Enhancing SQL injection detection via hybrid structural and transformer-based semantic feature extraction

Semantic Representation Learning using Transformer Architectures for Context Aware Detection of Web Application Attack

The modern day digital world is virtually entirely run on web applications, which can be used in social media and banking, e-commerce and even healthcare. However, they also continue to get increasingly targetable to advanced cyberattacks such as SQL injection and cross site scripting and command based injection as the...

A Structured Query Language (SQL) Injection Attack Detection Based on Probabilistic Neural Network

—Structured Query Language (SQL) injection attacks remain one of the most critical web application security threats. Deep Learning (DL) models such as Convolutional Neural Networks (CNN) often require high computational resources, large numbers of parameters, and long training times, which limit their suitability for l...

Random Forest over Semantic PDF Object Graphs for Robust Malware Detection

Enhancing Web Application Firewalls with Machine Learning for SQL Injection Detection

A DistilBERT-Stacked Ensemble pipeline to improve detection efficiency and robustness while reducing false-positive and false-negative rates is designed and optimised to highlight the value of adversarial training and stacked meta-learning in building robust Web Application Firewalls for SQLi detection.

EviReqRAG: An Evidence-Based Retrieval-Augmented Generation Framework for Software Requirements and Security Classification

Requirements classification is one of the key steps in requirements engineering, as it supports early analysis, traceability, validation, prioritization, and security-conscious decision making. However, automating this classification is challenging due to the nature of software requirements, which are often short, ambi...

Enhancing Web Application Firewalls with BERT-GNN for SQL Injection Detection

This research study has resulted in an optimised hybrid BERT-GNN pipeline with improved detection accuracy and robustness while reducing false-positive and false-negative rates.