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17th International Conference on Security of information and Networks, SIN 2024

作者： Bisht, Tarun Babu, Sarath Singh, Virendra Indian Institute of Technology Bombay Computer Science and Engineering Department Mumbai India

ISBN: (纸本)9798331509736

The widespread use and the immense user base make Windows systems a prime target for attackers seeking to exploit vulnerabilities and maximize impact. Modern obfuscation techniques enable malware to evade detection tools, allowing it to intrude on systems and carry out malicious activities. Thus, to prevent potential harm to the victim's system, it is crucial to detect malware at the early execution stage and initiate adequate action. However, there is often a trade-off between accuracy and earliness in malware detection, as detecting threats at earlier stages may sometimes come at the cost of reduced detection accuracy. We introduce an early malware detection framework that balances this trade-off. Our proposed framework iteratively constructs API call prefix subsequence and applies security-sensitive embedding using API call parameters. The causal sequence encoder transforms these sequences into contextual vectors, which are then classified by a multi-layer perceptron. The proposed framework not only outperforms the state-of-the-art early malware detection method EarlyMalDetect but also demonstrates comparable performance to post-sequence malware detection methods like CTIMD and BD-MDLC, achieving accurate maliciousness prediction on or before analyzing just 3 % of the API sequence. © 2024 IEEE.

关键词： Causal encoder Deep learning Early detection Malware Sequence analysis

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Weberator: A low code backend generator tool 5

Weberator: A low code backend generator tool

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5th IEEE International Conference on Advances in Electronics, computers and Communications, ICAECC 2023

作者： Koneti, Chandrakant Rechintala, Likhith Reddy Koneti, Jaya Sriharshita Indian Institute of Engineering Science and Technology Department of Information Technology Kolkata India Chaitanya Bharathi Institute of Technology Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350305449

Nowadays, everyone desires to own a website, but creating it is tiresome. Developers can build a web application more effortlessly with the assistance of a software that automates the generation process. Several open-source softwares exist that help developers generate basic web application code. Some generation tools ease a developer's work by generating backend code. These generation tools work by retrieving information about the application and building it accordingly. One such generator tool is 'JHipster', an open-source software that generates a deployment-ready backend application. However, these generation tools are little challenging to operate and might be complex for many novice developers. Also, they require numerous prerequisite dependencies, and beginner developers might find it strenuous to comprehend and work with them. It is not everyone's cup of tea to apprehend the generation process and work with such high-level software. To bridge the gap between a beginner's technical knowledge and an advanced tool like JHipster, a mobile application named 'Weberator' that helps reduce the time and effort to create a backend application was developed. This paper shows the implementation of 'Weberator' that solves the issue of the technical gaps discussed above. This mobile application allows users to choose the technologies for their backend application and helps by generating the entire backend application into a ZIP format file. © 2023 IEEE.

关键词： Open source software

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PanoptiChrome: A Modern In-browser Taint Analysis Framework 24

PanoptiChrome: A Modern In-browser Taint Analysis Framework

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33rd ACM Web Conference, WWW 2024

作者： Kanyal, Rahul Sarangi, Smruti R. Computer Science and Engineering Indian Institute of Technology Delhi New Delhi India

ISBN: (纸本)9798400701719

Taint tracking in web browsers is a problem of profound interest because it allows developers to accurately understand the flow of sensitive data across JavaScript (JS) functions. Modern websites load JS functions from either the web server or other third-party sites, hence this problem has acquired a much more complex and pernicious dimension. Sadly, for the latest version of the Chromium browser (used by 75% of users), there is no dynamic taint propagation engine primarily because it is incredibly complex to build one. The nearest contending work in this space was published in 2018 for version 57;at the time of writing, we are at Chromium version 117, and the current version is very different from the 2018 version. We outline the details of a multi-year effort in this paper that led to PanoptiChrome, which accurately tracks information flow across an arbitrary number of sources and sinks and is, to a large extent, portable across platforms. As an example use case of the platform, we experimentally show that we can discover fingerprinting APIs that can uniquely identify the browser and sometimes the user, which are missed by state-of-the-art tools, owing to our comprehensive dynamic analysis methodology. For the top 20,000 most popular websites, we discovered a total of 362 APIs that have the potential to be used for fingerprinting - out of these, 208 APIs were previously not reported by state-of-the-art tools. © 2024 ACM.

关键词： Websites

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Controllable chaos in the coupled waveguide-optomechanical system with phase shifter

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science China(Physics,Mechanics & Astronomy) 2025年第4期68卷 26-34页

作者： Deng-Wei Zhang Pei-Qi Qin Li-Li Zheng Zhi-Ming Zhan Xin-You Lü Department of Mathematics and Physics Luoyang Institute of Science and TechnologyLuoyang 471023China School of Computer and Information Engineering School of SoftwareLuoyang Institute of Science and TechnologyLuoyang 471023China School of Artificial Intelligence Jianghan UniversityWuhan 430056China School of Physics Huazhong University of Science and TechnologyWuhan 430074China

We theoretically investigate chaotic dynamics in an optomechanical system composed of a whispering-gallery-mode(WGM)microresonator and a *** find that tuning the optical phase using a phase shifter and modifying the coupling strength via a unidirectional waveguide(IWG)can induce chaotic *** underlying reason for this phenomenon is that adjusting the phase and coupling strength via the phase shifter and IWG bring the system close to an exceptional point(EP),where field localization dynamically enhances the optomechanical nonlinearity,leading to the generation of chaotic *** addition,due to the sensitivity of chaos to phase in the vicinity of the EP,we propose a theoretical scheme to measure the optical phase perturbations using *** work may offer an alternative approach to chaos generation with current experimental technology and provide theoretical guidance for optical signal processing and chaotic secure communication.

关键词： cavity optomechanics chaos exceptional point

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Gannet Optimization Algorithm Enhanced by Quasi-Affine Transformation Algorithm and Its Application in 3D Coverage of Wireless Sensor Network

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第1期9卷 69-87页

作者： Peng, Yun-Feng Chu, Shu-Chuan Wang, Ru-Yu Zhao, Jia Pan, Jeng-Shyang School of Computer Science Northeast Electric Power University Jilin132012 China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China School of Information Engineering Nanchang Institute of Technology Nanchang330099 China Department of Information Management Chaoyang University of Technology Taichung41349 Taiwan

The gannet optimization algorithm (GOA) is an effective group intelligence algorithm inspired by the foraging behavior of gannets. Despite its merits, considerable potential exists for enhancing its exploration and convergence capabilities. A gannet optimization algorithm improved by the quasi-affine transformation evolutionary algorithm, restart strategy, and elite selection strategy (QRE-GOA) is proposed in this paper. The algorithm employs the quasi-affine transformation evolutionary algorithm (QUATRE) to matrix the gannet optimization algorithm to search for optimal solutions more accurately. The use of a restart strategy and elite selection can be more effective in preventing the algorithm from falling into local optimal solutions. Comparative experiments were conducted using the CEC2017 benchmark series. A detailed analysis was conducted, we per-formed a detailed analysis to check the accuracy and convergence speed of the algorithm search in different dimensions. Subsequently, a comprehensive analysis was undertaken to assess the accuracy and convergence speed of the algorithm’s search process across various dimensions. The experimental statistical results show that the new algorithm has stronger exploration ability and better convergence ability than the original algorithm. In addition, this paper focuses on process justification so that the experimental results are reliable, trustworthy, and interpretable. Finally, the newly proposed QRE-GOA algorithm is deployed to address the 3D Coverage challenge in Wireless Sensor Networks. Experimental findings unequivocally demonstrate the superior performance of QRE-GOA. These results, underscore its potential for addressing existing limitations in optimization methods. © 2024, Taiwan Ubiquitous information CO LTD. All rights reserved.

关键词： Evolutionary algorithms

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A novel dominating set and centrality based graph convolutional network for node classification

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Multimedia Tools and Applications 2025年第14期84卷 12979-13004页

作者： Garg, Neeraj Garg, Sneha Jadeja, Mahipal Meena, Yogesh Kumar Gopalani, Dinesh Chauhan, Ganpat Singh Department of Computer Science and Engineering Malaviya National Institute of Technology Jaipur Rajasthan Jaipur302017 India Department of Computer Science and Engineering Swami Keshvanand Institute of Technology Jaipur Rajasthan Jaipur302017 India Department of Information Technology School of IT Manipal University Jaipur Rajasthan Jaipur302034 India

To learn and analyze graph-structured data, Graph Neural Networks (GNNs) have emerged as a powerful framework over traditional neural networks, which work well on grid-like or sequential structure data. GNNs are particularly designed to record dependencies and relations in large and complicated networks such as social networks, molecular structures, citation networks and recommendation systems. The main idea behind GNN is the message-passing mechanism that allows sharing of information between nodes and edges in a graph network, enabling the model to learn about graph structures and perform well even with limited training data. GNNs have three different architectures: Graph Convolutional Network (GCN), GraphSAGE, Graph Attention Model (GAT) and other hybrid models. In this paper, we have proposed novel GCN models namely dominating Set with Degree Centrality based GCN (DS_DC_GCN) and dominating Set with Betweenness Centrality based GCN (DS_BWC_GCN) model, for node classification task. In the traditional GNN models, training nodes are selected randomly. In our proposed models, we first identify a subset of the nodes with the help of a dominating set (D) of the underlying network. Then we consider final training nodes (say D′) from set D based on the centrality scores (degree or betweenness). We give preference to nodes having high centrality scores for training. To evaluate the performance of the proposed models, we have used three standard citation network datasets - Cora, Citeseer and Pubmed. Our models obtain improved node classification performance as compared to other state-of-the-art-methods. The consideration of high centrality nodes of dominating set in training helps to improve the feature availability in the underlying computational graphs, which in turn improves learning and hence, model performance. These models also have the potential to offer better results for other network tasks, including link prediction and community detection. © The Author(s), under

关键词： Graph neural networks

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Machine Learning Driven Analysis of Mental Health Indicators in Social Media Posts 5

Machine Learning Driven Analysis of Mental Health Indicators...

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Garg, Kamal Garg, Srishti Dixit, Himanshu Pandey, Kavita Jaypee Institute of Information Technology Department of Computer Science & Engineering and Information Technology Noida2311 India

ISBN: (纸本)9798350379945

Mental stress has been a major issue among all age groups today. This is more like a stigma in the society where people don't even want to talk about it openly leading to severe health issues. Nowadays, people are deeply engaged with social media platforms for connection and self-expression of their emotions. They express all these through posts, photos, and comments. Thus, social media platforms can help in predicting the mental state and well-being of an individual. Prediction of a person's mental health can help to undertake some preventive measures. Various researchers have worked around this topic but there is no evidence of utilizing the most active web of people for obtaining the most accurate results. Machine Learning (ML) algorithms are used in this research study to identify whether a person is mentally stressed or not based on the social media data obtained from the user's post or by using a text input of around hundred words from users related to their day-to-day activities. This research study has used a decision tree, random forest, and Bidirectional Encoder Representations from Transformers (referred as BERT onwards) model classifier to train models. This study has used a dataset of Reddit (a social media platform) posts text data for training the models. The primary objective is to compare the efficacy of various machine learning models in predicting mental health based on social media data. © 2024 IEEE.

关键词： Adversarial machine learning

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An Adaptive Strategy for Dynamic Resource Allocation and Scheduling for Multitasking NoC based Multicore Systems 28

An Adaptive Strategy for Dynamic Resource Allocation and Sch...

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28th International Symposium on VLSI Design and Test, VDAT 2024

作者： Paul, Suraj Indian Institute of Engineering Science and Technology Dept. of Information Technology West Bengal India

ISBN: (纸本)9798350380101

Workloads on multicore platforms are increasingly becoming dynamic. Resource assignment decisions of applications impact their timing response and system performance, especially for Network-on-Chip (NoC) based multicore systems hosting real-time applications. Such systems offer better scope for parallel application execution. Design-time solutions for task allocation may not be suitable for dynamic workloads. In such scenarios, dynamic task allocation techniques give improvement in system resource utilization and lends adaptivity to changing application workload. This work presents an efficient and scalable adaptive technique for joint allocation and scheduling of tasks of mixed critical applications at runtime. Potential allocation solutions are explored at design-time by focusing on timing characteristics of applications and their communication energy consumption while considering a set of multitasking processors. These design-points are reconfigured dynamically at runtime for adapting the resource allocation decisions of multiple applications depending on platform resources and application requirements. Simulation results demonstrate that the proposed strategy achieves 20.4% and 33.5% improvement in communication energy consumption and timing satisfaction of tasks of allocated applications, respectively. Also, the run-time overhead of online reconfiguration reduces by 27.8% with respect to the existing dynamic resource allocation approaches. © 2024 IEEE.

关键词： Multitasking

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Multi-scale feature fusion for facial expression recognition

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Neural Computing and Applications 2025年第17期37卷 11399-11420页

作者： Guo, Jiatao Peng, Junjie Huang, Yansong Chen, Gan Cai, Zesu Tan, Shuhua School of Computer Engineering and Science Shanghai University Shanghai China Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai China School of Computer Science and Technology Harbin Institute of Technology Harbin China National Engineering Laboratory for Logistics Information Technology YTO Express Co. Ltd. Shanghai China

As an important computer vision task that can be used in many areas, facial expression recognition (FER) has been widely studied which much progress has been obtained especially when deep learning (DL) approaches have been introduced in FER. However, existing methods are restricted to extracting localized information or utilizing attention mechanism to focus on the region most relevant to the emotion, possibly resulting in errors in facial expression interpretation. Moreover, the inconsistency of image sizes in the wild FER datasets further increases the difficulty of the FER task. To address these issues, we propose a multi-scale feature fusion (MSFF) for FER. Specifically, our method first extracts features from facial images using multi-scale feature extraction to obtain rich multi-dimensional information. Meanwhile, extensive feature extraction at different scales is achieved by expanding the receptive field of the model, which provides the basis for more detailed analysis of facial expressions. Eventually, features at different scales are fused to enhance their synergistic effect. Subsequently, the adaptive extraction of comprehensive and valuable information from the fused features is performed, optimizing the performance of expression recognition. Extensive experimental results demonstrate that the proposed method outperforms the state-of-the-art methods on in-the-wild datasets. It is promising for FER in realistic and complex scenarios. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Feature extraction

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Organ Harbour: A Blockchain Solution for Organ Donation and Transplantation 2024

Organ Harbour: A Blockchain Solution for Organ Donation and ...

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16th International Conference on Contemporary Computing, IC3 2024

作者： Bansal, Nikita Jain, Samyak Nishal, Ribha Pandey, Kavita Department of Computer Science & Engineering and Information Technology Jaypee Institute of Information Technology Noida201309 India

ISBN: (纸本)9798400709722

Organ donation and transplantation have long been critical medical procedures, but they face numerous challenges, including scarcity, malpractices, and complex allocation processes. Also, they are vulnerable to the single point of failure problem due to centralization. Unlike existing blockchain solutions that only cover aspects like donor-recipient matching or medical record storage, our approach integrates hospitals into the system to ensure fair allocation of organs. The proposed organ donation decentralized app Organ Harbour utilizes Ethereum blockchain-based solution to automate this process in decentralized way, also it ensures transparency, privacy, security, and trustworthiness. This ensures secure and transparent registration of donor-patient information, including medical IDs, blood types, and organ details. Organ Harbour streamlines the process, eliminating third-party involvement and reducing transplantation costs. Moreover, by leveraging blockchain's immutability and smart contract capabilities, the system enhances data integrity and prediction accuracy. Overall, this research presents an approach to organ donation and transplantation, fostering security, transparency, and an efficient healthcare ecosystem. © 2024 Copyright held by the owner/author(s).

关键词： Blockchain

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