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44th ACM/IEEE International Conference on Software Engineering, ICSE 2024

作者： Feng, Siyue Suo, Wenqi Wu, Yueming Zou, Deqing Liu, Yang Jin, Hai Huazhong University of Science and Technology China Nanyang Technological University Singapore Hubei Engineering Research Center on Big Data Security Cluster and Grid Computing Lab National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security China Jinyinhu Laboratory Wuhan430074 China School of Cyber Science and Engineering Hust Wuhan430074 China School of Computer Science and Technology Hust Wuhan430074 China

ISBN: (纸本)9798400702174

As software engineering advances and the code demand rises, the prevalence of code clones has increased. This phenomenon poses risks like vulnerability propagation, underscoring the growing importance of code clone detection techniques. While numerous code clone detection methods have been proposed, they often fall short in real-world code environments. They either struggle to identify code clones effectively or demand substantial time and computational resources to handle complex clones. This paper introduces a code clone detection method namely Toma using tokens and machine learning. Specifically, we extract token type sequences and employ six similarity calculation methods to generate feature vectors. These vectors are then input into a trained machine learning model for classification. To evaluate the effectiveness and scalability of Toma, we conduct experiments on the widely used BigCloneBench dataset. Results show that our tool outperforms token-based code clone detectors and most tree-based clone detectors, demonstrating high effectiveness and significant time savings. © 2024 ACM.

关键词： Machine learning

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Security Intensification using Blockchain coupled with Internet of Things: Proposal, Challenges and Anatomization 4

Security Intensification using Blockchain coupled with Inter...

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4th International Conference on Advances in computing, Communication Control and Networking, ICAC3N 2022

作者： Anand, M. Vivek Sabharwal, Munish Vijayalakshmi, S. Galgotias University School of Computing Science and Engineering Greater Noida201308 India Christ University Dept of Data Science 412112 India

ISBN: (纸本)9781665474368

Internet of things is an important part of our day-to-day life where all things are connected in the network with the internet. The number of devices linked to the network grows steadily each day in recent years. The innovation in the manufacturing industry also the reason for the production of different devices that uses various technologies to make a possible connection between the devices. Even though the Internet of Things has been developing and demonstrating its potential in recent years, its security when connected to the internet is in doubt. Blockchain is a disruptive technology that provides security to their network without tampering with the data in the network. Researchers and experts have recommended using the blockchain to address security vulnerabilities in the Internet of Things. In this paper, we have analyzed some of the issues which are occurring while integrating blockchain into the Internet of things. The major issues were discussed and which will be helpful to move towards the research direction to solve those problems. © 2022 IEEE.

关键词： Blockchain

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data-Driven Design for Anomaly Detection in Network Access Control Systems 2

Data-Driven Design for Anomaly Detection in Network Access C...

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2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023

作者： Muhammad, Musa Abubakar Caraffini, Fabio Fasanmade, Adebamigbe Ishola, Olabayo Mohammed, Kabiru Morden, Jarrad Coventry University School of Computing Mathematics and Data Science Coventry United Kingdom Swansea University Department of Computer Science Swansea United Kingdom De Montfort University School of Computer Science and Informatics Leicester United Kingdom

ISBN: (纸本)9798350335644

Current network access control systems can contain unpredictable interactions between multiple device models, multiple network protocol layers (e.g. TCP, UDP and ICMP), hardware, and clock-skew-specific influences, and cannot detect or identify abnormal behaviours based on the type of device. To complicate things further, the 'bring your own device' policy is increasing security threats, vulnerabilities, and risks to enterprise network environments, making intrusion detection and prevention systems unable to detect illegal and unauthorised access to devices in the enterprise network. The consequences can be disastrous. In this light, this work propose a simple but effective clustering approach capable of separating normal and abnormal network traffic patterns to detect such challenges (anomalies). The work in this paper applies this approach to single devices and aggregations of data per device type. Additionally, this work propose plotting the notched box for each cluster to acquire a better understanding of their data distributions and measuring the clusters' performance using the Adjusted Rand Index. The results show that the proposed method is valid, can be used in several contexts, and features a 95% confidence that most single device and device type distributions overlap, which makes them equivalently usable for anomaly detection purposes. © 2023 IEEE.

关键词： K-means clustering

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REQUIRED NUMBER OF ITERATIONS FOR SPARSE SIGNAL RECOVERY VIA ORTHOGONAL LEAST SQUARES

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Journal of Computational Mathematics 2023年第1期41卷 1-17页

作者： Haifeng Li Jing Zhang Jinming Wen Dongfang Li Henan Engineering Laboratory for Big Data Statistical Analysis and Optimal Control College of Mathematics and Information ScienceHenan Normal UniversityXinxiang 453007China College of Information Science and Technology Jinan UniversityGuangzhou 510632China School of Mathematics and Statistics Huazhong University of Science and TechnologyWuhan 430074China Hubei Key Laboratory of Engineering Modeling and Scientific Computing Huazhong University of Science and TechnologyWuhan 430074China

In countless applications,we need to reconstruct a K-sparse signal x∈R n from noisy measurements y=Φx+v,whereΦ∈R^(m×n)is a sensing matrix and v∈R m is a noise *** least squares(OLS),which selects at each step the column that results in the most significant decrease in the residual power,is one of the most popular sparse recovery *** this paper,we investigate the number of iterations required for recovering x with the OLS *** show that OLS provides a stable reconstruction of all K-sparse signals x in[2.8K]iterations provided thatΦsatisfies the restricted isometry property(RIP).Our result provides a better recovery bound and fewer number of required iterations than those proposed by Foucart in 2013.

关键词： Sparse signal recovery Orthogonal least squares(OLS) Restricted isometry property(RIP)

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Et tu, Brute? Side-Channel Assisted Chosen Ciphertext Attacks Using Valid Ciphertexts on HQC KEM 16th

Et tu, Brute? Side-Channel Assisted Chosen Ciphertext Attack...

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16th International Workshop on Post-Quantum Cryptography, PQCrypto 2025

作者： Paiva, Thales B. Ravi, Prasanna Jap, Dirmanto Bhasin, Shivam Das, Sayan Chattopadhyay, Anupam University of Sao Paulo Fundep and CASNAV Sao Paulo Brazil Temasek Laboratories Nanyang Technological University Singapore Singapore College of Computing and Data Science Nanyang Technological University Singapore Singapore PQStation Singapore Singapore

ISBN: (纸本)9783031866012

HQC is a code-based key encapsulation mechanism (KEM) that was selected to move to the fourth round of the NIST post-quantum standardization process. While this scheme was previously targeted by side-channel assisted chosen-ciphertext attacks for key recovery, all these attacks have relied on malformed ciphertexts for key recovery. Thus, all these attacks can be easily prevented by deploying a detection based countermeasures for invalid ciphertexts, and refreshing the secret key upon detection of an invalid ciphertext. This prevents further exposure of the secret key to the attacker and thus serves as an attractive option for protection against prior attacks. Thus, in this work, we present a critical analysis of the detection based countermeasure, and present the first side-channel based chosen-ciphertext attack that attempts to utilize only valid ciphertexts for key recovery, thereby defeating the detection based countermeasure. We propose novel attacks exploiting leakage from the ExpandAndSum and FindPeaks operations within the Reed-Muller decoder for full key recovery with 100% success rate. We show that our attacks are quite robust to noise in the side-channel measurements, and we also present novel extensions of our attack to the shuffling countermeasure on both the ExpandAndSum and FindPeaks operation, which renders the shuffling countermeasure ineffective. Our work therefore shows that low-cost detection based countermeasures can be rendered ineffective, and cannot offer standalone protection against CC-based side-channel attacks. Thus, our work encourages more study towards development of new low-cost countermeasures against CC-based side-channel attacks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Costs

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DE-ConvGraph 3D UNet: A Novel Deep Learning Model for Optimizing Radiotherapy Treatment Plans in Oropharyngeal Cancer

DE-ConvGraph 3D UNet: A Novel Deep Learning Model for Optimi...

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2023 IEEE Applied Imagery Pattern Recognition Workshop, AIPR 2023

作者： Murugadoss, Bhuvanashree Amudha, J. Sugumaran, Vijayan Amrita Vishwa Vidyapeetham School of Computing Department of Computer Science and Engineering Karnataka Bengaluru India Oakland University Center for Data Science and Big Data Analytics RochesterMI United States Oakland University Department of Decision and Information Sciences RochesterMI United States

ISBN: (纸本)9798350359527

Automated radiotherapy treatment planning aims to improve treatment accuracy and efficiency. However, the prevalent Knowledge-Based Planning (KBP) method faces issues like lengthy manual problem formulation and challenges in accurately modeling human anatomy and processing high-dimensional data. This work focuses on treatment plan optimization and considers deep learning as a potential solution. Deep learning algorithms, by virtue of their ability to learn from large quantities of data and model complex relationships, can automate the formulation of the optimization problem in KBP, saving significant time and effort. However, despite these compelling advantages, it should be noted that the current convolution-based encoder-decoder models used for radiotherapy treatment plan optimization have a limited capability in capturing long-range dependencies between distant voxels. This work aims to introduce 'DE-ConvGraph 3D UNet', a novel deep learning model, to address these limitations and optimize radiotherapy treatment plans for oropharyngeal cancer. The proposed 'DE-ConvGraph 3D UNet' model includes a Graph Convolutional Network (GCN) component to capture long-range dependencies between distant voxels. Furthermore, the dual-encoder structure of the model combines the strengths of GCN and 3D Convolutional U-Net, enabling global relationships and local pattern capturing in a 3D patient volume. Experiments were conducted using the benchmark dataset-OpenKBP-Opt. The proposed model shows improvements in performance in comparison to state-of-The-Art U-Net variants in terms of mean squared voxel-wise error, dose volume histogram points and clinical criteria satisfaction. © 2023 IEEE.

关键词： Learning algorithms

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RNTrajRec: Road Network Enhanced Trajectory Recovery with Spatial-Temporal Transformer 39

RNTrajRec: Road Network Enhanced Trajectory Recovery with Sp...

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39th IEEE International Conference on data Engineering, ICDE 2023

作者： Chen, Yuqi Zhang, Hanyuan Sun, Weiwei Zheng, Baihua Fudan University School of Computer Science Shanghai Key Laboratory of Data Science China Shanghai Institute of Intelligent Electronics & Systems China Singapore Management University School of Computing and Information Systems Singapore

ISBN: (纸本)9798350322279

GPS trajectories are the essential foundations for many trajectory-based applications. Most applications require a large number of high sample rate trajectories to achieve a good performance. However, many real-life trajectories are collected with low sample rate due to energy concern or other constraints. We study the task of trajectory recovery in this paper as a means to increase the sample rate of low sample trajectories. Most existing works on trajectory recovery follow a sequence-to-sequence diagram, with an encoder to encode a trajectory and a decoder to recover real GPS points in the trajectory. However, these works ignore the topology of road network and only use grid information or raw GPS points as input. Therefore, the encoder model is not able to capture rich spatial information of the GPS points along the trajectory, making the prediction less accurate and less spatial consistent. In this paper, we propose a road network enhanced transformer-based framework, namely RNTrajRec, for trajectory recovery. RNTrajRec first uses a graph model, namely GridGNN, to learn the embedding features of each road segment. It next develops a spatial-temporal transformer model, namely GPSFormer, to learn rich spatial and temporal features along with a Sub-Graph Generation module to capture the spatial features for each GPS point in the trajectory. It finally forwards the outputs of encoder model to a multi-task decoder model to recover the missing GPS points. Extensive experiments based on three large-scale real-life trajectory datasets confirm the effectiveness of our approach. © 2023 IEEE.

关键词： Recovery

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Is Score Matching Suitable for Estimating Point Processes? 38

Is Score Matching Suitable for Estimating Point Processes?

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Cao, Haoqun Meng, Zizhuo Ke, Tianjun Zhou, Feng Center for Applied Statistics School of Statistics Renmin University of China China Data Science Institute University of Technology Sydney Australia Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing China

Score matching estimators have gained widespread attention in recent years partly because they are free from calculating the integral of normalizing constant, thereby addressing the computational challenges in maximum likelihood estimation (MLE). Some existing works have proposed score matching estimators for point processes. However, this work demonstrates that the incompleteness of the estimators proposed in those works renders them applicable only to specific problems, and they fail for more general point processes. To address this issue, this work introduces the weighted score matching estimator to point processes. Theoretically, we prove the consistency of our estimator and establish its rate of convergence. Experimental results indicate that our estimator accurately estimates model parameters on synthetic data and yields results consistent with MLE on real data. In contrast, existing score matching estimators fail to perform effectively. Codes are publicly available at https://***/KenCao2007/WSM_TPP. © 2024 Neural information processing systems foundation. All rights reserved.

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Gloss-Free Sign Language Translation using Sign2gpt-Next Technique

Gloss-Free Sign Language Translation using Sign2gpt-Next Tec...

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2024 International Conference on computing and Intelligent Reality Technologies, ICCIRT 2024

作者： Babisha, A. Umarani Srikanth, G. Angel Kiruba, D.R. Sundar, R. Department of Artificial Intelligence and Data Science Panimalar Engineering College Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Computer Science and Engineering DMI College of Engineering Chennai India Department of Computer Science and Engineering School of Computing Sathyabama Institute of Science and Technology Chennai India

ISBN: (纸本)9798331510299

This work proposes a new approach to convert the data from sign language to spoken language without exposing the data to a gloss layer. Whenever gloss annotations are used which frequently are incomplete and act as an intermediate representation, it is usually found to be a bottleneck in translating sign language videos. In order to avoid this, the authors use a combination of modern machine learning algorithms to arrive at the conclusion. They use Dino-V2 Vision Transformer to implement the function of extracting the spatial features of the input sign language video frames. This model is particularly suitable for learning and visual patterns in general and particularly for capturing fine details of these patterns. Further, a spatio-temporal transformer layer is added to automate the temporal processing of the Sign-Language which helps in the arrangement of the body gestures and limb movements needed at a specific temporal frame. It is then translated into the desired spoken language using a multilingual Generative Pre-trained Transformer (GPT) model. The approach can be said to be a big leap forward in sign language transliteration especially for such situations where few Gloss annotations are available, and making way for better and improved tools for the Deaf and the hard-of-hearing. © 2024 IEEE.

关键词： Audition

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A recurrent neural network architecture for android mobile data analysis for detecting malware infected data

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Soft computing 2024年第21期28卷 12917-12928页

作者： Murugan, Prabhu Manimaran, A. Sundar, Ramesh Dakshinamoorthy, Prabakar Rajaram, Gnanajeyaraman Garg, Shruti Department of ECE Saveetha School of Engineering Simats Chennai602105 India Department of Artificial Intelligence and Machine Learning Saveetha School of Engineering Simats Chennai602105 India Department of Netwoking and Communication School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur India Department of Data Science and Business System School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur India Department of Applied Machine Learning Saveetha School of Engineering Saveetha Institute of Medical and Technological Sciences Tamilnadu Chennai India Birla Institute of Technology Mesra Ranchi India

One of the latest modern communication devices is a mobile device seriously affected by multiple malware. Malware is a virus software installed automatically by hackers on various computing devices. Malware corrupts the system software, kills *.exe files, and tries to access user-sensitive data from the device. Around the world, 80% of people use smartphones, and 70% use Android phones. All Android phones are installed with anti-virus software but not used properly. Thus, it creates a pathway for malware to enter Android mobile devices and deploy vulnerabilities. Several earlier methods have focused on using the malware detection method by analyzing static data, which cannot identify the dynamic behavior of the malware. Thus, this paper implemented one of the deep learning algorithms, RNN, for identifying and detecting malware activities in Android mobile devices statically and dynamically. An IoT network architecture is proposed for experimenting with the RNN algorithms verifying the performance. The results of the RNN method show that it is better than other models in malware detection in mobile devices. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Android malware

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