Agentic artificial intelligence (AI) is increasingly being explored for automating offensive security and red teaming tasks, enabling systems that can coordinate multi-step cyber operations through structured decision-making. While prior research has investigated reinforcement learning (RL) agents and large language models (LLMs) for penetration testing, most studies are evaluated in simulated or abstract environments, with limited empirical validation in real cyber-range settings. This paper presents a controlled experimental evaluation of an LLM-guided offensive security pipeline against deterministic scripted baselines in a cyber-range environment. Using a vulnerable Kioptrix virtual machine and a Kali Linux attacker, we implement three deterministic pipelines, fixed-path, sequential and rule-based, alongside two configurations of an LLM-guided agent: an initial version (LLM V1) and a refined constrained controller (LLM V2). All approaches operate within a restricted and auditable action space executed through predefined tools. Across repeated trials, the initial LLM configuration exhibits reduced reliability and increased execution cost due to exploratory behaviour. In contrast, the refined controller achieves a 100% success rate, reduces execution steps, eliminates wasted actions and matches the efficiency of rule-based automation. These results show that, within a controlled cyber-range environment, LLM-guided agents can approximate deterministic performance when appropriate constraints are applied. This suggests that agent-based approaches to offensive security can support semi-autonomous red teaming workflows, provided that decision-making is governed by structured control policies.
Atif Chowhan, Yasir Hamid· Journal of Information &...· 0 citations
Tremendous numbers of images and videos are generated throughout the life cycle of construction projects, offering rich visual data that, if analysed effectively, can support automation in progress monitoring, safety management, quality control, and productivity tracking. Within this context, image classification plays a foundational role: it is the supervised task of mapping an image to one of K semantic categories by learning from labelled examples, requiring the extraction of discriminative visual structure and the production of calibrated class predictions. Despite major advances from handcrafted feature pipelines to Convolutional Neural Networks (CNNs), fair comparison across methods remains challenging due to inconsistent preprocessing, dataset splits, training budgets, and reporting standards. This paper introduces a unified and fully reproducible benchmarking framework that standardises preprocessing, enforces fixed train–test splits, aligns training configurations, and evaluates models using a consistent multi-metric suite. Under identical experimental conditions, we benchmark four paradigm families: classical machine learning (ML) models, handcrafted feature-based methods (Histogram of Oriented Gradients (HOG), Local Binary Patterns (LBP), Bag-of-Visual-Words (BoVW) [Formula: see text] Support Vector Machine (SVM)), and CNNs (LeNet-5, ResNet-18), across three datasets (MNIST, Fashion-MNIST, CIFAR-10). We formalise image classification mathematically as a supervised learning problem and assess performance using accuracy, macro-averaged [Formula: see text]1-score, and macro-averaged ROC–AUC, complemented by learning-curve and robustness analyses. Results show that ResNet-18 achieves the strongest performance on all datasets (98.95%, 90.27%, 72.90%), SVM with a Radial Basis Function (RBF) kernel remains competitive on grayscale benchmarks, and hand-crafted pipelines degrade sharply on CIFAR-10. ROC–AUC analysis further highlights calibration–accuracy discrepancies, such as Naïve Bayes achieving high AUC but modest accuracy on MNIST. These findings emphasise the importance of task-aware model selection and demonstrate that compute-aligned, multi-metric benchmarking is essential for fair and reproducible cross-paradigm evaluation. All CNN results are reported under a 50-epoch training budget, which represents a lower bound on converged performance; future extensions will incorporate domain-specific datasets and pre-trained architectures to broaden practical applicability.
Yasir Hamid, N. Ababneh, A. Abasi et al.· Journal of Information &...· 0 citations