Aug 2026· International Journal of Information Security· Vol 25· 0 citations· 35 references
Abstract
Phishing attacks via desktops, smartphones and internet of things devices are becoming increasingly sophisticated, posing critical security challenges for digital infrastructures. Defending against these attacks requires AI-based detection models that maintain high accuracy, since false positives or negatives can lead to severe breaches, while remaining lightweight enough to run on resource-constrained client devices. Split Learning (SL) meets these requirements by having clients compute only initial model layers locally and transmit intermediate activations (“smashed data”) to a server for the remaining inference, avoiding direct sharing of raw inputs. However, prior work in the image domain has shown that smashed data can leak original content, suggesting that SL may not be safe for user privacy. Therefore, it is essential to investigate whether these privacy risks also extend to language-model–based SL systems, which have fundamentally different neural network architectures, including attention mechanism. This paper introduces the Semantic Information Reconstruction Attack (SIRA), a novel framework designed to infer sensitive semantic elements directly from smashed data by leveraging the generative capabilities of large language models. In experiments on real-world phishing datasets, SIRA outperforms conventional reconstruction attacks in accurately inferring private webpage information. These findings reveal a potential privacy vulnerability in SL-based language models for security applications and motivate the development of targeted defense strategies.
A high-efficiency detection framework utilizing DistilBERT, a distilled knowledge representation of the BERT transformer is proposed, substantiate the viability of Knowledge Distillation as a mechanism to deploy state-of-the-art semantic security filters on edge infrastructure.
Mrinal Mrinal, Neeraj Kumar· International Journal of Cre...· 0 citations
A hybrid phishing detection framework that integrates three complementary techniques: DistilBERT for semantic analysis of URL text, Graph Neural Networks for modelling structural relationships among URL components, and LightGBM for efficient metadata-based feature classification is proposed.
Ms. I. Shalini, Ms. G. Sujini· International Journal for Re...· 0 citations
Phishing email detection has been destabilised by Large Language Models (LLMs), which allow attackers to mass-produce fluent, contextually plausible messages. Detectors tuned on legacy corpora, notably the 2008 CEAS spam collection, consequently report benchmark accuracies that do not survive deployment. We present PhishingGAT, a detector that fuses word-level semantic features with structural ones and is hardened against adversarial perturbation. Each email is represented as an undirected graph whose nodes are unique tokens carrying 100-dimensional GloVe embeddings, and whose edges record co- occurrence inside a three-token sliding window. A two-layer Graph Attention Network (GAT) then learns per-edge importance over that graph. Robustness comes from Projected Gradient Descent (PGD) adversarial training, applied to continuous node features during optimisation only. Training uses a hybrid corpus of 70,716 emails assembled from the CEAS collection, the Enron collection, and curated LLM-generated samples. Evaluation follows two protocols. The first is strictly zero-shot: 1,186 unseen LLM-generated adversarial emails, on which the model reaches 77.40 per cent accuracy and an area under the receiver operating characteristic curve (AUC) of 0.8700. The second is domain-adapted: 401 held-out hard samples, on which accuracy rises to 90.27 per cent and AUC to 0.9717. The 12.87 percentage-point difference between the two is, to our knowledge, the first such measurement reported for graph-based phishing detection, and it quantifies a degradation that same-distribution benchmarks systematically hide. An ablation separates the contribution of attention from that of adversarial defence, and repeated runs across independent random seeds confirm that the reported figures are reproducible rather than seed artefacts.
R. Kodali, Siva Rama Krishna T Dr· International Journal of Inn...· 0 citations
Results demonstrate the promise of the unified, interpretable approach in advancingadaptive and trustworthy generalized phishing detection systems and integrate interpretability into model decisions using Explainable AI, particularly SHapley Additive exPlanations.
Lea Mansour, Nour Hilal, Nadine Abbas et al.· Computer and Decision Making...· 2 citations
The security landscape is increasingly at risk from phishing attackers; individuals engaging in this type of attack successfully persuade users to divulge sensitive data such as identity information or financial resources. Existing blacklist-based solutions and other approaches have proved ineffective because the adversaries are able to adjust to current bottleneck detectors and because machine learning models are based on hand-crafted behavior patterns. Deep learning brings the inductive bias of generalization to the fore while explicitly constraining the architectures to only hold local representations and to limit their capacity to express long-range relationships in data. We present a model based on CapsNet for phishing website detection in this paper. In the framework of CapsNets, dynamic routing is used to fortify the relationship between spatial and hierarchical features in a model that is built upon these orders between features. This approach is mostly to enable the model to gain complex relationships in a URL string and HTML structure. As a result of this property, the architecture is resistant to various types of adversarial perturbations like character replacement, subdomain mimicry, and misphrasing in phishing URLs. The model is evaluated the model using datasets from PhishTank, OpenPhish, and a benign domain list sourced from the Tranco list. Experimental results show that the proposed method is superior to the state-of-the-art deep learning methods and traditional classifiers in terms of accuracy, precision, recall, and ROC-AUC and reduces the false positive rate. The results confirm that CapsNet is a stable and flexible method for phishing classification that can be used for designing and implementing secure reliable web protection systems.
Sujatha Krishna, Krishna Chandra M, Rajesh Natarajan et al.· 2026 4th International Confe...· 0 citations
The Adversarial-Resilient Lightweight Random Forest (AR-LRF) model is proposed, combining controlled ensemble complexity with simulated adversarial perturbations applied during training to mitigate adversarial vulnerabilities.
A. Chaudhuri, M. B· Scientific Reports· 0 citations