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arXiv 2023年

作者： Moncy, Megha Afreen, Sadia Purkayastha, Saptarshi Department of BioHealth Informatics Luddy School of Informatics Computing and Engineering Indiana University IndianapolisIN United States

This research examines the pivotal role of human behavior in the realm of healthcare data management, situated at the confluence of technological advancements and human conduct. An in-depth analysis spanning of security breaches in the United States from 2009 to the present elucidates the dominance of human-induced security breaches. While technological weak points are certainly a concern, our study highlights that a significant proportion of breaches are precipitated by human errors and practices, thus pinpointing a conspicuous deficiency in training, awareness, and organizational architecture. In spite of stringent federal mandates, such as the Health Insurance Portability and Accountability Act (HIPAA) and the Health Information Technology for Economic and Clinical Health (HITECH) Act, breaches persist, emphasizing the indispensable role of human factors within this domain. Such oversights not only jeopardize patient data confidentiality but also undermine the foundational trust inherent in the healthcare infrastructure. By probing the socio-technical facets of healthcare security infringements, this article advocates for an integrated, dynamic, and holistic approach to healthcare data security. The findings underscore the imperative of augmenting technological defenses while concurrently elevating human conduct and institutional ethos, thereby cultivating a robust and impervious healthcare data management environment. © 2023, CC BY.

关键词： Cost effectiveness

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Deep reinforcement learning with time-scale invariant memory

arXiv

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arXiv 2024年

作者： Kabir, Md Rysul Mochizuki-Freeman, James Tiganj, Zoran Department of Computer Science Luddy School of Informatics Computing and Engineering Indiana University Bloomington United States

The ability to estimate temporal relationships is critical for both animals and artificial agents. Cognitive science and neuroscience provide remarkable insights into behavioral and neural aspects of temporal credit assignment. In particular, scale invariance of learning dynamics, observed in behavior and supported by neural data, is one of the key principles that governs animal perception: proportional rescaling of temporal relationships does not alter the overall learning efficiency. Here we integrate a computational neuroscience model of scale invariant memory into deep reinforcement learning (RL) agents. We first provide a theoretical analysis and then demonstrate through experiments that such agents can learn robustly across a wide range of temporal scales, unlike agents built with commonly used recurrent memory architectures such as LSTM. This result illustrates that incorporating computational principles from neuroscience and cognitive science into deep neural networks can enhance adaptability to complex temporal dynamics, mirroring some of the core properties of human learning. © 2024, CC BY.

关键词： Deep reinforcement learning

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Cross-Modality Perturbation Synergy Attack for Person Re-identification 38

Cross-Modality Perturbation Synergy Attack for Person Re-ide...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Gong, Yunpeng Zhong, Zhun Qu, Yansong Luo, Zhiming Ji, Rongrong Jiang, Min School of Informatics Xiamen University China School of Computer Science and Information Engineering Hefei University of Technology China The Department of Artificial Intelligence Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China School of Informatics Key Laboratory of Digital Protection and Intelligent Processing of Intangible CulturalHeritage of Fujian and Taiwan Ministry of Culture and Tourism Xiamen University Fujian Xiamen361005 China

In recent years, there has been significant research focusing on addressing security concerns in single-modal person re-identification (ReID) systems that are based on RGB images. However, the safety of cross-modality scenarios, which are more commonly encountered in practical applications involving images captured by infrared cameras, has not received adequate attention. The main challenge in cross-modality ReID lies in effectively dealing with visual differences between different modalities. For instance, infrared images are typically grayscale, unlike visible images that contain color information. Existing attack methods have primarily focused on the characteristics of the visible image modality, overlooking the features of other modalities and the variations in data distribution among different modalities. This oversight can potentially undermine the effectiveness of these methods in image retrieval across diverse modalities. This study represents the first exploration into the security of cross-modality ReID models and proposes a universal perturbation attack specifically designed for cross-modality ReID. This attack optimizes perturbations by leveraging gradients from diverse modality data, thereby disrupting the discriminator and reinforcing the differences between modalities. We conducted experiments on three widely used cross-modality datasets, namely RegDB, SYSU, and LLCM. The results not only demonstrate the effectiveness of our method but also provide insights for future improvements in the robustness of cross-modality ReID systems. © 2024 Neural information processing systems foundation. All rights reserved.

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Knowledge Base Processing Method Based on Text Classification Algorithm 5

Knowledge Base Processing Method Based on Text Classificatio...

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5th IEEE International Conference on Artificial Intelligence in engineering and Technology, IICAIET 2023

作者： Zhong, Baisheng Sainin, Mohd Shamrie Fun, Tan Soo Universiti Malaysia Sabah Faculty of Computing and Informatics Sabah Kota Kinabalu Malaysia School of Engineering Guangzhou College of Technology and Businces Guangzhou China Research Centre Sabah Kota Kinabalu Malaysia

ISBN: (纸本)9798350304152

The text classification algorithm's knowledge base processing method utilizes existing data from the knowledge base to guide the construction and training of the classification model. In practical application, the knowledge base processing method has been prove to have a good performance in text classification tasks. The utilization of knowledge base processing method in text classification has led to an average accuracy improvement of more than 17%. Furthermore, this method significantly reduces labeling costs by approximately 70% compared to traditional approaches. Recently, knowledge base processing methods have been widely used in supporting business applications, social media analysis and other fields. This paper proposes a knowledge base method to establish a feature model related to domain speciality and combine it with traditional text classification algorithm, so as to optimize the training and reasoning process of the classification model and improve the accuracy of classification effect. Lastly, we suggested strategies to overcome the shortcoming of the knowledge base method in improving the construction and training of the classification model. © 2023 IEEE.

关键词： Classification (of information)

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Password Manager with Multi-Factor Authentication 1

Password Manager with Multi-Factor Authentication

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1st IEEE International Conference on Networking and Communications, ICNWC 2023

作者： Dhanalakshmi, R. Vijayaraghavan, N. Narasimhan, S. Basha, Saleem Vellore Institute of Technology School of Computer Science and Engineering Chennai India KCG College of Technology Department of Mathematics Chennai India Mazoon University College Computing and Informatics Department muscat Oman

ISBN: (纸本)9798350336009

Data breach is a serious issue as it leaks the personal information of more than billions of users and their privacy is compromised. More than 77% of organizations do not have a Cyber Security Incident Response plan. So, it is necessary to be informed of network security and ways to store and generate secured passwords. Having different and random passwords for ones digital accounts can exponentially increase the security of user's data. The goal of this project is to build a password manager which can securely store and encrypt passwords and other data. The multifactor authentication will provide increased security to validate the user into password management system. Multifactor authentication system includes physical security key and graphical password authentication. © 2023 IEEE.

关键词： Cryptography

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Performance Analysis of Three Spectrum Sensing Detection Techniques with Ambient Backscatter Communication in Cognitive Radio Networks

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Computer Modeling in engineering & Sciences 2023年第10期137卷 813-825页

作者： Shayla Islam Anil Kumar Budati Mohammad Kamrul Hasan Saoucene Mahfoudh Syed Bilal Hussian Shah Institute of Computer Science&Digital Innovation ICSDIUCSI UniversityKuala Lumpur56000Malaysia Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan Malaysia(UKM)Selangor43600Malaysia School of Engineering Computing and InformaticsDar Al-Hekma UniversityJeddah22246Saudi Arabia

In wireless communications, the Ambient Backscatter Communication (AmBC) technique is a promisingapproach, detecting user presence accurately at low power levels. At low power or a low Signal-to-Noise Ratio(SNR), there is no dedicated power for the users. Instead, they can transmit information by reflecting the ambientRadio Frequency (RF) signals in the spectrum. Therefore, it is essential to detect user presence in the spectrum forthe transmission of data without loss or without collision at a specific time. In this paper, the authors proposed anovel Spectrum Sensing (SS) detection technique in the Cognitive Radio (CR) spectrum, by developing the *** Matched Filter Detection with Inverse covariance (MFDI), Cyclostationary Feature Detection with Inversecovariance (CFDI) and Hybrid Filter Detection with Inverse covariance (HFDI) approaches are used with AmBCto detect the presence of users at low power levels. The performance of the three detection techniques is measuredusing the parameters of Probability of Detection (PD), Probability of False Alarms (Pfa), Probability of MissedDetection (Pmd), sensing time and throughput at low power or low SNR. The results show that there is a significantimprovement via the HFDI technique for all the parameters.

关键词： Ambient backscatter communication cognitive radio MFDI CFDI HFDI

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MultiSFL: Towards Accurate Split Federated Learning via Multi-Model Aggregation and Knowledge Replay 39

MultiSFL: Towards Accurate Split Federated Learning via Mult...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Xia, Zeke Hu, Ming Yan, Dengke Liu, Ruixuan Li, Anran Xie, Xiaofei Chen, Mingsong MoE Engineering Research Center of SW/HW Co-Design Tech. and App. East China Normal University China School of Computing and Information Systems Singapore Management University Singapore Department of Biomedical Informatics & Data Science School of Medicine Yale University United States

ISBN: (纸本)157735897X

Although Split Federated Learning (SFL) effectively enables knowledge sharing among resource-constrained clients, it suffers from low training performance due to the neglect of data heterogeneity and catastrophic forgetting problems. To address these issues, we propose a novel SFL approach named MultiSFL, which adopts i) an effective multi-model aggregation mechanism to alleviate gradient divergence caused by heterogeneous data and ii) a novel knowledge replay strategy to deal with the catastrophic forgetting problem. MultiSFL adopts two servers (i.e., the fed server and main server) to maintain multiple branch models for local training and an aggregated master model for knowledge sharing among branch models. To mitigate catastrophic forgetting, the main server of MultiSFL selects multiple assistant devices for knowledge replay according to the training data distribution of each full branch model. Experimental results obtained from various non-IID and IID scenarios demonstrate that MultiSFL significantly outperforms conventional SFL methods by up to a 23.25% test accuracy improvement. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Federated learning

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A Logical Investigation of Stock Market Prediction and Analysis using Supervised Machine Learning Algorithm 1

A Logical Investigation of Stock Market Prediction and Analy...

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1st IEEE International Conference on Networking and Communications, ICNWC 2023

作者： Dhanalakshmi, R. Kumar, V. Vasantha Basha, Saleem Vijayaraghavan, N. Vellore Institute of Technology School of Computer Science and Engineering Chennai India Mazoon University College Computing and Informatics Department Muscat Oman KCG College of Technology Department of CSE Chennai India

ISBN: (纸本)9798350336009

In the Field of computer science, artificial intelligence (AI) is a broad field, which is concerned with structuring smart products and machines able to perform tasks which require the intellectual capability of humans. Artificial Intelligence is in need for Stock trading's future. Robotic advisers examine millions of pieces of data using artificial intelligence and carry out the analysis at the best possible price. Market forecasts are more precise and effective thanks to the analysts. For providing higher returns, the trading firms efficiently mitigate the risk using Artificial Intelligence Techniques. Our proposed Stock Market system is a Web Application which uses the Random Forest Algorithm. The Random Forest Algorithm comes under Supervised Learning. With the system, one can forecast the stock prices for the subsequent days. © 2023 IEEE.

关键词： Forecasting

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Multi-agent Traffic Light Controls with SUMO 8

Multi-agent Traffic Light Controls with SUMO

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8th International Conference on Business and Industrial Research, ICBIR 2023

作者： Phon-Amnuaisuk, Somnuk Tan, Soon-Jiann Yap, Yok-Hoe Choong, Florence Chiao-Mei Vejjanugraha, Pikul Khor, Kok-Chin Ng, Keng-Hoong School of Computing and Informatics Universiti Teknologi Brunei Gadong Brunei Universiti Teknologi Brunei Faculty of Engineering Gadong Brunei School of Engineering and Physical Sciences Heriot-Watt University Malaysia Campus Malaysia Thai-Nichi Institute of Technology Department of International College Thailand Universiti Tunku Abdul Rahman Lkc Faculty of Engineering and Science Malaysia School of Computing Asia Pacific University of Technology and Innovation Malaysia

ISBN: (纸本)9798350399646

This study focuses on the optimization of Traffic Light System (TLS) control through the use of adaptive agents. The performance of adaptive cycle TLS was compared with fixed cycle TLS. Two different adaptive cycle TLS agents were investigated, reactive and Deep Q-Network (DQN) agents. The reactive agent adjusted its control signals based on traffic measures such as queue length, while the DQN agent employed a deep reinforcement learning algorithm to determine its control signals. Two sets of simulations were conducted to evaluate the performance of both approaches. The results showed that the adaptive cycle TLS was more effective in reducing waiting times and increasing traffic throughput than the fixed cycle TLS. Among the adaptive agents, the reactive agent outperformed the DQN agent, due to the difficulty in learning an optimal policy in the traffic control domain, which has a non-stationary and complex nature. This preliminary study showed the potential benefits of using adaptive agents for traffic light control, and further studies in various areas such as employing more advanced TLS control methods, expanding the scale of the study, and applying real-demand in the simulation, could be carried out in future work. © 2023 IEEE.

关键词： Reinforcement learning

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Advanced Persistent Threat Identification with Boosting and Explainable AI

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SN Computer Science 2023年第3期4卷 271页

作者： Hasan, Md. Mahadi Islam, Muhammad Usama Uddin, Jasim Department of Computer Science and Engineering Asian University of Bangladesh Ashulia Dhaka 1341 Bangladesh School of Computing and Informatics University of Louisiana at Lafayette 104 E University Ave Lafayette 70503 LA United States Department of Applied Computing and Engineering Cardiff School of Technologies Cardiff Metropolitan University Western Avenue Wales Cardiff CF5 2YB United Kingdom

Advanced persistent threat (APT) is a serious concern in cyber-security that has matured and grown over the years with the advent of technology. The main aim of this study is to establish an effective identification model for APT attacks to prevent and reduce their influence. Machine learning has the potential as well as substantial background to detect and predict cyber-security threats including APT. This study utilized several boosting-based machine learning methods to predict various types of APTs that are consistent in cyber-security domain. Furthermore, Explainable Artificial Intelligence (XAI) was coupled with the predictions to provide actionable insights to the domain stakeholders as well as practitioners in this domain. The results, particularly XGBoost with weighted F1 score of 0.97 and SHapley Additive exPlanations (SHAP)-based explanation, prove that boosting methods as well as machine learning models paired with XAI are indeed promising in handling cyber-security-related dataset problems which can be extrapolated towards new avenues of challenging research by effectively deploying boosting-based XAI models. © 2023, The Author(s).

关键词： APT Boosting Cyber-security Explainability Machine learning XAI

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