An Enhanced Deep Learning Approach to Detect Cyberattacks and Malware in Cybertext

Sharma, Aayush (2025) An Enhanced Deep Learning Approach to Detect Cyberattacks and Malware in Cybertext. Masters thesis, Dublin, National College of Ireland.

Preview	PDF (Master of Science) Download (1MB) | Preview
Preview	PDF (Configuration Manual) Download (816kB) | Preview

Abstract

Cyberattacks increasingly appear within textual communication such as threat reports, phishing content, incident alerts, and malware descriptions, creating a need for automated systems capable of classifying complex cyber-related language with high reliability. Traditional detection methods and many earlier analytical models often depend on shallow representations or handcrafted features, which can struggle to capture contextual semantics, subtle linguistic cues, and evolving attacker terminology. This study addresses these challenges through a hybrid ALBERT+LSTM framework for multiclass cybertext threat classification, where ALBERT provides compact contextual embeddings and an LSTM layer learns sequential dependencies within the encoded text. The overall workflow follows the KDD process (data selection, preprocessing, transformation, data mining, and evaluation) and includes text cleaning and normalization, transformation into model-ready representations, model training, and consistent metric-based comparison against established machine learning and deep learning baselines. Experimental results show that the ALBERT+LSTM model achieves 0.90 accuracy and an 0.90 F1-score, exceeding the performance of Random Forest, XGBoost, BiLSTM, and GRU under the same evaluation protocol. These findings demonstrate that combining lightweight contextual modelling with sequence learning improves threat-category separation in cybertext and supports robust automated cybersecurity analytics.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Moldovan, Arghir Nicolae UNSPECIFIED
Subjects:	P Language and Literature > P Philology. Linguistics > Computational linguistics. Natural language processing Q Science > QA Mathematics > Computer software > Computer Security T Technology > T Technology (General) > Information Technology > Computer software > Computer Security Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Cyber Security
Depositing User:	Ciara O'Brien
Date Deposited:	04 Sep 2026 10:26
Last Modified:	04 Sep 2026 10:26
URI:	https://norma.ncirl.ie/id/eprint/9830

Actions (login required)

View Item