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All Journal Journal of ICT, Design, Engineering and Technological Science
Noor Zaman Jhanjhi
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Engineering Mechanical Engineering

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BERT‑LSTM‑LGBM Approach for DDoS Attacks Detection in IoT Network Using ML Imdad Ali Shah; Noor Zaman Jhanjhi
Journal of ICT, Design, Engineering and Technological Science Volume 9, Issue 2
Publisher : Journal of ICT, Design, Engineering and Technological Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33150/JITDETS‑9.2.3

Abstract

New cybersecurity challenges have increased as the interconnected IoT devices grow, such as DDoS attacks, which are observed as more attacks exploit resource‑constrained IoT devices. Conventional detection mechanisms often fail to capture the dynamic and diverse nature of IoT network traffic, and several researchers and professionals have addressed these concerns. In view of the issues raised by the researchers, the presented models need to enhance their accuracy and performance. The BERT_LSTM‑LGBM model has been proposed for an intelligent and accurate DDoS attack detection in IoT devices. BERT component is used to remove deep contextual features from network traffic data, capturing intractable relationships and semantic dependency. The long Short‑Term Memory (LSTM) network further improves temporal arrangements learning to detect sequential anomalies, while the LGBM classifier promises high‑speed and comprehensible decision‑making. The results show that the BERT‑LSTM‑LGBM framework is robust and can detect diverse DDoS attack patterns, offering a scalable and intelligent solution for securing next‑generation IoT infrastructures. Our proposed model presents its exceptional proficiency in threat detection within the IoT environment. We achieved remarkable results such as 99.8%, 98%, and 99%.
A Hybrid NLP and Deep Learning Framework for Phishing Detection in Emails and URLs Basheer Riskhan; Md Saiful Arefin; Mutasim Billah; Abdullah Al Hadi; Siti Shafrah Shahawai; Siva Raja Sindiramutty; Noor Zaman Jhanjhi
Journal of ICT, Design, Engineering and Technological Science Volume 9, Issue 2
Publisher : Journal of ICT, Design, Engineering and Technological Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33150/JITDETS-9.2.5

Abstract

Phishing attacks are constantly evolving, exploiting users with malicious URLs and misleading emails, while conventional rule‑based detection methods struggle to keep pace with new threats. To improve detection accuracy and adaptability, this study proposes a hybrid phishing detection framework that combines Deep Learning (DL) and Natural Language Processing (NLP) techniques. For email classification, the system uses TF‑IDF‑based feature extraction, including word‑ and character‑level n‑grams, domain encoding, and link‑count analysis; for URL analysis, character‑level tokenisation and manually created structural features are used. In addition to CNN, LSTM, and Hybrid CNN‑LSTM models for URL classification, three deep learning architectures are developed for email detection: Convolutional Neural Network (CNN), Bidi‑rectional Long Short‑Term Memory (BiLSTM), and a Hybrid CNN‑BiLSTM model. The hybrid architectures efficiently capture intricate phishing patterns by combining sequential dependency learning with spatial feature extraction. Both primary email and large‑scale URL datasets are used, with stratified data partitioning and suitable preprocessing methods, to assess the proposed framework. The methodology addresses the drawbacks of static, single‑model systems in contemporary cybersecurity environments by demonstrating a scalable, flexible approach to phishing detection.
Co-Authors Abdullah Al Hadi Basheer Riskhan Imdad Ali Shah Md Saiful Arefin Mutasim Billah Siti Shafrah Shahawai Siva Raja Sindiramutty