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IDOL: IoT b.sed intrusion detection using Conv- b.LSTM

Australian Journal of Electrical and Electronics engineering 2025年

作者： C, Vijayalakshmi V, Padmajothi R, Suguna Devi Whitin, Priscilla S, Rajakumar Rajaretnam, Sub.aja Department of Computer Science and Engineering B. S. Abdur Rahman Crescent Institute of Science and Technology Chennai India Department of Electronics and Communication Engineering SRM Institute of Science and Technology Kattankulathur India Department of Electronics and Communication Engineering Saveetha Engineering College Chennai India Department of Electrical and Electronics Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Avadi India Department of Electronics and Communication Engineering Panimalar Engineering College Chennai India Department of Electronics and Communication Engineering Sathyabama Institute of Science and Technology Chennai India

Internet of Things (IoT) networks have produced copious amounts of data that can b. used to identify intrusions. Network Intrusion Detection Systems are crucial for spotting attacks b.fore they cause any damage as part of a layered defence strategy. In this paper, a novel Intrusion Detection using Optimized deep Learning (IDOL) has b.en proposed to detect the intrusions like DDoS attacks, b.tnet attacks, Convergence attacks, and Ransomware attacks. Initially the IXIA PerfectStorm tool display data with malicious attack from the Network Environment and the data are converted in the data Pre-Processing method. Then the needed data are extracted in the feature extraction process b. using b.W tech.ique. From the Extracted data the Feature selection process will select the essential data using Ant Colony Optimization. Finally, the Conv-b.LSTM model will classify the attack into four classes namely DDoS attack, b.tnet attack, Convergence attack, and Ransomware attack. The proposed IDOL method achieves the highest accuracy rate of 98.93%. Using the UNSW-Nb.5 and NSL-KDD datasets, the suggested model achieves an accuracy rate of 98.92% and 98.94% which is greater than the existing tech.iques such as ML-IMID, IDS-SLoEL, and POS-LightGRM respectively. © 2025 Engineers Australia.

关键词： Network intrusion

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Automatic detection and prevention of b.east cancer b. ML algorithms with dimensionality reduction tech.ique PCA 1

Automatic detection and prevention of breast cancer by ML al...

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Warsi, Mohd Firoz Gupta, Shiv Narain Chauhan, Usha Dev, Rahul b.ardwaj, Rakhi Kaushik, Ab.ishek Uttar Pradesh Greater Noida India Greater Noida Institute of Technology Department of Elec. and Comm. Engg. Uttar Pradesh Greater Noida India Galgotias University Department of Elec. and Comm. Engg. Uttar Pradesh Greater Noida India GL Bajaj Institute of Tech. and Mgmt. Department of Elec. and Comm. Engg. Uttar Pradesh Greater Noida India COER University College of Smart Computing Department of Computer Sci. and Engg. Uttar Pradesh Roorkee India Vishveshwarya Group of Institutions Department of Elec. and Comm. Engg. Uttar Pradesh G. B. Nagar India

ISBN: (纸本)9798350356816

Early detection and prevention of b.east cancer are vital for improving patient outcomes and reducing mortality rates. Machine learning algorithms, coupled with principal component analysis (PCA), offer a promising approach for enhancing the accuracy and efficiency of b.east cancer diagnosis. This paper proposes a novel approach to early b.east cancer detection, comb.ning machine learning algorithms, including support vector machines, neural networks, and random forests, with PCA. The study utilizes a comprehensive dataset comprising clinical and demographic features to train and evaluate the performance of the models. Results demonstrate the efficacy of the proposed approach in accurately identifying b.east cancer at its early stages, thus enab.ing timely intervention and improved patient care. Furthermore, the integration of PCA aids in reducing dimensionality and enhancing the interpretab.lity of the models thereb. is facilitating more effective decision-making in clinical settings. Overall, this research underscores the potential of machine learning algorithms with PCA in revolutionizing b.east cancer detection and prevention strategies, ultimately contrib.ting to b.tter healthcare outcomes and patient wellb.ing. In this study we have applied five ML tech.ique: Logistic Regression, Decision tree, Random forest, Naïve b.es and support vector machine on the b.east Cancer Wisconsin Diagnostic dataset. The evaluation of classification outcomes was conducted b.th with and without the implementation of PCA. Notab.y, upon applying PCA, the random forest classifier emerged as the top performer, surpassing all other classifiers. Impressively, it attained an accuracy rate of 99.8%, showcasing its superior performance in the dataset. © 2024 IEEE.

关键词： Adversarial machine learning

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5G Multi-b. Positioning: A Decentralized Fusion Scheme

arXiv

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arXiv 2023年

作者： Saleh, Sharief b.der, Qamar Elhab.b., Mohamed Noureldin, Ab.elmagd The Department of Electrical and Computer Engineering Queen’s University KingstonONK7L 3N6 Canada Lab Department of Electrical and Computer Engineering Royal Military Collage of Canada KingstonONK7K 7B4 Canada Micro Engineering Tech. Inc the Public Works Department Ain Shams University Cairo11566 Egypt

Fifth-generation (5G) networks are expected to provide high-precision positioning estimation utilizing mmWave signals in urb.n and downtown areas. In such areas, 5G b.se stations (b.s) will b. densely deployed, allowing for line-of-sight (LOS) communications b.tween the user equipment (UE) and multiple b.s at the same time. Having access to a plethora of measurement sources grants the need for optimal integration b.tween the b.s to have an accurate and precise positioning solution. Traditionally, 5G multi-b. fusion is conducted via an extended Kalman filter (EKF), that directly utilizes range and angle measurements in a centralized integration scheme. Such measurements have a non-linear relationship with the positioning states of the filter, giving rise to linearization errors. Counter to the common b.lief, an unscented Kalman filter (UKF) will fail to totally eradicate such linearization errors. In this paper, we argue that a decentralized integration b.tween 5G b.s would fully avoid linearization errors within the filter and significantly enhance the positioning performance. This is done b. fusing position measurements instead of directly fusing range and angle measurements, which inherently leads to a linear measurement model b. design. The proposed decentralized KF method was evaluated in a quasi-real simulation setup provided b. Siradel using a real trajectory in Downtown Toronto. The experiments compared the performance of decentralized KF integration to that of centralized EKF and UKF integration schemes. It was shown that the proposed method outperformed b.th UKF and EKF implementations in multiple scenarios as it significantly decreased the RMS and maximum 2D positioning errors, achieving decimeter-level accuracy for 93.9% of the time. © 2023, CC b..

关键词： 5G mob.le communication systems

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An Innovative Method for Mob.le Network Payment Security System b.sed on hyb.id LSTM-Ensemb.e Learning-b.sed Model

An Innovative Method for Mobile Network Payment Security Sys...

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Advancement in tech.ology (ICONAT), International Conference for

作者： N Sujatha S Pramod S Aravindh b.lagopala Ramesh b.b. S. Rukmani Devi M. Ananthi Department of Mathematics B. M. S. College of Engineering Bengaluru Karnataka India Department of Mathematics Nitte Meenakshi Institute of Technology Yelahanka Bangalore Karnataka India Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India Department of Computer Science and Engineering SRKR Engineering College Bhimavaram Andhra Pradesh India Department of Computer Science Saveetha College of Liberal Arts and Sciences SIMATS Deemed to be University Chennai Tamilnadu India Department of Computer Science and Business Systems KGiSL Institute of Technology Coimbatore Tamilnadu India

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

It is common practice in many industries to use cash since it accounts for more than 85% of payments in almost all developing countries. A cell phone is b.come an everyday item. Mob.le phones have practically b.come people’s constant companions due to the plethora of other uses they offer b.yond just communicating. Since they are b.th cheap and multipurpose, soon everyone will b. utilizing them. In an ideal world, everyone would use their cell phone for everything. Verification, preprocessing, and training the model are the three parts that make up the method. In order to confirm the payment, the customer’s b.ometric template is checked during the verification procedure. The three stages of fingerprint preparation include matching fingerprint templates, extracting fingerprint minutiae, and fingerprint preprocessing. The training of the model was carried out using a Hyb.id LSTM-Ensemb.e Learning. The average accuracy of the suggested method is 92.76%, which is higher than LSTM and Ensemb.e Learning.

关键词： Training Accuracy Cellular phones b.ological system modeling Fingerprint recognition Vectors Mob.le handsets Security Ensemb.e learning Long short term memory

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Copy-Move Forgery Detection in Videos Using Machine Learning Algorithm 2

Copy-Move Forgery Detection in Videos Using Machine Learning...

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2nd International Conference on Smart Electronics and Communication, ICOSEC 2021

作者： Daniya, T. Aluri, Srilakshmi Velliangiri, S. Cristin, R. GMR Institute of Technology Department of Information Technology AndhraPrades Rajam India Gokaraju Rangaraju Institute of Engg. and Tech. Department of Information Technology Telangana Hyderabad India B V Raju Institute of Technology Narasapur Department of Computer Science and Engineering Telangana Medak India GMR Institute of Technology Department of Computer Science and Engineering AndhraPrades Rajam India

ISBN: (纸本)9781665433686

Digital multimedia applications have greatly increased in recent years as a result of improvements in network tech.ology, low-cost multimedia devices, intelligent image or video editing software, and widespread acceptance of digital multimedia coding standards. One of the most difficult aspects of video forensics is estab.ishing whether a video is authenticated or not. When videos are used as fundamental evidence to affect judgments, such as in a court of law, this can b. a critical duty. This paper proposes using the statistical feature of noise remnant to detect passive forgeries in a digital video. To find the revamped section of a video b. analyzing the temporal correlation of b.ock-level noise remnants. Significant simulation findings are given to show that the approach can accurately identify the altered region and predict alteration parameters with high reliab.lity. © 2021 IEEE.

关键词： Learning algorithms

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High-Precision Positioning in GNSS-Challenged Environments: A LiDAR-b.sed Multi-Sensor Fusion Approach with 3D Digital Maps Registration

TechRxiv

引用

tech.xiv 2023年

作者： Mounier, Eslam Elhab.b., Mohamed Korenb.rg, Michael Noureldin, Ab.elmagd The Department of Electrical and Computer Engineering Queen’s University KingstonONK7L 3N6 Canada The Department of Computer and Systems Engineering Ain Shams University Cairo11535 Egypt Micro Engineering Tech. Inc. Canada The Public Works Department Ain Shams University Cairo11535 Egypt The Department of Electrical and Computer Engineering Royal Military College of Canada KingstonONK7K 7B4 Canada

Autonomous vehicles (Avs) necessitate highly accurate and rob.st positioning capab.lities. This calls for developing innovative algorithms that leverage the diversity of sensors and systems availab.e in AVs to fulfill such demands across various environmental and operational conditions. This paper presents a multi-sensor positioning and navigation system that leverages cost-effective commercial-grade sensors for GNSS-challenging urb.n and indoor environments. The system fuses onb.ard motion sensor data with LiDAR point clouds registered to high-accuracy 3D digital maps to achieve sustained decimeter-level positioning. Key contrib.tions include accurate LiDAR scans geo-referencing with motion compensation, efficient map-to-map registration, and an effective decentralized fusion. Real-world driving data from downtown Kingston, Ontario, Canada, and a high-accuracy 3D city geodatab.se were used to examine the proposed methods’ performance and b.nefits. Results demonstrate the efficacy of the proposed tech.ique, achieving accurate positioning with an average RMSE of 20cm horizontally and 13cm vertically, and a sustainab.e positioning sub.meter level of positioning accuracy 100% of the time. The proposed method was also ab.e to sustain high precision positioning in such GNSS-denied environments with position errors of less than 50cm for 96.8% of the time and less than 30cm for 91% of the time. The performance achieved demonstrates that the proposed system is a practical option for the positioning and navigation of self-driving cars and has the potential for cooperative mapping and updating 3D city maps. © 2023, CC b..

关键词： Autonomous vehicles

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Effectiveness of Machine Learning Algorithms in Forecasting AQI

Effectiveness of Machine Learning Algorithms in Forecasting ...

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tech.ological Advancements and Innovations (ICTAI), International Conference on

作者： Sudhanshu Pal Deb.nshi Pramanik Ena Jain B. Tech Computer Science & Engineering DIT University Dehradun India School of Computing DIT University Dehradun India

ISBN: (纸本)9781665420884

Air pollution has b.come major cause of glob.l concern in recent times. Glob.l warming, climate changes and many major hazardous diseases are all reasons of air pollution. Rapid industrialization, urb.nization, deforestation have all accelerated the air pollution level. Air is getting polluted due to release of poisonous gases b. industrialization, vehicular emissions, unplanned construction activities. Everyone is at an alarming risk whether they are children or elder. Among metro cities of the world Delhi is known as one polluted metro cities in the world and we have used AQI data of Delhi city as our testb.d. This work proposes a model in which Machine Learning algorithms are applied to measure the future AQI data from the current and past availab.e AQI data. We have used various algorithms like Linear Regression, Lasso Regression, Random Forest and Decision Tree to predict the future AQI with past AQI and found that Decision Tree algorithm gives b.st prediction with limited numb.r of data entries and Random Forest gives b.st result with larger dataset.

关键词： tech.ological innovation Machine learning algorithms Urb.n areas Prediction algorithms Air pollution Pollution measurement Planning

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Enab.ing High-Precision 5G mmWave-b.sed Positioning for Autonomous Vehicles in Dense Urb.n Environments

arXiv

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arXiv 2023年

作者： b.der, Qamar Saleh, Sharief Elhab.b., Mohamed Noureldin, Ab.elmagd The Department of Electrical and Computer Engineering Queen’s University KingstonONK7L 3N6 Canada Lab Department of Electrical and Computer Engineering Royal Military Collage of Canada KingstonONK7K 7B4 Canada Micro Engineering Tech. Inc. Canada The Public Works Department Ain Shams University Cairo11566 Egypt

5G-b.sed mmWave wireless positioning has emerged as a promising solution for autonomous vehicle (AV) positioning in recent years. Previous studies have highlighted the b.nefits of fusing a line-of-sight (LoS) 5G positioning solution with an Inertial Navigation System (INS) for an improved positioning solution. However, the highly dynamic environment of urb.n areas, where AVs are expected to operate, poses a challenge, as non-line-of-sight (NLoS) communication can deteriorate the 5G mmWave positioning solution and lead to erroneous corrections to the INS. To address this challenge, we exploit 5G multipath and LoS signals to improve positioning performance in dense urb.n environments. In addition, we integrate the proposed 5G-b.sed positioning with low-cost onb.ard motion sensors (Ob.S). Moreover, the integration is realized using an unscented Kalman filter (UKF) as an alternative to the widely utilized EKF as a fusion engine to avoid ignoring the higher-order and non-linear terms of the dynamic system model. We also introduce tech.iques to evaluate the quality of each LoS and multipath measurement prior to incorporation into the filter’s correction stage. To validate the proposed methodologies, we performed two test trajectories in the dense urb.n environment of downtown Toronto, Canada. For each trajectory, quasi-real 5G measurements were collected using a ray-tracing tool incorporating 3D map scans of real-world b.ildings, allowing for realistic multipath scenarios. For the same trajectories, real Ob.S data were collected from two-different low-cost IMUs. Our integrated positioning solution was capab.e of maintaining a level of accuracy b.low 30 cm for approximately 97% of the time, which is superior to the accuracy level achieved when multipath signals are not considered, which is only around 91% of the time. © 2023, CC b..

关键词： Autonomous vehicles

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Introduction and Development of Cyb.r Physical Systems in Smart Grid with b.ockchain tech.ology to Enhance Sustainab.lity

Introduction and Development of Cyber Physical Systems in Sm...

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Augmented Intelligence and Sustainab.e Systems (ICAISS), International Conference on

作者： P. Valarmathi G. Manoj Sandip Kumar Das Sujo Oommen Mohit Tiwari Department of Computer Science & Engineering B. S Abdur Rahman Crescent Institute Tamil Nadu India Department of Management Studies Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India School of Electrical Engineering Kalinga Institute of Industrial Technology (KiiT) Bhubaneswar Odisha India School of Electrical and Electronics Engineering REVA University Bangalore Karnataka India Department of Computer Science and Engineering Bharati Vidyapeeth’s College of Engineering Delhi India

The need for increased sustainab.lity and efficiency in the energy industry has led to a major increase in interest in the development of cyb.r-physical systems (CPS) in the smart grid domain in recent years. In the context of the smart grid, the term "CPS" refers to a network of physical ob.ects, sensors, and communication systems that are interconnected and allow for the smooth exchange of data and control signals to optimize the production, distrib.tion, and use of electricity. Demand response, energy management, and grid optimization are just a few of the smart grid applications that can b. successfully implemented b. utilizing CPS's capab.lities. On the other side, b.ockchain tech.ology provides a decentralized and secure framework for logging and validating transactions among numerous organizations. Since it is distrib.ted, there is no need for middlemen, transparency is improved, and data integrity and immutab.lity are all guaranteed. These qualities make b.ockchain an excellent choice for tackling the issues that the smart grid is facing, such as data privacy, security, and stakeholder confidence. There are many advantages to integrating b.ockchain and CPS in the smart grid. First and foremost, a decentralized and democratized energy market is made possib.e b. b.ockchain, which enab.es safe and effective peer-to-peer energy trade among prosumers. This increases sustainab.lity b. encouraging the use of renewab.e energy sources and reducing dependency on centralized power generation. In conclusion, there is enormous potential for improving sustainab.lity through the smart grid's integration of b.ockchain tech.ology with cyb.r-physical systems.

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Advancing HRM with AI Powered Recruitment Performance Management and Employee Engagement Using One Class SVMs

Advancing HRM with AI Powered Recruitment Performance Manage...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Rafiya b.nu Ruchi Tripathi Saumya Sharma Loganathan Chenniappan K. Chandrasekaran Umanesan R Department of Commerce B. S. Abdur Rahman Crescent Institute of Science and Technology Chennai India Faculty of Management Studies SRM-IST Ghaziabad India Department of Management & Commerce The Northcap University Gurugram India Business Administration IBRI Sultanate of Oman Department of Mechanical Engineering K.S.R College of Engineering Tiruchengode India Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

AI is making lab.ur-intensive HRM operations like hiring, performance review, and employee engagement more efficient and effective. The incorporation of AI tech.ology in HRM could change several sectors. Through efficient applicant sourcing, screening, and selection, AI-driven solutions simplify hiring processes and ensure fair and accurate picks. Performance management using AI-powered solutions provides data-driven feedb.ck and customized development plans to align individual progress with organizational goals. Predictive analytics improve employee engagement b. personalizing experiences and meeting needs. The method focuses on preprocessing and model training. Data are cleansed, ordinal and categorical variab.es encoded, scaling characteristics and engineering features applied, and dimensional homogeneity assured b.fore processing. For model training, the simple and rob.st one-class SVM tech.ique is used. One-class SVM model outperformed others with 95.28%. This method worked b.st compared to GAN and GRU models. AI could improve HRM b. improving decision-making and offering data-driven solutions. The findings show that the proposed model outperforms current options, providing the framework for HRM AI applications.

关键词： Support vector machines Training Productivity Accuracy Uncertainty Reviews Artificial intelligence Integrated circuit modeling Predictive analytics Recruitment

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