arXiv subjects

Saba Aslam

Publications and source records attributed to Saba Aslam.

3 recordsLinked to original sources

arXiv2026

A Calibrated and Explainable Bimodal Machine Learning Framework for Hybrid Intrusion Detection

Modern communication systems face critical gaps in detecting unknown attacks and rare threat classes due to extreme data imbalance and black-box decision logic. We propose a bimodal framework of calibrated and explainable machine learning (ML) for network security, unifying known-class precision with open-set generalization without the complexity of deep learning. Our framework introduces security-oriented feature extraction to enhance signal-to-noise ratio, hybrid resampling (ADASYN + manual boosting) to reduce class imbalance, isotonic calibration and adaptive thresholding ($\tau=0.30$ for XSS) to recover recall for rare attacks, and SHAP-based explainability to validate domain-aligned decision logic. Evaluated on the CIC-IDS2017 dataset and compared with prior ML models and studies, our framework achieves significant accuracy on known attacks (Macro F1 = 0.8626) and detects unknown classes at 1% FPR with TPR up to 90.17% (DoS slowloris), and 77.04% (Web-XSS). The SHAP analysis confirms decisions are driven by security-relevant features, not model artifacts. Our work bridges the gap between theoretical models and operational IDS by delivering calibrated, explainable, and open-set-capable attack detection and prevention in a single, reproducible framework. Keywords: intrusion detection, cybersecurity and privacy, explainable AI, machine learning

cs.CR↗

arXiv2024

CEL: A Continual Learning Model for Disease Outbreak Prediction by Leveraging Domain Adaptation via Elastic Weight Consolidation

Continual learning, the ability of a model to learn over time without forgetting previous knowledge and, therefore, be adaptive to new data, is paramount in dynamic fields such as disease outbreak prediction. Deep neural networks, i.e., LSTM, are prone to error due to catastrophic forgetting. This study introduces a novel CEL model for continual learning by leveraging domain adaptation via Elastic Weight Consolidation (EWC). This model aims to mitigate the catastrophic forgetting phenomenon in a domain incremental setting. The Fisher Information Matrix (FIM) is constructed with EWC to develop a regularization term that penalizes changes to important parameters, namely, the important previous knowledge. CEL's performance is evaluated on three distinct diseases, Influenza, Mpox, and Measles, with different metrics. The high R-squared values during evaluation and reevaluation outperform the other state-of-the-art models in several contexts, indicating that CEL adapts to incremental data well. CEL's robustness and reliability are underscored by its minimal 65% forgetting rate and 18% higher memory stability compared to existing benchmark studies. This study highlights CEL's versatility in disease outbreak prediction, addressing evolving data with temporal patterns. It offers a valuable model for proactive disease control with accurate, timely predictions.

cs.LG↗

arXiv2024

AntiPhishStack: LSTM-based Stacked Generalization Model for Optimized Phishing URL Detection

The escalating reliance on revolutionary online web services has introduced heightened security risks, with persistent challenges posed by phishing despite extensive security measures. Traditional phishing systems, reliant on machine learning and manual features, struggle with evolving tactics. Recent advances in deep learning offer promising avenues for tackling novel phishing challenges and malicious URLs. This paper introduces a two-phase stack generalized model named AntiPhishStack, designed to detect phishing sites. The model leverages the learning of URLs and character-level TF-IDF features symmetrically, enhancing its ability to combat emerging phishing threats. In Phase I, features are trained on a base machine learning classifier, employing K-fold cross-validation for robust mean prediction. Phase II employs a two-layered stacked-based LSTM network with five adaptive optimizers for dynamic compilation, ensuring premier prediction on these features. Additionally, the symmetrical predictions from both phases are optimized and integrated to train a meta-XGBoost classifier, contributing to a final robust prediction. The significance of this work lies in advancing phishing detection with AntiPhishStack, operating without prior phishing-specific feature knowledge. Experimental validation on two benchmark datasets, comprising benign and phishing or malicious URLs, demonstrates the model's exceptional performance, achieving a notable 96.04% accuracy compared to existing studies. This research adds value to the ongoing discourse on symmetry and asymmetry in information security and provides a forward-thinking solution for enhancing network security in the face of evolving cyber threats.

cs.CR↗