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13th International Conference on Ambient Systems, Networks and Technologies, ANT 2022 / 5th International Conference on Emerging Data and Industry 4.0, EDI40 2022

作者： Shatnawi, Ahmed S. Yassen, Qussai Yateem, Abdulrahman Department of Computer Information Systems Jordan University of since and Technology Irbid Jordan Department of Network and Security Engineering Jordan University of since and Technology Irbid Jordan

In the past decade, mobile devices became necessary for modern civilization and contributed directly to its development stages in defining mobile information access. Nonetheless, along with these rapid developments in modern mobile devices, security issues rise dramatically, and malware is the most concerning of all. Therefore, many studies and research are still trending in this spectrum, using Machine Learning approaches to prevent and reduce malware's impact. This paper seeks to add to what is already a foundation of various malware detection efforts by presenting a static base classification approach for malware detection based on android permissions and API calls. This approach is based on three well-known Machine Learning algorithms, Support Vector Machines (SVM), K-nearest neighbors (KNN), and Naive Bayes (NB) against a comprehensive new Android malware dataset (CICInvesAndMal2019), in pursuit of achieving high malware detection rates and contribution to the efforts and studies in protecting the development of mobile information. access. © 2022 Elsevier B.V.. All rights reserved.

关键词： Android Malware CICInves KNN Machine Learning Mal2019 Dataset Malware Detection Mobile Information Access Naive Bayes SVM

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Prognostic Kalman Filter Based Bayesian Learning Model for Data Accuracy Prediction

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computers, Materials & Continua 2022年第7期72卷 243-259页

作者： S.Karthik Robin Singh Bhadoria Jeong Gon Lee Arun Kumar Sivaraman Sovan Samanta A.Balasundaram Brijesh Kumar Chaurasia S.Ashokkumar Department of ECE College of Engineering and TechnologySRM Institute of Science and TechnologyVadapalaniChennai600026India Department of CSE Birla Institute of Applied Sciences(BIAS)BhimtalUttarakhand263136India Division of Applied Mathematics Wonkwang University460Iksan-DaeroIksan-SiJeonbuk54538Korea School of Computer Science and Engineering Vellore Institute of Technology(VIT)Chennai600127India Department of Mathematics Tamralipta MahavidyalayaWest Bengal721636India School of Computer Science and Engineering Center for Cyber Physical SystemsVellore Institute of Technology(VIT)Chennai600127India Indian Institute of Information Technology(IIIT) LucknowUttar Pradesh226002India Department of Computer Science and Engineering Saveetha School of EngineeringSIMATSChennai602105India

Data is always a crucial issue of concern especially during its prediction and computation in digital *** paper exactly helps in providing efficient learning mechanism for accurate predictability and reducing redundant data *** also discusses the Bayesian analysis that finds the conditional probability of at least two parametric based predictions for the *** paper presents a method for improving the performance of Bayesian classification using the combination of Kalman Filter and *** method is applied on a small dataset just for establishing the fact that the proposed algorithm can reduce the time for computing the clusters from *** proposed Bayesian learning probabilistic model is used to check the statistical noise and other inaccuracies using unknown *** scenario is being implemented using efficient machine learning algorithm to perpetuate the Bayesian probabilistic *** also demonstrates the generative function forKalman-filer based prediction model and its *** paper implements the algorithm using open source platform of Python and efficiently integrates all different modules to piece of code via Common Platform Enumeration(CPE)for Python.

关键词： Bayesian learning model kalman filter machine learning data accuracy prediction

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Robust ANN Searching for Dynamic Vector Streams without Rebuilding Index

Robust ANN Searching for Dynamic Vector Streams without Rebu...

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computer, Big Data and Artificial Intelligence (ICCBD+AI), International Conference on

作者： Haowen Bian Lingwei Chao Jingxuan Su Wenhong Tian Ruini Xue School of Computer Science and Engineering (School of Cyber Security) University of Electronic Science and Technology of China Chengdu China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798331533991

ISBN: (纸本)9798331534004

Graph-based approximate nearest neighbour (ANN) algorithms have dominated vector indexing recently. However, some of them do not support incremental vector addition, while most of them do not support in-place deletion. Therefore, additional rebuilding overhead is required to keep the index updated. We propose HSG (Hybrid Stride Graph) to enable the vector index to accept dynamic vector streams. Inspired by the K-ANN graph and NSG, HSG constructs the index with short and long edges simultaneously without any preprocessing or prior dataset knowledge. Finally, HSG is evaluated against three well-known datasets and the experimental results indicate that HSG can deliver acceptable QPS with an excellent recall guarantee.

关键词： Heuristic algorithms Big Data Approximation algorithms Vectors Indexes Artificial intelligence Streams Indexing

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UZNER: A Benchmark for Named Entity Recognition in Uzbek 12th

UZNER: A Benchmark for Named Entity Recognition in Uzbek

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12th National CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2023

作者： Yusufu, Aizihaierjiang Jiang, Liu Ainiwaer, Abidan Teng, Chong Yusufu, Aizierguli Li, Fei Ji, Donghong Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Wuhan China School of Information Management Wuhan University Wuhan China School of Computer Science and Technology Xinjiang Normal University Urumqi China

ISBN: (纸本)9783031446924

Named entity recognition (NER) is a key task in natural language processing, and entity recognition can provide necessary semantic information for many downstream tasks. However, the performance of NER is often limited by the richness of language resources. For low-resource languages, NER usually performs poorly due to the lack of sufficient labeled data and pre-trained models. To address this issue, we manually constructed a large-scale, high-quality Uzbek NER corpus of Uzbek, and experimented with various NER methods. We improved state-of-the-art baseline models by introducing additional features and data translations. Data translation enables the model to learn richer syntactic structure and semantic information. Affix features provide knowledge at the morphological level and play an important role in identifying oversimplified low-frequency entity labels. Our data and models will be available to facilitate low-resource NER. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Semantics

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An Intranet Defense Capability Assessment Method for Network security Solutions Based on ATT&CK Framework 2nd

An Intranet Defense Capability Assessment Method for Network...

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2nd International Conference on Network Simulation and Evaluation, NSE 2023

作者： An, Qi Zhu, Zifeng Li, Shudong Han, Weihong Wu, Xiaobo Li, Shumei Cyberspace Institute of Advanced Technology GuangzhouUniversity Guangzhou510006 China Department of New Networks Peng Cheng Laboratory Shenzhen518000 China School of Computer Science and Cyber Engineering Guangzhou University Guangzhou510006 China Computer College Jilin Normal University Siping136000 China

ISBN: (纸本)9789819745180

Faced with the growing threat of intranet attacks and numerous network security solutions, the defensive capabilities of network security solutions need to be evaluated in order to reasonably select a network security solution. However, the existing assessment of the defense capability of network security solutions mainly focuses on anti-DDoS devices, and lacks the assessment of the defense capability of the intranet, and it is complicated to obtain the values of assessment indicators during the assessment process. In addition, it is also difficult to obtain ideas for defense improvement of network security solutions. In the face of this situation, we propose an intranet defense capability assessment method for network security solutions based on the ATT&CK framework from an attack perspective. This method introduces the ATT&CK framework into the assessment of the intranet defense capability of network security solutions, constructs an indicator system with comprehensiveness and good scalability, and involves tactics, techniques and sub-technologies in the enterprise framework used by 138 APT organizations in the ATT&CK framework as objective data for indicator assignment, balancing the influence of attackers’ subjective experiences and preferences on the assessment results, and this method can easily obtain ideas to improve the intranet defense capability of network security solutions. The effectiveness of the proposed evaluation method is verified by conducting simulation experiments at a network range. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Intranets

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Defensive Auto-Updatable and Adaptable Bot Recommender System (DAABRS): A New Architecture Approach in Cloud Computing Systems 6

Defensive Auto-Updatable and Adaptable Bot Recommender Syste...

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6th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2024

作者： Khwaldeh, Sufian Mohit, Y. Khushwant, S. Department of Network and Information Security College of lnformation Technology University of Fujairah 1207 United Arab Emirates Department of Information Technology Faculty of Information Technology and Systems The University of Jordan Aqaba77110 Jordan Department of Mathematics University Institute of Sciences Chandigarh University Punjab Mohali140413 India Department of Computer Science & Engineering University Institute of Engineering and Technology M. D. University Rohtak124001 India

ISBN: (纸本)9798350394634

Cloud server data security makes extensive use of data centers and instantly accessible edge systems for possible transactions involving sizable and confidential data. These Edge Systems are chosen for rental based on their usefulness in situations where new privacy and security difficulties must be overcome. The use of Edge Systems is subject to dynamic mobility, which increases the danger of attacking threats. With this suggested approach, the Defensive Auto-updatable and Adaptable Bot Recommender approach (DAABRS) is used by the Cloud Center to identify the safeguarded Edge System. Cloud Data Centers with Edge Systems contain a variety of users and environments, making it simple for attackers to enter anonymously and compromise Edge System privacy. The most effective security measure against increasing risks and assaults for Edge Systems that rent more cloud storage is to use Stealth Mode Bots inside of data segments. In addition to anticipating and averting data breaches in server partitioning, tenant systems, edge systems, and containers by integrating stealth mode bots into each. Suggested use cases: Tenants, Edge Systems, or Containers for effective security-enhanced Stealth Mode Bots that are activated when a particular data segment is unplugged. Updatable and adaptive bots are used in the suggested strategy to counter Edge strategy assaults. Additionally, it uses Stealth Mode Bots to stop heavily attacked Data Containers, and machine learning will be used to update specifications to identify and prevent further assaults. © 2024 IEEE.

关键词： Containers

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Enhancing Efficiency and Empowering Institutions: Leveraging Wireless Sensor Devices, IoT Edge Computing, and Fog Computing in Educational Systems 7

Enhancing Efficiency and Empowering Institutions: Leveraging...

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7th IEEE International Conference on Computational Systems and Information technology for Sustainable Solutions, CSITSS 2023

作者： Siddiqui, Shams Tabrez Ali Sohail, Md Mohammed Aftab, Aasif Sarfaraz, Mohd Alam, Mohammad Imran Salim, Abu College of Cs It Jazan University Department of Computer Science Jazan Saudi Arabia College of Cs It Jazan University Department of Computer & Network Engineering Jazan Saudi Arabia College of Cs It Jazan University Department of Information Technology and Security Jazan Saudi Arabia

ISBN: (纸本)9798350343144

Wireless sensor devices, edge computing, and fog computing can transform education by improving efficiency and empowering institutions. This research paper explores the application of these technologies in educational settings and investigates their benefits, challenges, and potential solutions. Educational institutions can track classroom occupancy, ambient conditions, and student participation using wireless sensor systems. loT edge computing allows data processing and analysis at the network edge for rapid insights and decision-making. Fog computing integrates cloud resources with edge computing to support complicated computations and data storage. Wireless sensor devices, loT edge computing, and fog computing are used to optimize educational processes like resource management, student tracking, personalized learning, and campus security. Data privacy, security, scalability, and interoperability issues are studied and solutions suggested. This research shows how wireless sensor devices, loT edge computing, and fog computing may alter educational systems, helping institutions improve efficiency and empower stakeholders. © 2023 IEEE.

关键词： Fog computing

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Advancing Patient Care for Autonomous Saline Level Alert System with Cloud-Based Isolation Forest Algorithm 5

Advancing Patient Care for Autonomous Saline Level Alert Sys...

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

作者： Nandhini, R. Sumathi, G. Ramya, S. Thulasi, Saravanan Ishwarya, M.V. Murugan, S. Rajalakshmi Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Department of Information Technology Tamil Nadu Trichy India Paavai Engineering College Department of Cyber Security Tamil Nadu Namakkal India John Deere India Pvt Ltd Customer and Product Support Services Maharashtra Pune India Agni College of Technology Artificial Intelligence and Data Science Department Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350379945

This work presents a novel method for improving patient care by creating a self-sufficient system to detect saline levels using an Isolation Forest Algorithm (IFA) hosted on the cloud. Standard practices for checking saline levels in IV infusion systems often include human intervention, which might cause important discrepancies to go undetected for longer periods of time. It presents a system that uses machine learning to identify aberrant saline levels in real-time, improving patient safety and relieving healthcare providers of some of their workload. The IFA, a powerful anomaly detection tool that works particularly well with high-dimensional data like that produced by intravenous infusion devices, is at the heart of our strategy. It gains scalability, flexibility, and real-time processing capabilities crucial for early alerting in clinical settings by implementing our algorithm on the cloud. Extensive testing and validation with both synthetic and real-world datasets prove that our suggested approach reliably and effectively detects abnormal saline levels with few false alarms. By providing healthcare practitioners with prompt alerts of possible infusion anomalies and allowing proactive action to avert adverse events, the autonomous saline level alarm system has major implications for patient care. The technology provides smooth interaction with current healthcare infrastructure by moving computing duties to the cloud, which eliminates the need for extra hardware and maintenance burdens. © 2024 IEEE.

关键词： Digital storage

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AQROM:A quality of service aware routing optimization mechanism based on asynchronous advantage actor-critic in software-defined networks

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Digital Communications and Networks 2024年第5期10卷 1405-1414页

作者： Wei Zhou Xing Jiang Qingsong Luo Bingli Guo Xiang Sun Fengyuan Sun Lingyu Meng School of Information and Communication Guilin University of Electronic TechnologyGuilin541004China The 34th Research Institute of China Electronics Technology Group Corporation Guilin541004China Guangxi Key Laboratory of Optical Network and Optical Information Security Guilin541004China State Key Laboratory of Information Photonics and Optical Communications BeijingUniversity of Posts and TelecommunicationsBeijing100876China Department of Electrical and Computer Engineering University of New MexicoAlbuquerqueNM87131USA

In Software-Defined Networks(SDNs),determining how to efficiently achieve Quality of Service(QoS)-aware routing is challenging but critical for significantly improving the performance of a network,where the metrics of QoS can be defined as,for example,average latency,packet loss ratio,and *** SDN controller can use network statistics and a Deep Reinforcement Learning(DRL)method to resolve this *** this paper,we formulate dynamic routing in an SDN as a Markov decision process and propose a DRL algorithm called the Asynchronous Advantage Actor-Critic QoS-aware Routing Optimization Mechanism(AQROM)to determine routing strategies that balance the traffic loads in the *** can improve the QoS of the network and reduce the training time via dynamic routing strategy updates;that is,the reward function can be dynamically and promptly altered based on the optimization objective regardless of the network topology and traffic *** can be considered as one-step optimization and a black-box routing mechanism in high-dimensional input and output sets for both discrete and continuous states,and actions with respect to the operations in the *** simulations were conducted using OMNeT++and the results demonstrated that AQROM 1)achieved much faster and stable convergence than the Deep Deterministic Policy Gradient(DDPG)and Advantage Actor-Critic(A2C),2)incurred a lower packet loss ratio and latency than Open Shortest Path First(OSPF),DDPG,and A2C,and 3)resulted in higher and more stable throughput than OSPF,DDPG,and A2C.

关键词： Software-defined networks Asynchronous advantage actor-critic QoS-aware routing optimization mechanism

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Image Classification Using Optimized Convolution Neural Network

Image Classification Using Optimized Convolution Neural Netw...

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engineering and technology (PICET), Parul International Conference on

作者： Ramesh Chegoni V. SriLakshmi R. Sai Venkata Ramana Veeramreddy Rajasekhar Ravindra Changala Venkata Nagaraju Thatha Department of Information Technology CMR Technical Campus Hyderabad India Department of CSE(AI & ML) B V Raju Institute of Technology Narsapur India Computer Science & Engineering( Cyber Security) Department Madanapalle Institute of Technology & Science Department of Computer Science and Engineering Malla Reddy Engineering College (Autonomous) Hyderabad India Department of Computer Science and Engineering Guru Nanak Institutions Technical Campus Hyderabad India Department of Information Technology MLR Institute of Technology Hyderabad India

ISBN: (数字)9798350369748

ISBN: (纸本)9798350369755

Conventional artificial neural networks and machine learning algorithms encountered difficulties in fulfilling the processing needs for extracting features from large-scale images and training models. Furthermore, they showed poor efficiency and accuracy in classifying images. In order to lay a firm groundwork and offer aid for the categorization and recognition of large datasets, this study introduces a deep learning model for classification of images. The feature extraction technique of CNNs is refined by mitigating noise and adjusting model parameters, resulting in the development of an advanced CNN architecture for deep learning in image classification. The efficacy of the proposed model is assessed through tests that scrutinize the correlation between iteration numbers and accuracy of various network models are commonly employed in image classification. Notably, the research model surpasses its counterparts in terms of classification accuracy, indicating its superiority in the realm of image classification. The study contributes valuable insights by enhancing the understanding of CNNs and offering a more effective model for image categorization, supported by rigorous evaluations

关键词： Deep learning Training Accuracy Machine learning algorithms Image recognition Correlation Noise Feature extraction Image categorization Image classification

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