Life-long phishing attack detection using continual learning.

Phishing is an identity theft that employs social engineering methods to get confidential data from unwary users. A phisher frequently attempts to trick the victim into clicking a URL that leads to a malicious website. Many phishing attack victims lose their credentials and digital assets daily. This study demonstrates how the performance of traditional machine learning (ML)-based phishing detection models deteriorates over time. This failure is due to drastic changes in feature distributions caused by new phishing techniques and technological evolution over time. This paper explores continual learning (CL) techniques for sustained phishing detection performance over time. To demonstrate this behavior, we collect phishing and benign samples for three consecutive years from 2018 to 2020 and divide them into six datasets to evaluate traditional ML and proposed CL algorithms. We train a vanilla neural network (VNN) model in the CL fashion using deep feature embedding of HTML contents. We compare the proposed CL algorithms with the VNN model trained from scratch and with transfer learning (TL). We show that CL algorithms maintain accuracy over time with a tolerable deterioration of 2.45%. In contrast, VNN and TL-based models' performance deteriorates by over 20.65% and 8%, respectively.

An empirical evaluation of lightweight random walk based routing protocol in duty cycle aware wireless sensor networks.

Energy efficiency is an important design paradigm in Wireless Sensor Networks (WSNs) and its consumption in dynamic environment is even more critical. Duty cycling of sensor nodes is used to address the energy consumption problem. However, along with advantages, duty cycle aware networks introduce some complexities like synchronization and latency. Due to their inherent characteristics, many traditional routing protocols show low performance in densely deployed WSNs with duty cycle awareness, when sensor nodes are supposed to have high mobility. In this paper we first present a three messages exchange Lightweight Random Walk Routing (LRWR) protocol and then evaluate its performance in WSNs for routing low data rate packets. Through NS-2 based simulations, we examine the LRWR protocol by comparing it with DYMO, a widely used WSN protocol, in both static and dynamic environments with varying duty cycles, assuming the standard IEEE 802.15.4 in lower layers. Results for the three metrics, that is, reliability, end-to-end delay, and energy consumption, show that LRWR protocol outperforms DYMO in scalability, mobility, and robustness, showing this protocol as a suitable choice in low duty cycle and dense WSNs.