A Lightweight Hybrid MLP-Based Framework for Real-Time Phishing URL Detection Using Structural URL Features

Phishing attacks remain a major cybersecurity threat, exploiting deceptive URLs to steal sensitive user information. Traditional blacklist and rule-based detection approaches are reactive and often fail to identify newly emerging phishing URLs. This paper proposes a lightweight hybrid framework f...

Token-Level Generalization in LoRA Adapter Backdoors: Attack Characterization and Behavioral Detection

We show that LoRA adapters, the dominant distribution format for fine-tuned LLMs, can be reliably backdoored through training data poisoning while preserving baseline task performance. On a Qwen 2.5 1.5B prompt-injection classifier, a small fraction of poisoned examples drives a...

DKD-KAN: A Lightweight Knowledge-Distilled KAN Intrusion Detection Framework, Based on MLP and KAN

Cyber-security systems often operate in resource-constrained environments, such as edge environments and real-time monitoring systems, where model size and inference time are crucial. A light-weight intrusion detection framework is proposed that utilizes the Kolmogorov-Arnold Network KAN to captu...

Smart Surveillance: Identifying IoT Device Behaviours Using ML-Powered Traffic Analysis

The proliferation of Internet of Things IoT devices has grown exponentially in recent years, introducing significant security challenges. Accurate identification of the types of IoT devices and their associated actions through network traffic analysis is essential to mitigate potential threats. B...

A Novel Study on Intelligent Methods and Explainable AI for Dynamic Malware Analysis

Deep learning models are one of the security strategies, trained on extensive datasets, and play a critical role in detecting and responding to these threats by recognizing complex patterns in malicious code. However, the opaque nature of these models-often described as "black boxes"-makes their...

EVA-S2PMLP: Secure and Scalable Two-Party MLP via Spatial Transformation

Whitepaper called EVA-S2PMLP: Secure and Scalable Two-Party MLP via Spatial Transformation...

Optimizing DDoS Detection in SDNs through Machine Learning Models

The emergence of Software-Defined Networking SDN has changed the network structure by separating the control plane from the data plane. However, this innovation has also increased susceptibility to DDoS attacks. Existing detection techniques are often ineffective due to data imbalance and accurac...

Performance of Machine Learning Classifiers for Anomaly Detection in Cyber Security Applications

This work empirically evaluates machine learning models on two imbalanced public datasets KDDCUP99 and Credit Card Fraud 2013. The method includes data preparation, model training, and evaluation, using an 80/20 train/test split. Models tested include eXtreme Gradient Boosting XGB, Multi Layer...

Silenzio: Secure Non-Interactive Outsourced MLP Training

Outsourcing the ML training to cloud providers presents a compelling opportunity for resource constrained clients, while it simultaneously bears inherent privacy risks, especially for highly sensitive training data. We introduce Silenzio, the first fully non-interactive outsourcing scheme for the...