Exploiting Label Skews in Federated Learning with Model Concatenation

Federated Learning (FL) has emerged as a promising solution to perform deep learning on different data owners without exchanging raw data. However, non-IID data has been a key challenge in FL, which could significantly degrade the accuracy of the final model. Among different non-IID types, label skews have been challenging and common in image classification and other tasks. Instead of averaging the local models in most previous studies, we propose FedConcat, a simple and effective approach that concatenates these local models as the base of the global model to effectively aggregate the local knowledge. To reduce the size of the global model, we adopt the clustering technique to group the clients by their label distributions and collaboratively train a model inside each cluster. We theoretically analyze the advantage of concatenation over averaging by analyzing the information bottleneck of deep neural networks. Experimental results demonstrate that FedConcat achieves significantly higher accuracy than previous state-of-the-art FL methods in various heterogeneous label skew distribution settings and meanwhile has lower communication costs. Our code is publicly available at https://github.com/sjtudyq/FedConcat

TOWARDS VIDEO FINGERPRINTING ATTACKS OVER TOR

As web users resort to adopting encrypted networks like Tor to protect their anonymity online, adversaries find new ways to collect their private information. Since videos over the internet are a major source of recruitment, training, incitement to commit acts of terrorism, and more, this project envisions developing a machine learning algorithm that can help the Department of Defense find terrorists who take advantage of the dark web to help promote extremist ideology. This thesis describes the steps for training a machine learning classifier in a closed-world scenario to predict YouTube video patterns over an encrypted network like Tor. Our results suggest an adversary may predict the video that a user downloads over Tor with up to 92% accuracy, or may predict the length of a video with error as low as 5.3s. Similar to known website fingerprinting attacks, we show that Tor is susceptible to video fingerprinting, suggesting that Tor does not provide the level of anonymity as previously thought.Lieutenant Commander, United States NavyApproved for public release. Distribution is unlimited

Detecting Prominent Features and Classifying Network Traffic for Securing Internet of Things Based on Ensemble Methods

abstract: Rapid growth of internet and connected devices ranging from cloud systems to internet of things have raised critical concerns for securing these systems. In the recent past, security attacks on different kinds of devices have evolved in terms of complexity and diversity. One of the challenges is establishing secure communication in the network among various devices and systems. Despite being protected with authentication and encryption, the network still needs to be protected against cyber-attacks. For this, the network traffic has to be closely monitored and should detect anomalies and intrusions. Intrusion detection can be categorized as a network traffic classification problem in machine learning. Existing network traffic classification methods require a lot of training and data preprocessing, and this problem is more serious if the dataset size is huge. In addition, the machine learning and deep learning methods that have been used so far were trained on datasets that contain obsolete attacks. In this thesis, these problems are addressed by using ensemble methods applied on an up to date network attacks dataset. Ensemble methods use multiple learning algorithms to get better classification accuracy that could be obtained when the corresponding learning algorithm is applied alone. This dataset for network traffic classification has recent attack scenarios and contains over fifteen attacks. This approach shows that ensemble methods can be used to classify network traffic and detect intrusions with less training times of the model, and lesser pre-processing without feature selection. In addition, this thesis also shows that only with less than ten percent of the total features of input dataset will lead to similar accuracy that is achieved on whole dataset. This can heavily reduce the training times and classification duration in real-time scenarios.Dissertation/ThesisMasters Thesis Computer Science 201