arXiv subjects

Jema David Ndibwile

Publications and source records attributed to Jema David Ndibwile.

5 recordsLinked to original sources

arXiv2026

Pre-Model Representation Failures in GNN-Based Smart Contract Vulnerability Detection

This paper is a failure analysis of the representation layer underlying GNN-based smart contract vulnerability detectors. These systems convert source code into graphs before any learning takes place; if the graph fails to capture the code's semantics, no model improvement can compensate. We investigate GNNSCVulDetector and identify four failures. First, structurally different contracts produce byte-for-byte identical graphs, constituting a concrete evasion attack. Second, graph construction is governed by a hardcoded 47-entry variable whitelist (including one duplicate entry), which constrains what the extractor can recognise. As a consequence, identical vulnerabilities with different variable names produce inconsistent graphs, graph quality degrades as naming diverges from the whitelist, and when no entry matches the pipeline produces structural output not grounded in source variables. Third, the C node (the graph element representing the external caller that triggers a reentrancy attack) is absent from even the most canonical vulnerable contract in the literature. Fourth, a controlled experiment confirms this as a direct misclassification: a fully exploitable contract is labelled safe because the C -> W edge is never constructed. All four failures are demonstrated experimentally. Current accuracy figures in the literature are measured under conditions that do not expose these failures. We demonstrate one confirmed case of misclassification caused directly by a representation-layer failure; the prevalence of such failures in real-world contract populations remains an open empirical question.

cs.CR↗

arXiv2026

Adversarial Robustness in Smishing Detection: A Comparative Analysis of Adversarial Fragility in Classical vs. Transformer-Based Detection Systems

Smishing detection systems are commonly trained and evaluated on clean, monolingual text. In low-resource settings, however, attackers frequently circumvent these systems through character obfuscation, cross-lingual code-switching, and structural perturbation. This study evaluates adversarial robustness for five model architectures: three classical lexical models (Random Forest, XGBoost, CNN+BiLSTM) and two multilingual transformers (mBERT, XLM-RoBERTa), using a dataset of 27,037 messages. Classical models are subjected to black-box generic attacks, while transformers are evaluated with attention-guided targeting. Each model is tested across three attack types and intensity levels, with performance measured by the Robustness Degradation Ratio (RDR). The results reveal a distinct architectural boundary: classical models experience near-catastrophic failure under character obfuscation and structural perturbation (RDR up to 0.988), whereas transformers demonstrate significantly greater resilience (RDR up to 0.351), with structural perturbation representing their most pronounced vulnerability. Effect-size analysis (Cliff's d) indicates a substantial difference between the two model categories. Within the transformer group, XLM-RoBERTa, despite achieving a higher clean-text baseline, exhibits greater degradation than mBERT. These findings demonstrate that clean-text performance is not a reliable predictor of adversarial robustness. Statistical validation using Mann-Whitney U and Friedman tests confirms that these patterns are attributable to model architecture rather than sampling. The results underscore the necessity for architecture-specific defences and frame smishing detection as an adversarial cybersecurity challenge rather than a static classification task.

cs.CR↗

arXiv2026

Evaluating Endpoint Detection Robustness Against Genetic Algorithm Driven Code Transformations

Post-compromise test variants are widely used in controlled security evaluation and endpoint robustness benchmarking. However, modern Antivirus (AV) and Endpoint Detection and Response (EDR) systems increasingly combine signature- and behavior-based detection, challenging the reliability of conventional detection pipelines under adaptive variation. This study introduces ShellForge, a Genetic Algorithm (GA)-driven framework that evolves post-compromise variants representative of remote command execution to generate functionally equivalent variants for systematic detection evaluation. ShellForge applies syntactic transformations, encoding schemes, and structural permutations guided by a multi-objective fitness function informed by AV and EDR detection feedback. We compare ShellForge against representative baseline transformation frameworks under identical sandbox configurations. Our findings highlight measurable robustness gaps in baseline signature- and behavior-oriented detection pipelines under controlled variant generation. In addition, we propose a reproducible benchmark for endpoint detection robustness evaluation, motivating the need for robustness-aware defensive monitoring and behavioral correlation.

cs.CR↗

arXiv2026

Architectural Bias in Face Presentation Attack Detection: A Comparative Study of Vision Transformers and Convolutional Neural Networks

Face Presentation Attack Detection (PAD) systems constitute a critical security layer in biometric authentication; however, existing approaches exhibit systematic performance disparities across demographic groups, disproportionately affecting individuals with darker skin tones. This paper presents a comparative empirical investigation of whether Vision Transformer architectures reduce demographic bias in face PAD systems relative to convolutional baselines. Experiments are conducted on the CASIA-SURF Cross-Ethnicity Face Anti-Spoofing (CeFA) dataset. Three architectures are evaluated: a Multimodal ViT-Tiny trained from scratch, a ResNet18 CNN baseline, and a pretrained DeiT-S fine-tuned on CeFA across African, East Asian, and zero-shot Central Asian demographic groups. DeiT-S achieves the highest overall accuracy of 97.27% and the lowest EER of 0.86%, outperforming ResNet18 at 90.15% accuracy. In terms of fairness, DeiT-S reduces the inter-ethnic ACER gap between African and East Asian subjects to 0.13%, compared to 0.75% reported in an LBP-based work [6], representing an 83% reduction. Most notably, while ResNet18 records a BPCER of 10.44% on zero-shot Central Asian subjects, DeiT-S maintains 2.89% on the same unseen group, demonstrating a 3.6x generalization advantage. These results suggest that pretrained Vision Transformers achieve superior PAD accuracy, produce smaller demographic performance gaps, and generalize more equitably across unseen demographic groups, indicating that cross-demographic fairness in PAD may partly be influenced by architectural design.

cs.CV↗

arXiv2026

Using Machine Learning to Enhance the Detection of Obfuscated Abusive Words in Swahili: A Focus on Child Safety

The rise of digital technology has dramatically increased the potential for cyberbullying and online abuse, necessitating enhanced measures for detection and prevention, especially among children. This study focuses on detecting abusive obfuscated language in Swahili, a low-resource language that poses unique challenges due to its limited linguistic resources and technological support. Swahili is chosen due to its popularity and being the most widely spoken language in Africa, with over 16 million native speakers and upwards of 100 million speakers in total, spanning regions in East Africa and some parts of the Middle East. We employed machine learning models including Support Vector Machines (SVM), Logistic Regression, and Decision Trees, optimized through rigorous parameter tuning and techniques like Synthetic Minority Over-sampling Technique (SMOTE) to handle data imbalance. Our analysis revealed that, while these models perform well in high-dimensional textual data, our dataset's small size and imbalance limit our findings' generalizability. Precision, recall, and F1 scores were thoroughly analyzed, highlighting the nuanced performance of each model in detecting obfuscated language. This research contributes to the broader discourse on ensuring safer online environments for children, advocating for expanded datasets and advanced machine-learning techniques to improve the effectiveness of cyberbullying detection systems. Future work will focus on enhancing data robustness, exploring transfer learning, and integrating multimodal data to create more comprehensive and culturally sensitive detection mechanisms.

cs.CL↗