IEEE 2023: WEB SECURITY OR CYBER CRIME

 

IEEE 2023:  Machine Learning and Software-Defined Networking to Detect DDoS Attacks in IOT Networks

Abstract: In an era marked by the rapid proliferation of IoT devices, safeguarding network security has become an imperative task. This interim report provides an overview of our ongoing project, "Empowering Cyber Defense: Machine Learning Algorithm for Attack Identification," aimed at enhancing intrusion detection in IoT environments. Leveraging the power of Artificial Neural Networks (ANN), SVM (Support Vector Machine), a system tackles the formidable challenge of identifying attacks in the NF-ToN-IoT-V2 dataset, Our research explores four key attack classifications: Benign, Scanning, Password-based, and DDoS attacks. Through rigorous experimentation, we assess the efficacy of our proposed model in accurately identifying these attacks. By harnessing the rich features of IoT network traffic data, our system demonstrates promising results in terms of precision, recall, and overall classification performance. This interim report provides insights into our methodology, dataset, and initial findings, underscoring the significance of our research in bolstering IoT network security and defending against evolving cyber threats. In this we achieved validation accuracy.

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IEEE 2023:  Analysis Of Cyber Aggression And Cyber Bullying In Social Networking.

Abstract: Cyber-bullying is a form of cyber-aggression that has become a major concern in today’s information society. In the digital age, the Internet has enabled the circulation of ideas and information and, in turn, has increased awareness among people. However, this does not come with its drawbacks. With the proliferation of online platforms, hoaxers can easily lure people towards their propagandist views or false news. The need to root out such false information and hate speech during this COVID-19 pandemic has never been more essential. The following study presents a survey of various papers that attempt to tackle similar problem statements with fake news, sentiment classification, and topic extraction. The paper focuses on how existing quality research can help improve the current state of research on COVID-19 related datasets by guiding researchers towards valuable procedures to help governmental authorities combat the rise in the spread of false news and the malicious and hate comments. 

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IEEE 2023:  Malware Threat on Edge / Fog Computing Environments from Internet of Things Devices.

Abstract: Integration of the Internet into the entities of the different domains of human society (such as smart homes, health care, smart grids, manufacturing processes, product supply chains, and environmental monitoring) is emerging as a new paradigm called the Internet of Things (IoT). However, the ubiquitous and wide-range IoT networks make them prone to cyber attacks. This research aims to investigate and mitigate the escalating threat of malware in Edge/Fog Computing environments, specifically originating from Internet of Things (IoT) devices. Leveraging the CICIDS 2017 dataset, we propose a two-fold approach employing Support Vector Machine (SVM) algorithm and a Hybrid Deep Learning model, combining Convolutional Neural Network (CNN) with Long Short-Term Memory (LSTM) networks. The SVM algorithm will provide a robust baseline for detecting known malware patterns, while the CNN-LSTM hybrid model will enhance the system's capability to identify complex, evolving threats. Our study seeks to contribute to the development of advanced intrusion detection systems tailored for the unique challenges posed by Edge/Fog Computing, ensuring the security and integrity of IoT-driven ecosystems. The research outcomes are anticipated to shed light on effective strategies to safeguard critical infrastructures in the era of pervasive IoT adoption.

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IEEE 2023: Phishing Web Sites Features Classification Based on Learning Machine.

Abstract:  Phishing are one of the most common and most dangerous attacks among cybercrimes. The aim of these attacks is to steal the information used by individuals and organizations to conduct transactions. Phishing websites contain various hints among their contents and web browser-based information. Phishing is a new type of network attack where the attacker creates a replica of an existing Web page to fool users (e.g., by using specially designed e-mails or instant messages) into submitting personal, financial, or password data to what they think is their service provides' Web site. The purpose of this study is to perform Extreme Learning Machine (ELM) based classification for 30 features including Phishing Websites Data in UCI Machine Learning Repository database.

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IEEE 2023: MACHINE LEARNING

 

IEEE 2023: Improving Medicine Recommendation with Sentiment Analysis of Text Reviews and Machine Learning

Abstract: This study presents a comprehensive comparative analysis of the performance of Artificial Neural Networks (ANNs) and Random Forest Classifiers in binary classification tasks. The ANN model exhibited a commendable accuracy rate of 98%, showcasing its efficacy in accurately classifying instances. Additionally, the Random Forest Classifier demonstrated an equivalent accuracy rate of 98%, affirming its robustness as a classification tool. In-depth examination of confusion matrices provided valuable insights into the strengths and limitations of each model. The study also delves into the interpretability and computational complexity of both approaches, shedding light on their suitability for different applications. Overall, this research contributes to the understanding of the comparative performance of ANN and Random Forest Classifier models, offering valuable guidance for practitioners in selecting the most suitable approach for specific binary classification tasks.

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IEEE 2023:  Secure IDS with Machine Learning & Network Decoy System

Abstract: The rapid growth of network-based attacks and security breaches has highlighted the need for robust Intrusion Detection Systems (IDS) to protect computer networks and sensitive data. The main objective of the proposed system is to develop an Intrusion Detection System (IDS) integrated with a Decoy system, employing the powerful Random Forest and K-Nearest Neighbors (KNN) machine learning algorithms. The primary objective of this project is to design and implement intelligent IDS that can effectively detect and classify various types of network intrusions while minimizing false positives. To enhance the system's security, a Decoy system will be integrated, which aims to deceive potential attackers and divert their attention from the actual network resources. The proposed IDS will leverage the Random Forest algorithm, a powerful ensemble learning technique that combines multiple decision trees to achieve high accuracy and robustness. The project will also explore the application of the K-Nearest Neighbors (KNN) algorithm for intrusion detection. KNN is a non-parametric algorithm that classifies new data points based on the majority vote of their nearest neighbors.

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IEEE 2023: DEEP LEARNING

 

IEEE 2023:  Enhancing Facial Image Forgery Detection through Transfer Learning-Based Classification

Abstract :   In today's world, digital images have become ubiquitous, adorning the screens of our smartphones and populating the web pages of online platforms. Their applications span across numerous industries, including media, forensic and criminal investigations, medicine, and more. However, the accessibility and widespread usage of consumer photo editing tools have made image alteration effortless. This poses significant risks and challenges, particularly in fields where image integrity is paramount, making it exceedingly difficult to authenticate and establish the credibility of digital images.

Digital picture forgery involves manipulating an image's meaning by tampering with its content without leaving discernible traces. This study focuses on the classification of facial image forgeries using transfer learning techniques, specifically employing Convolutional Neural Networks (CNN) techniques. Three transfer learning methods, namely VGG-19, Inception V3, and Dense Net 201, are demonstrated to detect and classify facial image forgeries accurately.

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IEEE 2023: Transfer Learning Strategies for Cardiovascular Disease Detection in ECG Imagery

Abstract : This study proposes a novel approach for detecting cardiovascular diseases (CVD) from electrocardiogram (ECG) imagery using Convolutional Neural Networks (CNNs) and transfer learning techniques. Leveraging the pre-trained models Inception V3 and DenseNet201, we aim to overcome limitations posed by small datasets in medical imaging. Our methodology involves fine-tuning these architectures on a custom dataset of ECG images to adapt them to the task of CVD detection. By transferring knowledge learned from large-scale image datasets, our models achieve superior performance in terms of accuracy and generalization. Through extensive experiments and evaluation, we demonstrate the effectiveness of our approach in accurately diagnosing various cardiovascular conditions. This research contributes to the advancement of computer-aided diagnosis systems for CVDs, providing a scalable and efficient solution for early detection and intervention. Our implementation in Python facilitates easy integration into existing healthcare systems.

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IEEE 2023: Harnessing Audio for Avian Identification:  A Review of Bird Species Identification Techniques.

Abstract : This project aims to advance avian identification through the utilization of audio signals, employing a comprehensive approach that integrates with deep learning techniques. The study focuses on developing three distinct models: Ridge Classifier, Support Vector Machine (SVM) Classifier, and Artificial Neural Network (ANN). By converting audio signals into numerical data using standard Python libraries, we aim to create robust models capable of accurately identifying bird species based on their distinct audio signatures. The Ridge and SVM classifiers, representing classical ML paradigms, will leverage advanced feature extraction methodologies. Simultaneously, the ANN model, a deep learning approach, will harness the power of neural networks to capture intricate patterns within the audio data. This interdisciplinary fusion of ML and deep learning techniques is poised to significantly enhance avian species identification, contributing to biodiversity monitoring and conservation efforts.

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IEEE 2023: ISR-GAN: Improved Super-Resolution based GAN to Resolve Low Resolution Text Images

Abstract : The rapid advancement of Generative Adversarial Networks (GANs) has paved the way for significant breakthroughs in image processing tasks, particularly in the domain of super-resolution. In this proposal, we introduce ISR-GAN (Improved Super-Resolution based GAN), a novel framework tailored specifically for enhancing low-resolution text images. Our aim is to address the challenges associated with resolving degraded text content, where traditional methods often struggle due to the intricate details and sharp edges characteristic of text. By leveraging the adversarial training mechanism of GANs, coupled with advanced super-resolution techniques, ISR-GAN aims to produce high-fidelity, visually appealing reconstructions of low-resolution text images. We propose to employ carefully designed network architecture, incorporating both perceptual and adversarial loss functions, to encourage the generation of text images that not only exhibit enhanced resolution but also preserve textual content and clarity. Through extensive experimentation and evaluation, we seek to demonstrate the effectiveness and superiority of ISR-GAN over existing methods, offering a promising solution for improving the quality and readability of low-resolution text images.

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