an implicit acceleration estimation network (IANet) and a VFI back-bone, the former fully leverages spatio-temporal information to explore latent acceleration priors between two input frames, which is then used to progressively modulate linear motions from the latter into quadratic motions in coarse-to-fine manner. Furthermore, to encourage both components to distill more acceleration and motion cues oriented towards VFI, we propose a knowledge distillation strategy in which implicit acceleration distillation loss and implicit motion distillation loss are employed to adaptively guide latent acceleration priors and intermediate motions learning, respectively. Extensive experiments show that our proposed IQ-VFI can achieve state-of-the-art performances on various benchmark datasets.

关键词： Interpolation computer vision Accuracy Motion estimation Computational modeling Estimation Benchmark testing

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A Time-Efficient Distributed Constant Conditional Functional Dependency Discovery Algorithm for Data Consistency

A Time-Efficient Distributed Constant Conditional Functional...

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IEEE Annual International computer Software and Applications Conference (COMPSAC)

作者： Junchao Chen Jian-tao Zhou Xinyu Hao Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Engineering Laboratory for Big Data Analysis Technology College of Computer Science Inner Mongolia University Engineering Research Center of Ecological Big Data Ministry of Education National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Hohhot China

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

The accuracy of data analysis depends on data quality, and addressing data consistency issues is a key challenge to improve it. Constant Conditional Functional Dependency (CCFD) is an effective approach that ensures data consistency by enforcing bindings of semantically related values, thus providing quality assurance for data analysis and decision-making processes. However, with the growth of data scale, especially the increasing number of data tuples and attributes, existing single-machine CCFD discovery algorithms face issues of low computational efficiency and lengthy computation time. This paper proposes a time-efficient distributed CCFD discovery algorithm (DCCFD). Through the optimization of data preprocessing and index mapping, the data organization structure is enhanced, laying the foundation for the discovery of CCFDs under distributed conditions. The Spark parallel computing framework is used to partition the dataset, which accelerates the parallel loading and processing of data. Additionally, this algorithm ensures accuracy and processing speed when discovering dependencies by efficiently generating frequent itemsets and verifying CCFDs in parallel. Experiments on multiple real datasets show that, especially with the complex Airline dataset, the DCCFD algorithm not only accurately discovers CCFDs, but also reduces the average running time by 75.64% compared with the preCFDMiner algorithm.

关键词： Accuracy Data preprocessing Software algorithms Distributed databases parallel processing Software Partitioning algorithms

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FSLNet: Leveraging Skeleton Cue Guided FBIC for Bionic Intelligent Surveillance Robot via Transformer

FSLNet: Leveraging Skeleton Cue Guided FBIC for Bionic Intel...

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IEEE International Conference on Robotics and Biomimetics

作者： Hai Liu Qiang Chen Zhibing Liu Yongjian Deng Zhaoli Zhang You-Fu Li National Engineering Research Center for E-Learning Central China Normal University Wuhan China College of Computer Science Beijing University of Technology Beijing China Department of Mechanical Engineering City University of Hong Kong Kowloon Hong Kong

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

Fine-grained bird image classification (FBIC) plays a critical role in robotic vision tracking and monitoring of endangered bird species. Traditional surveillance robots often face major challenges when operating in complex environments, especially under extreme conditions involving occlusions and complex backgrounds. To overcome these limitations, we developed a novel bionic intelligent surveillance robot that incorporates FSLNet - a fine-grained skeletal learning network that integrates skeletal tokens into a Vision Transformer (ViT) framework. Unlike traditional CNN-based models, FSLNet leverages skeletal tokens to capture key invariant features, improving the ability to distinguish between visually similar bird species under challenging circumstances. The architecture of our model includes visual token construction, skeleton token construction, and a transformer-based backbone for thorough feature learning. Extensive experiments on CUB-200-2011 and NABirds datasets demonstrate that our method achieves superior performance compared to state-of-the-art approaches.

关键词： Visualization Accuracy Biological system modeling Surveillance Birds Transformers Feature extraction Skeleton Robots Image classification

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A Fine-Grained Anomaly Detection Method Fusing Isolation Forest and Knowledge Graph Reasoning 19th

A Fine-Grained Anomaly Detection Method Fusing Isolation For...

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19th International Conference on Web Information Systems and Applications, WISA 2022

作者： Xu, Jie Zhou, Jiantao College of Computer Science Engineering Research Center of Ecological Big Data Ministry of Education National and Local Joint Engineering Research Center of Mongolian Intelligent Information Processing Technology Inner Mongolia Cloud Computing and Service Software Engineering Laboratory Inner Mongolia Social Computing and Data Processing Key Laboratory Inner Mongolia Discipline Inspection and Supervision Big Data Key Laboratory Inner Mongolia Big Data Analysis Technology Engineering Laboratory Inner Mongolia University Hohhot China

ISBN: (纸本)9783031203084

Anomaly detection aims to find outliers data that do not conform to expected behaviors in a specific scenario, which is indispensable and critical in current safety environments related studies. However, when performing outlier detection on a large-scale multidimensional dataset, most of the traditional methods make no distinctions between classes of outliers and lack the reasoning ability and the explainability of the analysis results, which leads to the low accuracy of global outlier detection and the inability to effectively identify local outliers. In this paper, we propose a fine-grained anomaly detection method which well utilizes isolation forest and knowledge graph reasoning tactics. First, a better extracting data feature method and a more reasonable weighted strategy are used to find global outliers based on the traditional isolation forest algorithm, and then we construct a custom rule base according to the in-depth research and analysis of global abnormal data, and at last the ontology knowledge is reasoned based on such rule base to achieve the detection of local outliers. Through analysis of extensive experiment results, our method could effectively discover more abnormal data without big loss of time costs, has strong generalization, and correspondingly improves the performance for abnormal detection. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Anomaly detection

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Meta-heuristic computing knacks for target angle estimation in monostatic radar system with coprime arrays

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Ain Shams engineering Journal 2024年第5期15卷

作者： Akbar, Sadiq Sohail, Muhammad Asif Zahoor Raja, Muhammad Zaman, Fawad Ullah, Rizwan Abdul Rehman Khan, Muhammad Ajavakom, Nopdanai Phanomchoeng, Gridsada Department of Mechanical Engineering Faculty of Engineering Chulalongkorn University Bangkok10330 Thailand Hefei230026 China Future Technology Research Center National Yunlin University of Science and Technology Douliou64002 Taiwan Department of Electrical & Computer Engineering COMSATS University Islamabad Islamabad44000 Pakistan Department of Electrical Engineering Faculty of Engineering Chulalongkorn University Bangkok10330 Thailand Department of Electrical & Computer Engineering COMSATS University Islamabad Attock Campus 43600 Pakistan Human-Robot Collaboration and Systems Integration Research Unit Faculty of Engineering Chulalongkorn University Bangkok10330 Thailand

Angle estimation of signals received by antenna arrays is a key step in adaptive beamforming, used to detect and locate targets in space. It is desirable to reduce costs by employing a smaller number of antennas while maintaining efficiency and degrees of freedom (DOF) of the system at the same time. Co-prime antenna arrays not only circumvent the operational constraints of cost and hardware complexity, but also boost the DOF by utilizing far fewer antennas as compared to traditional uniform linear arrays. In this paper, meta-heuristic computing paradigm for estimating arrival angle of targets with co-prime arrays incorporated in monostatic radar is proposed. The system's autocorrelation and Mean Square Error (MSE) are used for fitness function optimized by global/local search with Genetic Algorithm (GA) and Pattern Search (PS). Monte Carlo simulations are performed to evaluate the performance via estimation accuracy, MSE, and computational cost for varying numbers of impinging waves. © 2024 THE AUTHORS

关键词： Genetic algorithms

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Robin: A Novel Method to Produce Robust Interpreters for Deep Learning-Based Code Classifiers

arXiv

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arXiv 2023年

作者： Li, Zhen Zhang, Ruqian Zou, Deqing Wang, Ning Li, Yating Xu, Shouhuai Chen, Chen Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Cluster and Grid Computing Lab China School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Department of Computer Science University of Colorado Colorado Springs United States Center for Research in Computer Vision University of Central Florida United States School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

Deep learning has been widely used in source code classification tasks, such as code classification according to their functionalities, code authorship attribution, and vulnerability detection. Unfortunately, the black-box nature of deep learning makes it hard to interpret and understand why a classifier (i.e., classification model) makes a particular prediction on a given example. This lack of interpretability (or explainability) might have hindered their adoption by practitioners because it is not clear when they should or should not trust a classifier's prediction. The lack of interpretability has motivated a number of studies in recent years. However, existing methods are neither robust nor able to cope with out-of-distribution examples. In this paper, we propose a novel method to produce Robust interpreters for a given deep learning-based code classifier;the method is dubbed Robin. The key idea behind Robin is a novel hybrid structure combining an interpreter and two approximators, while leveraging the ideas of adversarial training and data augmentation. Experimental results show that on average the interpreter produced by Robin achieves a 6.11% higher fidelity (evaluated on the classifier), 67.22% higher fidelity (evaluated on the approximator), and 15.87x higher robustness than that of the three existing interpreters we evaluated. Moreover, the interpreter is 47.31% less affected by out-of-distribution examples than that of LEMNA. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep learning

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Crop disease recognition based on deep learning 9

Crop disease recognition based on deep learning

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9th International Conference on Digital Home, ICDH 2022

作者： Yuan, Siyuan Luo, Xiaonan Xu, Ruishu School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Guilin Huigu Institute of Artificial Intelligence Industrial Technology Guilin China Guilin University of Electronic Technology National Local Joint Engineering Research Center of Satellite Navigation and Location Service Guilin541004 China Zhaoqing Br. Center of Guangdong Laboratory for Lingnan Modern Agricultural Science and Technology China

ISBN: (数字)9781665454780

ISBN: (纸本)9781665454780

For the vigorous development of agriculture, the identification of crop diseases and the accurate spraying of pesticides are crucial. Based on the success of deep learning in image classification and recognition, this paper proposes a method for crop disease recognition based on convolutional neural network. Our work adopts classical modules such as residual block and inception block, and combines the method of depthwise separable convolution to reduce the amount of model parameters, and proposes a more efficient model. We used the PlantVillage dataset to train the model, which contains 38 classes of 14 different crops with a total of 54,305 images. After online preprocessing of the PlantVillage dataset, the problem of limited and unbalanced sample set is alleviated. The results obtained in the test dataset show that, The accuracy and precision of this model are 92.54% and 91.83%, respectively. Compared with other deep learning models, our model achieves higher accuracy and involves a smaller amount of parameters. © 2022 IEEE.

关键词： Image classification

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SFMNet:focusing on feature attention for image classification 9

SFMNet:focusing on feature attention for image classificatio...

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9th International Conference on Digital Home, ICDH 2022

作者： Wu, Wenwei Luo, Xiaonan Xu, Songhua Chen, Ruiai School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Guilin Huigu Institute of Artificial Intelligence Industrial Technology Guilin China Zhaoqing Br. Center of Guangdong Laboratory for Lingnan Modern Agricultural Science and Technology China Guilin University of Electronic Technology National Local Joint Engineering Research Center of Satellite Navigation and Location Service Guilin541004 China

ISBN: (数字)9781665454780

ISBN: (纸本)9781665454780

In the process of convolution network processing, a convolution of each layer can be regarded as a mapping operation of image features. As we all know, when the human eye maps the prescene capture to the brain neurons, the perception of different parts of a scene is different, especially when the perspective focuses on a part or the characteristics of an object, the visual cortex pays more attention to this part. In the construction of CNN, there is consideration of image perception field, but little consideration of image feature area. Therefore, this paper proposes a convolution module called "select future map"(SFM). The model uses the residual idea of RESNET network and the use of channel attention in SENet network, and develops the attention to its own characteristic attention. The structure of convolution module is divided into three parts. The first part is multi module feature convolution, which is used to extract the features of each module in the image. The second part is feature processing, which is used to pay attention to the proportion of different module features. The third part is feature aggregation, which multiplies the modular convolution and the corresponding specific gravity to obtain the next feature image. Taking this as the basic unit, add modules to form a network, and the final experimental results get an accuracy of 84.12%. © 2022 IEEE.

关键词： Convolution

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