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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Subba Rao, P.V.R. Polisetty, Venkata Ramana Murthy Jayanth, K Krishna Sai Manoj, N.V. Mohith, Vankadari Kumar, R. Prasanna Amrita Vishwa Vidyapeetham School of Computing Computer Science and Engineering Chennai India

ISBN: (纸本)9798350327533

The paper uses Deep Reinforcement Learning (DRL) to traffic signal regulation, solving urban traffic congestion. Our research paper demonstrates the simulation of intricate traffic conditions with microscopic accuracy, utilizing the Simulation of Urban Mobility (SUMO) platform. As the foundation for DRL, a state representation and action space are established. The learning process is guided by the Deep Q-Network (DQN) architecture, precisely set hyperparameters, and epsilon-greedy exploration. To improve stability, training data is gathered and stored in an experience replay buffer. The DQN-based technology is built into the SUMO simulation and allows for dynamic traffic signal modifications. Updates to the target network on a regular basis increase stability. Metrics for evaluation include average waiting time, traffic flow efficiency, and congestion levels, which compare favorably to baseline approaches. In conclusion, this study shows the usefulness of DRL in optimizing traffic signal control, providing viable strategies to minimize urban traffic congestion and increase transportation efficiency. © 2024 IEEE.

关键词： Congestion Reduction Deep Reinforcement Learning DQN Architecture Epsilon-Greedy Exploration SUMO Traffic Signal Control

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meMIA: Multilevel Ensemble Membership Inference Attack

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第1期6卷 93-106页

作者： Ullah, Najeeb Aman, Muhammad Naveed Sikdar, Biplab National University of Singapore Department of Electrical and Computer Engineering 117583 Singapore University of Nebraska-Lincoln School of Computing LincolnNE68588 United States

Leakage of private information in machine learning models can lead to breaches of confidentiality, identity theft, and unauthorized access to personal data. Ensuring the safe and trustworthy deployment of AI systems necessitates addressing privacy concerns to prevent unintentional disclosure and discrimination. One significant threat, membership inference (MI) attacks, exploit vulnerabilities in target learning models to determine if a given sample was part of the training set. However, the effectiveness of existing MI attacks is often limited by the number of classes in the dataset or the need for diverse multilevel adversarial features to exploit overfitted models. To enhance MI attack performance, we propose meMIA, a novel framework based on stacked ensemble learning. meMIA integrates embeddings from a neural network (NN) and a long short-term memory (LSTM) model, training a subsequent NN, termed the meta-model, on the concatenated embeddings. This method leverages the complementary strengths of NN and LSTM models;the LSTM captures order differences in confidence scores, while the NN discerns probability distribution differences between member and nonmember samples. We extensively evaluate meMIA on seven benchmark datasets, demonstrating that it surpasses current state-of-the-art MI attacks, achieving accuracy up to 94.6% and near-perfect recall. meMIA's superior performance, especially on datasets with fewer classes, underscores the urgent need for robust defenses against privacy attacks in machine learning, contributing to the safer and more ethical use of AI technologies. © 2024 IEEE.

关键词： Information leakage

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Characterization of Exact One-Query Quantum Algorithms for Partial Boolean Functions

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Journal of computer Science & Technology 2023年第6期38卷 1423-1430页

作者：叶泽坤李绿周 Institute of Quantum Computing and Computer Theory School of Computer Science and EngineeringSun Yat-sen UniversityGuangzhou 510006China Ministry of Education Key Laboratory of Machine Intelligence and Advanced Computing Sun Yat-sen UniversityGuangzhou 510006China

The query model(or black-box model)has attracted much attention from the communities of both classical and quantum ***,quantum advantages are revealed by presenting a quantum algorithm that has a better query complexity than its classical *** the history of quantum algorithms,the Deutsch algorithm and the Deutsch-Jozsa algorithm play a fundamental role and both are exact one-query quantum *** leads us to con-sider the problem:what functions can be computed by exact one-query quantum algorithms?This problem has been ad-dressed in the literature for total Boolean functions and symmetric partial Boolean functions,but is still open for general partial Boolean ***,in this paper,we continue to characterize the computational power of exact one-query quantum algorithms for general partial Boolean ***,we present several necessary and sufficient conditions for a partial Boolean function to be computed by exact one-query quantum ***,inspired by these conditions,we discover some new representative functions that can be computed by exact one-query quantum algorithms but have an essential difference from the already known ***,it is worth pointing out that before our work,the known func-tions that can be computed by exact one-query quantum algorithms are all symmetric functions and the quantum algo-rithm used is essentially the Deutsch-Jozsa algorithm,whereas the functions discovered in this paper are generally asym-metric and new algorithms to compute these functions are ***,this expands the class of functions that can be computed by exact one-query quantum algorithms.

关键词： quantum computing quantum query complexity quantum algorithm

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Augmenting Clinical Decisions with Deep Learning Lung Cancer Image Abnormality Segmentation 14

Augmenting Clinical Decisions with Deep Learning Lung Cancer...

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14th International Conference on Cloud computing, Data Science and engineering, Confluence 2024

作者： Venkatraman, K. Reddy, Sirigiri Naga Pavan Sathvik Amrita Vishwa Vidyapeetham School of Computing Computer Science and Engineering Chennai India

ISBN: (纸本)9798350344837

Lung cancer is a major global cause of death, highlighting the critical need for quick and accurate detection methods. The exploration of computational alternatives arose from the standard way of manually processing CT images, which is time-consuming and error-prone. In this work, we combine the advantages of Support Vector Machine (SVM) and VGG16 classifiers to provide a novel method for enhancing lung cancer diagnosis. By analyzing the 'IQ-OTH/NCCD' dataset, our hybrid model-which combines the VGG 16 and SVM algorithms-performs admirably in differentiating between aggressive, benign, and normal lung cancer cases. This combination of traditional machine learning with deep learning tackles accuracy and efficiency issues, which is a promising development over current diagnostic methods. We conduct a comprehensive comparative analysis with prominent architectures to select the optimal model based on accuracy, efficiency, and resource requirements. In addition to introducing the VGG 16+SVM model, our research provides valuable insights into deep learning architectures, with the ultimate goal of advancing precise and efficient diagnosis of lung cancer, which is crucial in combating this global health challenge in the future. © 2024 IEEE.

关键词： computerized tomography

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ConfigMaster: An Interactive Solution for System Management 8

ConfigMaster: An Interactive Solution for System Management

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Ganapathi, Ananya Sivakumar, Parikshith Divya, K.V. Elango, Aditya Gupta, Hardik Amrita School of Computing Dept. of Computer Science Engineering Bengaluru India

ISBN: (纸本)9798331505462

The 'ConfigMaster: An interactive solution for system management' is a simple utility written using Bash scripting. It is designed for Unix-like systems. The tool lets users easily configure system settings and do administrative tasks without having to remember complex commands. The tool minimizes user's work by automating tasks. The tool can be used even by users with very little technical knowledge, as it has a simple menu-based interface to manage things like network settings, software packages, and wireless connections directly from the terminal. By automating common tasks, the tool makes system management more efficient and accessible. By automating common tasks, the tool makes system management more efficient and accessible. It's very useful to simplify system configuration on Unix-like systems using scripting languages like Bash. 'ConfigMaster' aims to offer a smooth user experience with the incorporation of core management principles from an operating system, such as disk scheduling, process synchronization, and memory management etc. Being flexible itself, this tool will bring further improvements to make effective responses to emerging user needs. The framework being flexible, it can be expanded to add new features to configure under different scenarios. By integrating essentials operating system operations, 'ConfigMaster' enables users to efficiently perform system configurations along with promoting ease of use. © 2024 IEEE.

关键词： Wi-Fi

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QuSIM-Enhanced GSM Security: A Quantum Prover Authentication Protocol (QuPAP) for Mobile Communication

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IEEE Internet of Things Journal 2025年第13期12卷 23036-23060页

作者： Khan, Mansoor Ali Aman, Muhammad Naveed Sikdar, Biplab National University of Singapore Department of Electrical and Computer Engineering Singapore Singapore University of Nebraska-Lincoln School of Computing LincolnNE68588 United States

As the world rapidly embraces quantum technologies, the need for robust quantum security protocols becomes increasingly paramount. Quantum Key Distribution (QKD) has been at the forefront of secure key exchange, but establishing a root of trust remains unaddressed. This research article presents a pioneering approach for Global System for Mobile Communications (GSM) that bridges the gap between QKD and device identity verification. Our approach utilizes single-qubit states and amplitude encoding, integrating the BB84 protocol to securely share secret keys between entities. We implement two-factor authentication (2FA) to further protect against attacks and unauthorized access. Unlike entangled state-based schemes that require quantum memory and face practical implementation challenges, our single-qubit approach avoids these issues, making it feasible with current technology. Central to our approach is the verification of the SIM card holder’s authenticity using the Quantum Prover Authentication Protocol (QuPAP) at the mobile Authentication Center. This quantum cryptography-based process enhances GSM communication security and can be integrated into existing networks with minimal modifications. Our proposed smartphone, equipped with dual SIM capabilities–one conventional (cSIM) and one quantum (QuSIM)–ensures compatibility with both current and future networks, allowing the benefits of quantum security without requiring a complete system overhaul. By integrating quantum security into classical GSM protocols, our scheme not only enhances security but also addresses crucial aspects of device identity authentication, attestation, and trust establishment. The security and performance analysis of the QuPAP prototype demonstrates a quantum leap in mobile security, fostering a future of trust, privacy, and resilience in the ever-evolving landscape of communication technologies. © 2014 IEEE.

关键词： Authentication Protocol

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Leveraging AI to Compromise IoT Device Privacy by Exploiting Hardware Imperfections

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第6期6卷 1561-1574页

作者： Baig, Mirza Athar Iqbal, Asif Aman, Muhammad Naveed Sikdar, Biplab University of Nebraska–Lincoln School of Computing LincolnNE68588 United States National University of Singapore Department of Electrical and Computer Engineering 117583 Singapore

The constrained design, remote deployment, and sensitive data generated by Internet of Things (IoT) devices make them susceptible to various cyberattacks. One such attack is profiling IoT devices by tracking their packet transmissions. While existing methods mitigate these attacks using pseudonymous identities, we propose a novel attack strategy that exploits the physical layer characteristics of IoT devices. Specifically, we demonstrate how an attacker can leverage features extracted from device transmissions to identify packets originating from the same device. Once identified, the attacker can isolate the device's signals and potentially determine its physical location. This attack exploits the fact that microcontroller clock variations exist across devices, even within the same model line. By extracting transmission features and training machine learning (ML) models, we accurately identify the originating device of the packets. This study reveals inherent privacy vulnerabilities in IoT systems due to hardware imperfections that are beyond user control. These limitations have profound implications for the design of security frameworks in emerging ubiquitous sensing environments. Our experiments demonstrate that the proposed attack achieves 99% accuracy in real-world settings and can bypass privacy measures implemented at higher protocol layers. This work highlights the urgent need for privacy protection strategies across multiple layers of the IoT protocol stack. © 2020 IEEE.

关键词： Differential privacy

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A Reinforcement Learning Based Approach to Partition Testing

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计算机科学技术学报（英文版） 2025年第1期40卷 99-118页

作者：孙昌爱肖明俊代贺鹏刘淮 1School of Computer and Communication Engineering University of Science and Technology Beijing Beijing 100083 China 2Department of Computing Technologies Swinburne University of Technology Hawthorn VIC 3122 Australia

Partition testing is one of the most fundamental and popularly used software testing *** first di-vides the input domain of the program under test into a set of disjoint partitions,and then creates test cases based on these *** by the theory of software cybernetics,some strategies have been proposed to dynamically se-lect partitions based on the feedback information gained during *** basic intuition of these strategies is to assign higher probabilities to those partitions with higher fault-detection potentials,which are judged and updated mainly ac-cording to the previous test *** a feedback-driven mechanism can be considered as a learning process—it makes decisions based on the observations acquired in the test ***,advanced learning techniques could be leveraged to empower the smart partition selection,with the purpose of further improving the effectiveness and efficiency of partition *** this paper,we particularly leverage reinforcement learning to enhance the state-of-the-art adaptive partition testing *** algorithms,namely RLAPT_Q and RLAPT_S,have been developed to implement the proposed *** studies have been conducted to evaluate the performance of the proposed approach based on seven object programs with 26 *** experimental results show that our approach outperforms the existing partition testing techniques in terms of the fault-detection capability as well as the overall testing *** study demonstrates the applicability and effectiveness of reinforcement learning in advancing the performance of software testing.

关键词： partition testing reinforcement learning temporal-difference learning intelligent software engineering

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Dynamics analysis and cryptographic implementation of a fractional-order memristive cellular neural network model

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Chinese Physics B 2024年第4期33卷 418-433页

作者：周新卫蒋东华 Jean De Dieu Nkapkop Musheer Ahmad Jules Tagne Fossi Nestor Tsafack 吴建华 Department of Information Engineering Gongqing CollegeNanchang UniversityJiujiang 332020China School of Computer Science and Engineering Sun Yat-Sen UniversityGuangzhou 511400China Department of Electrical Engineering and Industrial Computing University Institute of TechnologyDoualaCameroon Department of Computer Engineering Jamia Millia IslamiaNew Delhi 110025India Department of Physics Faculty of ScienceUniversity of YaoundeCameroon Electrical Engineering Department and Industrial Computing of ISTAMA University of DoualaDoualaCameroon

Due to the fact that a memristor with memory properties is an ideal electronic component for implementation of the artificial neural synaptic function,a brand-new tristable locally active memristor model is first proposed in this ***,a novel four-dimensional fractional-order memristive cellular neural network(FO-MCNN)model with hidden attractors is constructed to enhance the engineering feasibility of the original CNN model and its ***,its hardware circuit implementation and complicated dynamic properties are investigated on multi-simulation ***,it is used toward secure communication application *** it as the pseudo-random number generator(PRNG),a new privacy image security scheme is designed based on the adaptive sampling rate compressive sensing(ASR-CS)***,the simulation analysis and comparative experiments manifest that the proposed data encryption scheme possesses strong immunity against various security attack models and satisfactory compression performance.

关键词： cellular neural network memristor hardware circuit compressive sensing privacy data protection

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ARCHER:a ReRAM-based accelerator for compressed recommendation systems

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Frontiers of computer Science 2024年第5期18卷 147-160页

作者： Xinyang SHEN Xiaofei LIAO Long ZHENG Yu HUANG Dan CHEN Hai JIN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabClusters and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China

Modern recommendation systems are widely used in modern data *** random and sparse embedding lookup operations are the main performance bottleneck for processing recommendation systems on traditional platforms as they induce abundant data movements between computing units and ***-based processing-in-memory(PIM)can resolve this problem by processing embedding vectors where they are ***,the embedding table can easily exceed the capacity limit of a monolithic ReRAM-based PIM chip,which induces off-chip accesses that may offset the PIM ***,we deploy the decomposed model on-chip and leverage the high computing efficiency of ReRAM to compensate for the decompression performance *** this paper,we propose ARCHER,a ReRAM-based PIM architecture that implements fully yon-chip recommendations under resource ***,we make a full analysis of the computation pattern and access pattern on the decomposed *** on the computation pattern,we unify the operations of each layer of the decomposed model in multiply-and-accumulate *** on the access observation,we propose a hierarchical mapping schema and a specialized hardware design to maximize resource *** the unified computation and mapping strategy,we can coordinatethe inter-processing elements *** evaluation shows that ARCHER outperforms the state-of-the-art GPU-based DLRM system,the state-of-the-art near-memory processing recommendation system RecNMP,and the ReRAM-based recommendation accelerator REREC by 15.79×,2.21×,and 1.21× in terms of performance and 56.06×,6.45×,and 1.71× in terms of energy savings,respectively.

关键词： recommendation system ReRAM processing-in-memory embedding layer

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