8 research outputs found
Ransomware Detection Using Federated Learning with Imbalanced Datasets
Ransomware is a type of malware which encrypts user data and extorts payments
in return for the decryption keys. This cyberthreat is one of the most serious
challenges facing organizations today and has already caused immense financial
damage. As a result, many researchers have been developing techniques to
counter ransomware. Recently, the federated learning (FL) approach has also
been applied for ransomware analysis, allowing corporations to achieve
scalable, effective detection and attribution without having to share their
private data. However, in reality there is much variation in the quantity and
composition of ransomware data collected across multiple FL client
sites/regions. This imbalance will inevitably degrade the effectiveness of any
defense mechanisms. To address this concern, a modified FL scheme is proposed
using a weighted cross-entropy loss function approach to mitigate dataset
imbalance. A detailed performance evaluation study is then presented for the
case of static analysis using the latest Windows-based ransomware families. The
findings confirm improved ML classifier performance for a highly imbalanced
dataset.Comment: 6 pages, 4 figures, 3 table
Recommended from our members
Location-Dependent Cobalt Deposition in Smartphone Cells upon Long-Term Fast-Charging Visualized by Synchrotron X-ray Fluorescence
In this work, we investigate the transition-metal dissolution of the layered cathode material LiCoO2 upon repeated fast-charging of three smartphone batteries from different manufacturers using synchrotron micro X-ray fluorescence (μ-XRF). Using this spatially resolved technique, dissolution of Co and subsequent location-dependent deposition on the anode are observed. μ-XRF mapping of selected parts of the anode electrode sheets, such as electrode folds and edges of the jelly roll, reveals the difference in the way Co is deposited on specific regions of the anode electrode. While some folds show no depositions, edges of the anode show gradually accumulating Co depositions. Careful quantification of the dissolved Co reveals that the capacity loss scales with the amount of deposited Co on the anode, that is, total Co loss from within the cathode. Soft X-ray absorption spectroscopy of the Co depositions on the anode shows that Co is mainly deposited in a reduced 2+ state. While optimization of the fast-charging protocol mitigates Li plating on the anode, no significant difference in the amount of deposited Co can be observed between an optimized and a nonoptimized fast-charging algorithm