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Optimizing Job Search: A Multi-Model Approach with DRNN-BES, Collaborative Filtering, and LLM-NER Resume Analysis

Optimizing Job Search: A Multi-Model Approach with DRNN-BES,...

引用

Blockchain and distributed systems security (ICBDS), IEEE international conference on

作者： Naveenkumar Jayakumar Netra Patil Roshankumar S. Pokal Rudra Pratap Dev Sidharth Nair School of Computer Science and Engineering Vellore Institute of Technology Vellore Dept. of Computer Engineering College of Engineering Bharati Vidyapeeth (Deemed to be University) Pune

ISBN: (数字)9798350354348

ISBN: (纸本)9798350354355

This research introduces an innovative job recommendation platform designed to optimize the job search process through advanced technology integration. The platform features a sophisticated Resume Analyzer leveraging NLP and ML to meticulously extract candidate information. Coupled with a Recommendation System that employs mixture models such as DRNN-BES and collaborative filtering, it delivers personalized job suggestions tailored to individual profiles. Additionally, the system utilizes state-of-the-art web scraping technologies to continuously update its database with diverse and current job listings from various online sources, ensuring a wide range of opportunities are accessible. This integration of technologies not only refines job-matching precision but also significantly improves the efficiency of connecting job seekers with appropriate opportunities. Initial experimental results underscore the enhanced effectiveness and accuracy of our recommendation system, particularly when incorporating user behavioral data into the collaborative filtering algorithm. This paper demonstrates the transformative potential of integrating AI into job seeking, offering a scalable, unbiased, and time-efficient approach to modern employment challenges.

关键词： Analytical models Accuracy Recurrent neural networks Collaborative filtering Heuristic algorithms Resumes Feature extraction security Recommender systems Text processing

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Evaluating the security and Privacy of Blockchain Integration in Domain Name systems

Evaluating the Security and Privacy of Blockchain Integratio...

引用

IEEE international conference on computer and Communication Engineering Technology (CCET)

作者： Mary Jane C. Samonte Raphael M. Abaleta Maverick D. Cayabyab Ladner Gilion D. Guerrero School of Information Technology Mapúa University Makati City Philippines

ISBN: (数字)9798350355673

ISBN: (纸本)9798350355680

Domain Name systems (DNS) are crucial components of any modern network, as they allow users to type in a domain name as opposed to complete IP addresses to visit web pages on the internet. The DNS resolution process typically involves a recursive server that fetches addresses from the root, top-level domain, and authoritative nameservers and sends the address to the client. Since this process can be slow, a caching system is implemented to save and access previous queries. However, this approach can result in security and privacy vulnerabilities. Blockchain is an emerging distributed ledger technology whose decentralized nature and data validation mechanisms help ensure data integrity, reliability, and authenticity. This study investigates the feasibility of integrating blockchain into DNS. It examines and reviews how DNS security is typically implemented, how blockchain can be implemented in other systems, like Internet of Things (IoT) systems, as a security measure, and how other technologies, like honeypots and artificial intelligence (AI), can be implemented to enhance system security in different systems, and potentially in DNS. The discussion results highlight blockchain's effectiveness in securing and improving the privacy of DNSs and their users. The study recommends that more studies be conducted on integrating blockchain in existing system architectures, like DNS, and on using other technologies, such as honeypots and AI, in conjunction with blockchain to improve security in computer networks and systems further.

关键词： Privacy Reviews Web pages systems architecture Blockchains Domain Name System security Internet of Things Servers Artificial intelligence

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Orphanage Channelization System Using Blockchain Technology

Orphanage Channelization System Using Blockchain Technology

引用

international conference on Recent Developments in Cyber security, ReDCySec 2023

作者： Choudhary, Tapasya Aggarwal, Vanshika Srivastava, Shivansh Trivedi, Shivani Department of Computer Science and Engineering ABESEC Ghaziabad India

ISBN: (纸本)9789819998104

Due to substance abuse or mental illness in the biological home, or the parent may simply be unwilling to care for the child. For such scenarios, various orphanages are running which is a shelter home or group home, built for the purpose of care of orphans and children who, for various reasons, cannot be cared for by their biological families but due to the current system used in such institutions there may take place some kind of malpractices (such as false records, incompetence). Thus, this paper discusses an application to solve the mentioned issues using blockchain technology. Blockchain is a decentralised distributed ledger, which consists of blocks that have their own hash value produced by an algorithm known as SHA-512 or SHA-256 which does not depend on third parties. This will ease information attainment and security by making the system more transparent and immutable. In this paper we used solidity programming language for creating the smart contract on the platform named remix which made available the necessary features in accomplishing the aims of the system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Blockchain

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Comprehensive Study on Authentication Methods in Wireless networks 7th

Comprehensive Study on Authentication Methods in Wireless Ne...

引用

7th international conference on Soft Computing and Signal Processing, ICSCSP 2024

作者： Kumar, Gotte Ranjith Babu, K. Suresh Research Scholar Department of Computer Science and Engineering Jawaharlal Nehru Technological University Kukatpally Telangana Hyderabad500085 India Assistant Professor School of CS&AI SR University Telangana Warangal506371 India Professor Department of Computer Science and Engineering Jawaharlal Nehru Technological University Kukatpally Telangana Hyderabad500085 India

ISBN: (纸本)9789819799251

Mobile devices, empowered by advanced wireless networks, have revolutionized information sharing and productivity across industries. However, this ubiquitous connectivity exposes user privacy and security to risks. The core vulnerability lies in the lack of authentication between mobile devices and DHCP (Dynamic Host Configuration Protocol) servers, which assign network configurations. This research explored existing authentication mechanisms in wireless networks to address these DHCP-related security concerns, highlighting the essence of secure data communication in mobile environments. The majority of the authentication mechanisms rely on centralized environments. Some rely on a cryptographic security key, which needs to be exchanged during authentication. Blockchain-based authentication is proposed. This technology is distributed, scalable, and robust. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Mobile security

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Face Mask Detection and Recognition System 1

Face Mask Detection and Recognition System

引用

1st IEEE international conference on Blockchain and distributed systems security, ICBDS 2022

作者： Gaiki, Sonali Deshmukh, Hrucha Gore, Sanjana Maddipati, Ramya Kamble, Shailesh Yeshwantrao Chavan College of Engineering Department of Computer Technology Nagpur India Indira Gandhi Delhi Technical University for Women Department of Artificial Intelligence and Data Science Delhi India

ISBN: (数字)9781665428323

ISBN: (纸本)9781665428323

As today a disease called COVID-19 is causing health crisis and deaths, it became most essential to wear a mask for protecting ourselves from Corona virus. Even in public areas, where is more rush we should wear mask as no virus can spread from person to person if any one of person from public is Corona positive. This paper introduces face mask detection that can be used by the authorities to make mitigation, evaluation, prevention, and action planning against COVID19. So basically in this project we are going to use Python, Keras, OpenCV alongwith MobileNet for this Face Mask Detection System. This includes some steps like data preprocessing, training and testing the model, run and view the accuracy and applying model in the camera. The inputs has provided here are 1000+ images of people with mask and without mask. First the data get processed and then by checking features of each image it will train all the models and the persons with and without mask get separated to two categories: with mask and without mask. If person is wearing mask with 90 or more percent of accuracy, then he will get added to with mask category and person not wearing mask get added to without mask category, so that we can permit with mask person to public areas. © 2022 IEEE.

关键词： Convolutional neural networks

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Enhancing DDoS Attack Detection in SDN with a Stacked Model Framework Utilizing Deep Neural networks

Enhancing DDoS Attack Detection in SDN with a Stacked Model ...

引用

IEEE international conference on Consumer Electronics (ICCE)

作者： Aishita Sharma Sunil K. Singh Sudhakar Kumar Shin Hung Pan Brij B. Gupta Kwok Tai Chui Department of CSE Chandigarh College of Engineering and Technology Chandigarh India Department of Information Management Asia University Taiwan Department of Computer Science and Information Engineering Asia University Taiwan University of Economics and Human Sciences in Warsaw Poland Hong Kong Metropolitan University Hong Kong

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

Software Defined Networking enhances network management and security through centralized control, vital for the growth of consumer electronics. With the rise of smart devices and IoT systems, the demand for scalable network management has intensified. While SDN streamlines network operations and supports essential technologies, it remains vulnerable to DDoS attacks that can disrupt these services. However, SDN environments are vulnerable to distributed Denial of Service (DDoS) attacks, posing significant risks to operational integrity. This paper analyzes the performance of various machine learning models at the individual level, providing insights into their effectiveness in detecting DDoS attacks. Based on these findings, a hybrid model is introduced, utilizing stacking techniques with a Deep Neural Network (DNN) as the meta-learner. The hybrid approach achieves a remarkable accuracy of 99.09%. The results underscore the crucial role of machine learning, particularly deep learning methodologies, in safeguarding SDN networks against cyber threats. As reliance on complex networked systems in consumer electronics increases, enhancing the security of SDN environments becomes essential for maintaining seamless operations.

关键词： Deep learning Stacking Artificial neural networks Denial-of-service attack security computer crime Software defined networking Smart devices Consumer electronics Centralized control

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Real-Time Visualization and Detection of Malicious Network Flows in IoT Devices using a Scalable Stream Processing Pipeline

Real-Time Visualization and Detection of Malicious Network F...

引用

international conference on Parallel distributed and Grid Computing (PDGC)

作者： S. Saravanan B. Uma Maheswari Kanagasundaram K Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Nokia Networks Bengaluru India

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

The continuous adoption of Internet of Things (IoT) devices has established notable security risks. In recent times, IoT devices are often targeted by malicious actors as IoT devices are limited with processing and memory resources. The detection of malicious network flows in real-time is necessary to maintain the security of IoT networks. This paper addresses the challenge by presenting a scalable stream processing pipeline that identifies malicious and non-malicious flows from IoT devices in real-time. The proposed system utilizes NFStream to generate network flows from device packets. The generated network flows are then published to a Kafka topic and consumed by a Kafka consumer. A distributed random forest-based machine learning model from Apache Spark MLlib is trained to classify these flows. The classification results are stored alongside the flows in a MongoDB database. Real-time visualization of the count of malicious and non-malicious flows for each IoT device is provided via Streamlit. Experimental results demonstrate the system's capability to accurately classify network traffic in real time while maintaining scalability for large-scale IoT environments. The proposed pipeline offers a robust solution for real-time malicious flow detection in large-scale IoT networks.

关键词： Accuracy Pipelines distributed databases Data visualization Cluster computing Telecommunication traffic Real-time systems Internet of Things Object recognition Monitoring

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Ensuring security Continuum from Edge to Cloud : Adaptive security for IoT-based Critical Infrastructures using FL at the Edge

Ensuring Security Continuum from Edge to Cloud : Adaptive Se...

引用

IEEE international conference on Big Data

作者： Ismail Ari Kerem Balkan Sandeep Pirbhulal Habtamu Abie Computer Engineering Department Ozyegin University Istanbul Turkey Norwegian Computing Center Oslo Norway

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Securing IoT-based Critical Infrastructures (CI) necessitates a cross-domain management of IT/OT systems encompassing hardware, software, data, and models along with their application scenarios. There is no feasible way to manually secure such heterogeneous, weakly-protected and physically distributed systems. In this work, we propose an adaptive security framework that uses Federated Learning (FL) for IoT data monitoring and analysis at the edge. We use Deep Neural Network (DNN) model selection and switching inside FL to address performance and security problems. We began prototyping the Edge-FL system using single board computers (a Raspberry Pi-5 cluster). We measure and compare the impacts of hardware heterogeneity. The framework aims to provide continuum of security and intelligence from IoT device to Edge to Cloud.

关键词： Performance evaluation Training Adaptation models Cloud computing Adaptive systems Artificial neural networks Switches Hardware security Internet of Things

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On-Device AI security Solutions: A Study on Lightweight Ensemble Learning Technique

On-Device AI Security Solutions: A Study on Lightweight Ense...

引用

international Communication systems and networks and Workshops, COMSNETS

作者： Huiyao Dong Igor Kotenko Faculty of Information Technology and Security ITMO University Saint Petersburg Russia Laboratory of Computer Security Problems St. Petersburg Federal Research Center of the RAS Saint Petersburg Russia

ISBN: (数字)9798331531195

ISBN: (纸本)9798331531201

IoT security poses a significant challenge due to the presence of extensive confidential data and vulnerable devices. Artificial intelligence (AI) is increasingly viewed as pivotal in cybersecurity, enhancing the efficacy of security measures including login mechanisms with fingerprinting, real-time phishing detection, and multi-stage attack identification. However, centralized AI models face issues such as high variance and over-fitting, as well as resource inefficiencies. This study explores the deployment of on-device AI security models in a distributed manner with lightweight yet effective ensemble deep learning techniques. The proposed methods involve lightweight ensemble techniques such as learnable weighted average voting (WAVGVoting) and concatenation-based stacking, integrated with three base learners with distinct structures. The experiments on Edge-IIoT, utilizing 5-fold cross-validation, show that our approaches are effective even in dynamic data and diverse settings. Specifically, the stacking model achieved an average accuracy of 99.8441% with a recall of 99.7762%, while WAVGVoting showed an average precision of 99.9116% and a false alarm rate of 0.0883%, certainly indicating its applicability.

关键词： Accuracy Phishing Stacking Intrusion detection Fingerprint recognition Real-time systems Internet of Things Ensemble learning Artificial intelligence Faces

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Enhanced Facial Emotion Recognition using Deep Learning Techniques: A Comprehensive Analysis and Implementation

Enhanced Facial Emotion Recognition using Deep Learning Tech...

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distributed systems, computer networks and Cybersecurity (ICDSCNC), international conference on

作者： Sunil S Harakannanavar Somesh S G Sughosh V Sumeet P Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bangalore India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

In recent years, deep learning approaches for facial expression recognition have gained significant momentum across various domains, including healthcare, security, and entertainment. These methods demonstrate a high degree of accuracy in identifying human emotions by analyzing subtle facial cues, distinguishing between expressions such as happiness, sadness, and anger. Convolutional neural networks (CNNs) have been particularly successful in this area, leveraging their ability to automatically extract and classify facial features for accurate emotion detection. Recent innovations have improved image preprocessing to enhance quality and reduce noise, while advanced feature extraction techniques and robust classification algorithms have further refined the recognition process. Future research will likely focus on improving model generalization across diverse datasets and exploring multimodal techniques that integrate facial cues with other behavioral signals. A streamlined three-layer CNN model has been developed, achieving a promising 75% accuracy in emotion classification, suggesting its potential for real-world applications that require a nuanced understanding of emotional expressions.

关键词： Deep learning Emotion recognition Technological innovation Image recognition Accuracy Face recognition Noise Streaming media Feature extraction Convolutional neural networks

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