Siva Sakthii U S’ Post

Excited to share my latest publication in the International Journal of Scientific Research in Engineering and Management (IJSREM)! 🎉 📄 Title: Phishing Website Detection Using Machine Learning In this study, we developed a method to detect phishing websites using machine learning. By analyzing URL features like length, special characters, subdomains, and keywords, we built a model that accurately identifies phishing URLs. This research highlights the methodology and the model's performance in real-world scenarios. Grateful for the support and guidance from my mentor, Ms. Gayathri N, and collaborators, Siva Sakthii U S and Janani Dhandayutham. 📖 Read the full paper here: https://lnkd.in/g-XMciH2 ISSN: 2582-3930 Impact Factor: 8.448 Volume 8 - Issue 07 July, 2024 #Research #Engineering #AI #MachineLearning #CyberSecurity #IJSREM #Publication #ScientificResearch

🌟 Exciting News! 🌟 I'm thrilled to share that our paper titled "MACHINE LEARNING-BASED STRATEGIES FOR DETECTING CYBERBULLYING IN ONLINE CHATS" has been officially published in the International Journal of Innovative Science and Research Technology - IJISRT (Digital Library). You can read the full paper here: https://lnkd.in/d9T8u5x6 A big thank you to everyone who supported this work especially my supervisor Fati Oiza Salami. This publication marks a significant milestone in our efforts to leverage machine learning for creating safer online environments. Feel free to reach out if you have any questions or would like to discuss the research further! #MachineLearning #Cyberbullying #Research #Publication #Cybersecurity #AI

I'm excited to share that our paper, "STFL: Utilizing a Semi-Supervised, Transfer-Learning, Federated-Learning Approach to Detect Phishing URL Attacks" has been accepted for presentation at the International Joint Conference on Neural Networks (IJCNN) as part of IEEE WCCI, to be held at Pacifico Yokohama, Japan 🇯🇵, from June 30 to July 5, 2024. In this paper, we present a novel machine learning approach for phishing detection that combines semi-supervised learning, transfer learning, and federated learning. We train a Bi-LSTM autoencoder across decentralized edge devices containing unlabeled data. A centralized server collects the Bi-LSTM autoencoders and aggregates them into a global network using the FedAVG algorithm. The server then performs transfer learning to utilize the learned patterns from the global Bi-LSTM autoencoder networks and induce a classification model. Our experiments demonstrate that our proposed approach outperforms existing methods. I would like to thank all co-authors Prof. Yuval Elovici , Prof. Asaf Shabtai and Dr. Edita Grolman, for their help and guidance. Looking forward to presenting our paper at the conference. #IJCNN2024 #IEEEWCCI #DataScience #MachineLearning #CyberSecurity #FederatedLearning

🚨 Excited to Share My Latest Research on Malware Detection! 🚨 As part of my ongoing efforts in the field of cybersecurity, I’ve recently completed research on developing a PE file malware detection system using advanced deep learning techniques like Stacked CNN and RNN (LSTM, GRU) models. 🧠💻 🔍 Key Highlights of the Research: • Leveraged opcode frequency-based static analysis to detect malware in Windows PE files, a format responsible for ~89% of malicious files. • Explored multiple datasets, including the BODMAS dataset (57,293 malware & 77,142 benign samples) and DikeDataset, for a comparative analysis. • Achieved significant improvements in detection accuracy by combining CNNs for static pattern recognition and RNNs for sequential behavior analysis. • Highlighted the importance of model adaptability and optimization, with key findings showing that different architectures perform better depending on the dataset and task. 🔐 With the rising complexity of malware attacks, this research aims to contribute towards more robust detection systems that can adapt to evolving threats, leveraging deep learning to enhance cybersecurity defenses. I’m excited about the potential applications of this work and look forward to exploring future innovations in this field! Thank you to everyone who supported me throughout this journey, especially Prof. Rashmi Rane mam. Feel free to reach out if you’re interested in discussing this further! 💬 #Cybersecurity #MalwareDetection #DeepLearning #RNN #CNN #AI #MachineLearning #Research #BODMAS #DikeDataset #WindowsPE #CyberThreats #TechInnovation

🎉Excited to Announce Our Recent Publication!🎉 Thrilled to share that our research paper titled "Auto Encoder Based Malware Detection Using Machine Learning" has been published in the Journal of Emerging Technologies and Innovative Research (JETIR). This research, which I worked on with Neha B R, Nisarga Raj D focuses on using autoencoders to enhance malware detection using machine learning, introducing innovative methods to improve threat detection accuracy. We’ve also received the official certificate for this publication. 🏆 Check out the full paper here: [Paper Link](https://lnkd.in/gS24PiXG) I would like to thank our beloved HOD Dr. Rajesh K S and our principal Dr. Balakrishna Rayanki for helping us in publishing this paper. Looking forward to future collaborations and continuing our journey in cybersecurity and AI/ML! #cybersecurity #malwareanalysis #research #machinelearning #malwaredetection #AI #publication #innovation

📢 Call for Chapters! Join us in shaping the upcoming book, Adversarial Example Detection, and Mitigation Using Machine Learning, part of the esteemed Advances in Information Security series by Springer! This is a unique opportunity to contribute your expertise to pioneering work in adversarial machine learning, sharing insights, methodologies, and advances with a global audience. Through this collaborative effort, you’ll help set a new standard in understanding and countering adversarial threats, pushing the field forward and inspiring future research. We’re calling on experts in cybersecurity, machine learning, and artificial intelligence to bring fresh perspectives and solutions to this critical topic. Let’s build a definitive resource together. Submit your chapter and become part of this transformative project! 🔗 More Information: https://lnkd.in/eHxAaYpH Editors: Dr. Rahim Taheri, Dr. Ehsan Nowroozi, and Prof. Lucas Cordeiro #CallForChapters #MachineLearning #AdversarialExamples #Cybersecurity #ArtificialIntelligence #InformationSecurity #SpringerBook #ResearchCollaboration #AcademicPublishing #InnovationInSecurity #MLforSecurity #AIResearch #DataScience University of Portsmouth Ravensbourne University London The University of Manchester IEEE IEEE Computer Society #AI #cybersecurity

🚀 Just wrapped up an incredible week at Black Hat 2024! 🌟 The experience was nothing short of amazing, especially completing the Black Hat Machine Learning course. Diving deep into the world of ML, we explored its rapid advancements and the security challenges that come with them. From hands-on labs in Jupyter notebooks to understanding adversarial techniques, this course provided invaluable insights into assessing and securing ML systems. 🛡️💻 A huge thank you to the instructors (Will Pearce Rich Harang Becca Lynch Erick Galinkin) and Elle S. for giving me the opportunity to participate in an engaging and enlightening experience. Looking forward to applying these new skills to tackle emerging security threats in the AI landscape! 🤖🔍 #BlackHat2024 #MachineLearning #CyberSecurity #AI #ML #Training #ProfessionalGrowth

My new paper, "Modeling Emergency Traffic using a Continuous-Time Markov Chain," has been published in the "Journal of Sensor and Actuator Networks". https://lnkd.in/dbJjk78A

#JCP #article #MostView [Most Viewed Paper] 🔔 Title: Comparison of Deepfake Detection Techniques through Deep Learning by Maryam Taeb, and Chi Hongmei from FAMU-FSU College of Engineering, and Florida A&M University See more details at 👉 : https://lnkd.in/gtHkZbwj #CyberSecurity #deepfakedetection #digitalforensics #mediaforensics #deeplearning #VGG19 #manipulation

Today's highlight is Shoshana Sugerman! If the RCC had a lead researcher position, it would definitely be Shoshana. She is always working on two or three different high-impact #research projects at the same time. Whether it's #quantum machine learning, exploring the ethics of Generative #AI on human-facing #cybersecurity tasks, and now evaluating compiler-based mitigation techniques, she is always excited to be contributing to the expansion of the state-of-the-art in cybersecurity knowledge. #security #cybersec #infosec #informationsecurity #generativeai #ml #machinelearning #RPI Rensselaer Polytechnic Institute Rensselaer Polytechnic Institute Information Technology & Web Science