The Address Resolution Protocol (ARP) plays a fundamental role in mapping IP addresses to MAC addresses within local area networks, yet it remains inherently vulnerable to spoofing attacks due to its lack of built-in authentication mechanisms. This weakness allows adversaries to intercept, manipulate, or redirect network traffic, posing a significant threat to network integrity, particularly on resource-constrained routers where conventional security solutions are often impractical to deploy. To address this gap, this paper proposes a lightweight ARP spoofing detection method based on entropy analysis, combined with a percentile-based dynamic threshold to adapt to varying network conditions without requiring manual calibration. The proposed method was implemented and evaluated on OpenWRT routers, a common platform for low-cost and embedded networking devices, to assess its practicality in real-world resource-constrained environments. Experimental results demonstrate that the method successfully detects eight out of ten simulated ARP spoofing scenarios, indicating a strong overall detection capability. Furthermore, resource consumption analysis shows that the approach introduces minimal system overhead, with only a 1% increase in memory usage, a 6% increase in disk usage, and CPU load consistently remaining below 0.02. These findings confirm that the proposed entropy-based detection method achieves an effective balance between detection accuracy and computational efficiency, making it a viable and scalable solution for real-time ARP spoofing detection in resource-constrained LAN environments such as small offices, home networks, and IoT deployments.
Octlivatua Patricia Disiulina, Vera Suryani· International Conference on...· 0 citations
Federated learning allows financial institutions to collaboratively identify fraud without distributing raw transaction data, while differential privacy safeguards individual records from inference attacks. Utilizing a lightweight four-layer neural network that was trained on a 10,000-sample subset of the PaySim mobile money dataset (originally 6.3 million transactions), this paper provides a systematic empirical evaluation of a differentially private federated fraud detection framework. The framework is evaluated in the context of varying data distributions (IID and multiple non-IID schemes), clients' numbers, participation rates, privacy budgets, and data quantity skews. The findings indicate that IID and moderately heterogeneous settings produce high accuracy and F1-scores. Conversely, performance is substantially undermined by severe non-IID partitions, numerous small clients, and extreme data imbalances, particularly when differential privacy noise is implemented. The non-private model's detection capability is largely preserved by intermediate privacy budgets (approximately ε≈1). Notably, membership inference attack success rates remain at or near random guessing (approximately 50% accuracy) in the absence of differential privacy, indicating that the baseline provides a restricted attack surface. In this evaluation setting, the privacy benefit is further confirmed by the fact that differential privacy at ε≈1 further suppresses the attack advantage toward zero. While these results provide practical configuration advice for federated, privacy-preserving fraud detection systems, they should be interpreted within the context of the simplified experimental setup that was implemented.
Gian Maxmillian Firdaus, M. Abdurohman, B. Erfianto et al.· International Conference on...· 0 citations
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