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检索条件"丛书名=Lecture Notes in Networks and Systems"

共 9236 条记录，以下是151-160 订阅

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Machine Learning-Envisioned Malware Attack Detection Mechanism for Edge Computing-Based networks 3rd

Machine Learning-Envisioned Malware Attack Detection Mechani...

引用

3rd International Conference on Network Security and Blockchain Technology

作者： Bhutani, Pearl Thapliyal, Siddhant Wazid, Mohammad Giri, Debasis Mishra, Amit Kumar Singh, Devesh Pratap Graph Era Deemed Univ Dept Comp Sci & Engn Dehra Dun 248002 Uttarakhand India Maulana Abul Kalam Azad Univ Technol Dept Informat Technol Nadia 741249 W Bengal India

ISBN: (纸本)9789819780501;9789819780518

The significance of cybersecurity resides in its ability to safeguard individuals and enterprises against cyberattacks, as well as the unauthorized acquisition or misplacement of confidential and classified data. Cybersecurity includes the ability to employ monitoring systems to safeguard personal data, financial information, trade secrets, intellectual property, and any sensitive government information. By establishing robust cybersecurity protocols, we can enhance our ability to safeguard against a wide range of online threats. In the present age, nearly every industry is utilizing the services of diverse machine learning and deep learning models to forecast, analyze, or produce outcomes with greater optimization and efficiency. Thus, we have employed the capacity of machine learning in the task of identifying and classifying malicious software. In this paper, we propose a machine learning-envisioned malware attack detection mechanism for edge computing-based networks. The practical implementation of the proposed technique is also provided, along with the performance comparison of several existing methods for the assigned intrusion detection task. The proposed scheme achieved the highest accuracy value as compared to the other existing methods.

关键词： Malware Machine learning Security Edge computing Cybersecurity Threats

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Why a Bot is Undetectable? An Explainability-Based Study of Misclassified Automated Accounts in Social networks 20th

Why a Bot is Undetectable? An Explainability-Based Study of ...

引用

20th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based systems

作者： Lopez-Joya, Salvador Diaz-Garcia, Jose Angel Ruiz, Maria Dolores Martin-Bautista, Maria Jose Res Ctr Informat & Communicat Technol CITIC UGR Granada 18014 Spain Univ Granada Dept Comp Sci & Artificial Intelligence Granada 18014 Spain

ISBN: (纸本)9783031739965;9783031739972

Detecting bots on social media platforms is a major challenge, as these automated entities are constantly evolving to evade detection. In this study, we investigate the main features that contribute to the difficulty of bot detection. Leveraging the TwiBot-20 dataset, we analyze the characteristics of misclassified accounts and explore the reasons behind their erroneous classification. Our approach combines feature engineering, Machine Learning with Random Forest, and the interpretation of model predictions using SHAP (SHapley Additive exPlanations) values. We employ clustering techniques to identify patterns in feature contributions and provide insights into the complexities of distinguishing between human and automated accounts. Our findings highlight the nature of bot detection and the need for advanced methods to address the problem of social media manipulation.

关键词： Bot detection Social media Machine learning Explainability SHAP values Clustering Data mining

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Secure Sharing of CCTV Feed 3rd

Secure Sharing of CCTV Feed

引用

3rd International Conference on Network Security and Blockchain Technology

作者： Guntuboina, Venkata Sankirtana Balamurugan, Krithikha Gurrala, Mahitha Rengan, Nandhini K. S. Patel, Sankita J. Sardar Vallabhbhai Natl Inst Technol Dept Comp Sci & Engn Surat 395007 Gujarat India

ISBN: (纸本)9789819780501;9789819780518

Thanks to the rapid development of information technologies, the number of CCTV cameras has increased, which has decreased crime and violence rates. Regardless of its benefits, there are some drawbacks, such as the lack of privacy in preserving the identity of people when video data is shared. Information collected and shared by analyzing real-time video data gathered from CCTV systems is vulnerable to privacy violations, necessitating secure countermeasures. This paper attempts to compare, contrast, and propose efficient methods for detecting and encrypting faces in image frames that are obtained from video. With the advancements in technology, security measures must also be improved to curb any violations of confidentiality. At times, the encryption data contains information that some parties, such as criminal investigators or administration, are interested in;it can be advantageous to recover these concealed facts. Hence, we also seek to gather relevant decryption techniques that would enable us to achieve the same without much loss of information.

关键词： Face detection Encryption Decryption Security CCTV feed

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A Gamified Multi Agent Robot System for Physical Stimulation 21st

A Gamified Multi Agent Robot System for Physical Stimulation

引用

21st International Symposium on Distributed Computing and Artificial Intelligence

作者： Garcia-Martinez, Jesus Carrasco-Martinez, Sara Alvarez-Arias, Sofia Arrojo, Guillermo A. Castillo, Jose C. Alonso-Martin, Fernando Castro-Gonzalez, Alvaro Carlos III Univ Madrid Dept Syst Engn & Automat Calle Butarque 15 Leganes 29811 Madrid Spain

ISBN: (纸本)9783031820724;9783031820731

Social robots have the potential to help people with mobility and coordination problems, especially in elderly people, in a fun and enjoyable way, as they can support physical stimulation exercises by enabling the gamification of games for this purpose. In this paper, we present a version of the computer game Pong as a serious game implemented as a multi-agent system, in which the social robot Mini guides the activity to promote the physical activity of the user. We track the user's hands to replace the classic paddle game controller, facilitating the assessment of physical stimulation and hand-eye coordination. To improve the game's engagement, the user can play against the robot, against another player, or against themselves. Incorporating gamification elements improves sustained user participation and skill development. The proposed system has been integrated into the robot's software architecture, and preliminary tests show potential for promoting physical activity among seniors.

关键词： Social Robots Serious Games Multi-Agent System Gamification Physical Stimulation

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Android Malware Detection: Leveraging Deep Learning with Process Control Block Information 21st

Android Malware Detection: Leveraging Deep Learning with Pro...

引用

21st International Symposium on Distributed Computing and Artificial Intelligence

作者： Alawneh, Heba Alkofahi, Hamza Umphress, David Jordan Univ Sci & Technol Irbid Jordan Auburn Univ Auburn AL 36849 USA

ISBN: (纸本)9783031820724;9783031820731

The security and privacy of Android device users are seriously threatened by the sophistication and endurance of malware. The risks associated with malware assaults on Android are larger than ever since mobile devices are being utilized for sensitive transactions and data storage more and more. This paper proposes a dynamic malware detection system that mines data from Process Control Blocks (PCBs) during process execution to identify malicious activity using deep learning techniques. Our method provides robust detection of various threats by examining processes at this fundamental level. It precisely monitors changes in PCB parameters for all application threads functioning at the kernel level, in contrast to user-level malware detection. Our PCB mining approach outperforms existing methods in accuracy and scalability of captured PCB records. Our testing findings revealed a high success rate, capturing over 99 percent of context transitions. Utilizing a manageable sequence size of 12 PCBs, our deep learning detection model successfully identified malicious activity at various points in the process execution, achieving an accuracy of 95.6%.

关键词： Dynamic Malware Detection Deep Learning Process Control Block mining kernel-level monitoring

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Preliminary Study on Assessing the Proportion of Individuals with Meaningful Connectivity in Rural Areas of Malaysia 9th

Preliminary Study on Assessing the Proportion of Individuals...

引用

9th International Congress on Information and Communication Technology (ICICT)

作者： Yih, Cheng Haw Zaman, Tariq Univ Technol Sarawak ASSET Sarawak 96000 Malaysia

ISBN: (纸本)9789819750344;9789819750351

Supported by the USP fund, Pusat Ekonomi Digital Keluarga Malaysia (PEDi), was commenced by Malaysian Communications and Multimedia Commission (MCMC), to support local entrepreneurs and businesses in adopting digital technologies but also enhance Internet access and digital literacy among local communities. This study focuses on meaningful connectivity, a concept introduced by the Alliance for Affordable Internet (A4AI), within PEDi. It emphasises four key dimensions that define meaningful Internet access. Employing a methodology derived from A4AI's guidelines, the research aimed to assess the meaningful Internet access component of the users in PEDis. Out of 91 respondents, only 51.19% reported experiencing meaningful connectivity based on A4AI framework.

关键词： Meaningful connectivity Digital divide Rural connectivity Universal service provision PEDis

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Enhancing Healthcare IoT Security: A CCN-Based Approach for FIB Establishment and Content Delivery 3rd

Enhancing Healthcare IoT Security: A CCN-Based Approach for ...

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3rd International Conference on Network Security and Blockchain Technology

作者： Sarkar, Atanu Ray, Sangram Natl Inst Technol Sikkim Dept Comp Sci & Engn Ravangla 737139 Sikkim India

ISBN: (纸本)9789819780501;9789819780518

The rapid growth of the Internet of Things (IoT) has positioned it as a critical resource for remote healthcare monitoring, emphasizing the need for secure and efficient retrieval of medical data and driven by advancements in technology. Nevertheless, the limited capacity of IoT devices renders it difficult to acquire medical data securely and effectively. In an edge cloud environment, an edge device provides an enormous amount of resources, while Content-Centric Networking (CCN) can enhance the performance of data retrieval. In order to perform IoT edge-based medical data retrieval, we are motivated to integrate IoT with the Edge cloud and take advantage of CCN. This paper proposes a secure remote healthcare management system integrated with CCN-based IoT with edge devices. The proposed security architecture employs hash name prefixes instead of plain text and signatures to provide secure medical data delivery and takes advantage of CCN's benefits to increase the efficiency of medical data retrieval.

关键词： Edge device Hash name prefix Content centric network Internet of things

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Nonlinearity in AES Subkey Scheduling-An Augmented Security 3rd

Nonlinearity in AES Subkey Scheduling-An Augmented Security

引用

3rd International Conference on Network Security and Blockchain Technology

作者： Howlader, Jaydeep Natl Inst Technol Durgapur Dept Comp Sci ad Engn Durgapur West Bengal India

ISBN: (纸本)9789819780501;9789819780518

Advanced Encryption Standard (AES) is a widely used symmetric encryption mechanism. The recent development of Composite Field Arithmetic (CFA) has significantly optimized the AES architecture. Moreover, subpipeline structures are often used to speed up the execution of AES. In this paper, we propose a nonlinear subkey scheduling for the rounds of AES. The objective is to introduce a different ordering of subkeys without incurring additional latency. The proposed mechanism will increase the complexity of some known chosen text/key attacks.

关键词： Composite field arithmetic Optimized AES AES cryptanalysis Meet-in-middle attack Related key cryptanalysis

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Improvement of Anomaly Detection Through Enhanced Feature Extraction in TimesNet 21st

Improvement of Anomaly Detection Through Enhanced Feature Ex...

引用

21st International Symposium on Distributed Computing and Artificial Intelligence

作者： Nagahama, Hiroyoshi Yoshioka, Michifumi Inoue, Katsufumi Todorokihara, Masayoshi Omori, Keishi Osaka Metropolitan Univ Grad Sch Informat Osaka Japan Seiko Epson Corp Microdevices Operat Div Suwa Japan

ISBN: (纸本)9783031820724;9783031820731

Accurate detection and classification of machine anomalies are essential tasks for performing machine maintenance. Recently, deep learning methods have been widely utilized for anomaly detection to improve performance. For this study, we utilized TimesNet as a feature extractor. TimesNet had achieved state-of-the-art performance in time series analysis tasks, including anomaly detection, in 2023. TimesNet enables efficient feature extraction by segmenting and stacking the vibrations at critical periods during the preprocessing. However, we found that TimesNet lacks exactness in the period length used for the segmentation. Thus, we additionally proposed two methods to address this issue and compared the accuracy of our proposed models to that of TimesNet. As a result, the proposed methods outperformed the original TimesNet.

关键词： Anomaly Detection Deep Learning TimesNet

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Predicting the Political Affiliation of Moroccan Parties Through Analysis of Electoral Programs 9th

Predicting the Political Affiliation of Moroccan Parties Thr...

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9th International Congress on Information and Communication Technology (ICICT)

作者： Karra, Rachid Lasfar, Abdelali Mohammed V Univ Rabat Mohammadia Sch Engineers LASTIMI Lab Rabat Morocco

ISBN: (纸本)9789819754403;9789819754410

Our study originates from the lack of scientific analysis in political life. Recent advancements in Natural Language Processing enable a comprehensive examination of political discourse and party programs. The initial step in a voter's decision-making process is distinguishing the political orientation of parties, emphasizing the need for clear distinctions in the programs presented by these political entities. We conducted an analysis of the political programs of the six primary political parties in Morocco, situated in North Africa. Our objective was to determine whether their political proposals could be effectively classified within the progressive-conservative framework. Two distinct methods were employed for this assessment. The first method involved calculating the frequency of terms and their cosine similarity. The properties of word embeddings facilitate the representation of similar words, thereby allowing for the grouping of related topics. The second method utilized prompt engineering with the Language Model "LLaMA". The findings indicate that Moroccan political parties can be effectively classified into the progressive-conservative framework based on an analysis of their respective programs.

关键词： Classification LLM NLP Political party Term frequency

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