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Advancing Phishing Detection with Random Forest: A Machine Learning Approach for Enhanced Cybersecurity 3

Advancing Phishing Detection with Random Forest: A Machine L...

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3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Chandra, Sucheta Das, Priyanka Mandal, Ankita Chakroborty, Sriparno Chowdhury, Ishika Ghosh, Kathamrita University of Engineering & Management Institute of Engineering & Management Dept. of Computer Application and Science Kolkata India

ISBN: (纸本)9798331523893

Phishing attacks remain a critical threat to cybersecurity, leveraging deception to steal sensitive information from individuals and organizations. Traditional detection methods often fail to keep up with evolving phishing tactics, highlighting the need for advanced solutions. This paper investigates the use of the Random Forest algorithm, a powerful machine learning technique, to enhance phishing detection systems. By leveraging the ensemble learning approach of Random Forest, we improve accuracy and efficiency in classifying phishing attempts, while reducing false positives. The methodology includes data preprocessing, feature extraction, and model implementation, showcasing its effectiveness in real-time detection. The study also emphasizes the importance of continuous learning, allowing the model to adapt to new phishing strategies, thereby offering a robust defense against emerging threats. This research contributes to advancing cybersecurity by providing a dynamic, scalable approach to phishing detection. © 2025 IEEE.

关键词： Random forests

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Empowering IoT Security by Classifying Threat Detection Through Machine Learning

Empowering IoT Security by Classifying Threat Detection Thro...

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2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Balavaseekaran, I. Dileep, Ambili Sabitha, Sai Hindustan Institute Of Technology And Science Dept Of Computer Science And Engineering Chennai India

ISBN: (纸本)9798350364828

Daily lives have been made much easier with the invasion of IoT devices in many fields such as health care, education, smart homes, agriculture, etc. Nowadays these devices are prone to many cyber attacks that exist currently and require impregnable security. The most common attacks that happen on an IoT device are DoS and DDoS. The methodology considered in this research employs machine learning algorithms and PCA (Principal Component Analysis) to classify the type of DDoS and DoS attacks that occur on IoT devices. The research also includes the implementation of genetic algorithm in evolutionary feature selection to optimize the features used in the classification process. This research work employs six supervised machine-learning algorithms such as K-Nearest Neighbor (KNN), Decision Tree, Naive Bayes, Neural Network, Random Forest, and Boosted Tree, to classify the different types of attacks that may occur in the Internet of Things (IoT) devices and a comparative analysis on these algorithms are performed. © 2024 IEEE.

关键词： Internet of things

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Convolutional Neural Networks (CNN) and Facial Features based Smart Driver Alertness Detection 7

Convolutional Neural Networks (CNN) and Facial Features base...

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7th International Conference on Trends in Electronics and Informatics, ICOEI 2023

作者： Kumar, K.V Pavan Chidanand, M. Shabu, S.L.Jany Grace, L.K.Joshila Poornima, D. Sathyabama Institute of Science and Technology Dept. of Computer Science and Engineering Chennai600119 India

ISBN: (纸本)9798350397284

The development of machine learning assists drivers in the form of unmanned automobiles etc. Nearly 22% of accidents are caused by the driver's drowsiness and lack of alertness. This study intends to monitor the alertness of driver by analyzing their facial expressions and eye variations. The developed smart processing system substantially reduces highway accidents. The proposed model considers various facial characteristics like PERCLOS, Eye Aspect Ratio (EAR), blink values, and yawning. The driver is continually observed by the camera in the proposed method. HC classifiers are used to identify the driver's face. CNN uses the extracted eye images to determine whether the eyes are closed. Based on the classification results, EAR is measured. If the mouth is detected to be open, the lip distance (yawn) is measured. Finally, a warning will be generated if the driver is in a drowsy state. © 2023 IEEE.

关键词： Aspect ratio

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Traffic Sign Recognition using Shift In-variant 2-D ConvNet 7

Traffic Sign Recognition using Shift In-variant 2-D ConvNet

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Ramsundar, B. Sudharsan, S. Pravin, A. Sathyabama Institute of Science And Technology Dept of Computer Science And Engineering Chennai India

ISBN: (纸本)9798350359299

This research introduces a novel method for traffic sign identification utilizing a variant 2-D convolutional neural network (CNN) architecture with a shift-invariant operation. Addressing challenges such as variations in size, illumination, occlusions, and perspective, the proposed approach efficiently learns and identifies traffic signs regardless of their location in the input image. The CNN architecture employs common convolutional layers followed by separate shift-invariant convolutional layers for each type of traffic sign, enabling the network to extract both shared and individual traits effectively. Tests conducted on benchmark datasets including the LISA Traffic Sign Dataset (LISA-TSD) and German Traffic Sign Recognition Benchmark (GTSRB) demonstrate superior recognition accuracy compared to state-of-the-art methodologies. The hierarchical design of the proposed model enhances recognisability, making it suitable for intelligent transportation systems. Overall, the study presents a promising variant 2-D CNN model for traffic sign identification with significant potential for real-world applications. © 2024 IEEE.

关键词： Traffic signs

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Deep Learning-Based Approach for Detecting Copy-Move Forgery in Digital Images 27

Deep Learning-Based Approach for Detecting Copy-Move Forgery...

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27th International Symposium on Wireless Personal Multimedia Communications, WPMC 2024

作者： Dar, Navid Ahmad Atsya, Rani Kumar, Anuj Sharda University dept. of Computer Science and Engineering UP Greater Noida India Greater Noida Institute of Technology dept. of computer applications UP Greater Noida India

ISBN: (纸本)9798350392319

Copying and pasting a portion of the same image to hide another portion is the most common method of fraudulent image enhancement. Cloning a restricted portion of an image and pasting it once or more inside a comparable picture is known as copy-move image forgery (CMF). The procedure aims to highlight or hide a certain aspect in the finished picture, usually a person or an object. The harm brought about by the horrible deed frequently takes unexpected forms. For the automated identification of CMF, the paper proposes a Generative Adversarial Network (GAN) and Convolutional Neural Network (CNN) approach. The CASIA 2.0 dataset's, which includes both real and fake photos, was used in the study. The proposed model is simple to use, and its application shows that it is faster than previous approaches that are now regarded as state of the art. The results show that, when compared to the other currently available methods, the recommended approach achieves the highest level of accuracy (90.56%). © 2024 IEEE.

关键词： Generative adversarial networks

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'Precision Agriculture Farming' Enhancing Farming Efficiency through technology Integration 1

'Precision Agriculture Farming' Enhancing Farming Efficiency...

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

作者： Sharma, Prashant Kumar, Pratyaksh Singh, Praveen Kumar Rana, Arun Kumar Galgotias College of Engineering And Technology Dept. Of Computer Science And Engineering Greater Noida India

ISBN: (纸本)9798350356816

Precision agriculture, or precision farming, or smart farming, refers to a contemporary approach that combines technology and data analytics with agricultural practices. This research paper examines the concept of precision agriculture, its principles, advantages, disadvantages, and potential to transform farming practices. Through the use of various technologies including GPS, sensors, drones, and data analytics, precision agriculture seeks to maximize resource efficiency, enhance crop yields, reduce the environmental footprint, and boost overall profitability for the farm. The paper also addresses case studies and real-life implementations of precision agriculture and illustrates its effectiveness in various sectors of agriculture. It further identifies the challenges and constraints of adopting precision agriculture and proposes possible solutions to overcome these challenges. This paper generally offers insight into the revolutionary potential of precision agriculture in reshaping the future of farming. © 2024 IEEE.

关键词： Smart agriculture

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Phase Fraction Calculation in Multiphase Steel Microstructures Through Semantic Segmentation 10

Phase Fraction Calculation in Multiphase Steel Microstructur...

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10th International Conference on Applied System Innovation, ICASI 2024

作者： Swain, Bishal Ranjan Cheoi, Kyung Joo Ko, Jaepil Kumoh National Institute of Technology Dept. of Computer & Ai Convergence Engineering Gumi Korea Republic of Chungbuk National University Dept. of Computer Science Cheonju Korea Republic of

ISBN: (纸本)9798350394924

The characterization of a material is defined by its transformation kinetics which is measured using the fraction of volume transformed. In common situations, a polycrystalline parent phase is transformed into various sub-phase constituent regions based on the desired material property. Characterization of such multiphase regions include accurate identification of phase and calculation of the phase fraction. To this end, we propose using semantic segmentation to identify and calculate the phase fraction in steel microstructures. We assign each pixel in an image to accomplish both phase identification and segmentation which we perform using enhanced ELU-Net coupled with improved combined loss function. Though scalable experiments we demonstrate more accurate phase fraction calculation via superior semantic segmentation by achieving an overall 8.6% performance increase over baseline with mean phase fraction error of 5.69%. © 2024 IEEE.

关键词： Semantic Segmentation

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Hybrid Model for Image Forgery Detection In Smart Healthcare

Hybrid Model for Image Forgery Detection In Smart Healthcare

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2023 International Conference on computer, Electronics and Electrical engineering and their Applications, IC2E3 2023

作者： Srilakshmi, V. Purushotham, D. Abhinav, Chinta Godalla, Nikhil Varshith, S.V. Gokaraju Rangaraju Institute of Engineering and Technology Dept. of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350338003

Smart Healthcare Systems (SHS) are gradually replacing traditional healthcare systems that involve human intervention. This is due to the advancement of IOT devices and also the introduction of edge computing. SHS provides security, flexibility, and efficiency. A key feature of SHS is to transport images from one place to another in real-time. Due to technological advancements, these images can tamper which is image forgery, this threats security in SHS. To tackle this problem, Image Forgery Detection using Support Vector Machines (SVM) and Extreme Learning Machines (ELM) is proposed in this model. We combine the scores of both SVM and ELM using Bayesian Sum Rule (BSR). The model is utilized to verify if the forgery is done or not. This model can be inbuilt into the SHS. Forgery Detection enhances security and reduces the threat of systems being failed, as images play a key role in the treatment procedure. © 2023 IEEE.

关键词： Support vector machines

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Microwave Marvels: Design and Computational Analysis of an Ultra-thin Metamaterial Absorber for C, X and K Band 6

Microwave Marvels: Design and Computational Analysis of an U...

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6th International Conference on Electrical engineering and Information and Communication technology, ICEEICT 2024

作者： Tasnim, Nowshin Rattre, Naziya Hasan Shawon, Sarowar Morshed Alam, Mohammed Shamsul Alam, Touhidul Chittagong University of Engineering & Technology Dept. of Computer Science & Engineering Chittagong4349 Bangladesh International Islamic University Chittagong Dept. of Computer Science & Engineering Chittagong4318 Bangladesh University of Science & Technology Chittagong Dept. of Computer Science & Engineering Chattogram4202 Bangladesh Universiti Kebangsaan Malaysia Bangi43600 UKM Malaysia

ISBN: (纸本)9798350385779

Metamaterial absorbers are an advancement in material science as they provide more advantages in comparison to conventional materials. Achieving miniaturization with a multilayered structure is the primary challenge here. In this study, a novel metamaterial absorber design is proposed for microwave application. The proposed design is shaped as a squad of four segments where each segment contains an octagonal split ring and a central ring. The multi-layered design of the absorber helps to secure a high absorption rate. An extensive range of research is done to determine particular measurement units, and geometric shapes with the help of CST Studio Suit 2019. Here both ground and patch are designed using copper and the separator is designed with Roger. Moreover, the thickness of the absorber is only 3.4 mm which ensures its ultra-thin and lightweight nature. With an elaborated numerical analysis, it has been shown that the proposed absorber provides 90% to 99% absorption at the C, X, and K bands. Moreover, a concise computational analysis is accomplished with FR4 lossy, Rogers RT6010LM lossy, and Rogers RT5870LM lossy as dielectric constant. The key contribution of this research is the high absorption rate, coupled with minimal thickness and simple structure. © 2024 IEEE.

关键词： Metamaterials

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Evaluation of Machine Learning Models in Student Academic Performance Prediction 5

Evaluation of Machine Learning Models in Student Academic Pe...

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5th International Conference on Advanced Research in Computing, ICARC 2025

作者： Sandeepa, A.G.R. Mohottala, Sanka General Sir John Kotelawala Defence University Dept. of Computer Engineering Ratmalana Sri Lanka Sri Lanka Institute of Information Technology Dept. of Computer Science Malabe Sri Lanka

ISBN: (纸本)9798331530983

This research investigates the use of machine learning methods to forecast students' academic performance in a school setting. Students' data with behavioral, academic, and demographic details were used in implementations with standard classical machine learning models including multi-layer perceptron classifier (MLPC). MLPC obtained 86.46% maximum accuracy for test set across all implementations while for train set, it was 99.45%. Under 10-fold cross validation, MLPC obtained 79.58% average accuracy for test set while for train set, it was 99.65%. MLP's better performance over other machine learning models strongly suggest the potential use of neural networks as data-efficient models. Feature selection approach played a crucial role in improving the performance and multiple evaluation approaches were used in order to compare with existing literature. Explainable machine learning methods were utilized to demystify the black box models and to validate the feature selection approach. © 2025 IEEE.

关键词： Students

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