Projects Current Projects

Dr. Tal Mizrahi, Eran Tavor

The Internet is a complex and dynamic network, whose performance is constantly changing depending on various factors, such as the nature of communication usage, malfunctions, cyber attacks, political events, and more. Measuring the performance of the internet network makes it possible to analyze its behavior and normality, and also makes it possible to learn about various events that affected the network's behavior. RIPE Atlas is a global network of thousands of probes that measure Internet connectivity and reachability in real-time. The probes are distributed worldwide and provide valuable data on network performance, stability, and security. Here are some of the types of information that can be extracted from RIPE Atlas: 1. Network performance: RIPE Atlas probes can measure key network performance metrics such as latency, packet loss, and jitter. This information can help network operators and researchers to identify and troubleshoot network problems, as well as to compare performance between different networks. 2. Internet reachability: RIPE Atlas probes can test the reachability of a website or other online service from different locations around the world. This can help website operators to identify and resolve connectivity issues that may be impacting their users. 3. DDoS attacks: RIPE Atlas can be used to detect and mitigate Distributed Denial of Service (DDoS) attacks. By monitoring traffic patterns and network performance, RIPE Atlas probes can help identify and respond to attacks in real-time. 4. DNS performance: RIPE Atlas includes a DNS monitoring service that measures the performance of DNS servers around the world. This can help DNS operators to identify and resolve performance issues that may be impacting the reliability and speed of their services. 5. Internet topology: RIPE Atlas probes can be used to map the topology of the Internet by measuring the paths that packets take between different network nodes. This information can be used to identify key bottlenecks and vulnerabilities in the network, as well as to develop more efficient routing algorithms. Overall, RIPE Atlas provides a wealth of information on the performance and security of the Internet, which can be valuable to network operators, researchers, and other stakeholders. The goal of the project Is to building a tool that will pull and analyze publicly available statistics (published by RIPE Atlas), as well as analyzing the statistics in order to identify unusual phenomena during the Corona crisis.

Eran Tavor

Binary exponential backoff (BEB) is a decades -old algorithm for coordinating access to a shared channel. In modern networks, BEB plays a crucial role in WiFi and other wireless communication standards. Despite this track record, well-known theoretical results indicate that under bursty traffic, BEB yields poor makespan, and superior algorithms are possible. In this project you will compare the performance of several backoff algorythms using the NS3 Network Simulator.

Eran Tavor

The QUIC protocol is a secure and encrypted transport protocol, characterized by multiplexing and low-latency capabilities. Its primary goal is to enhance the performance of HTTPS traffic. Recently standardized as RFC 9000, QUIC is poised to supplant TCP as the dominant transport protocol on the internet. The present research project entails a comparative analysis of TCP and QUIC, focusing on performance and security. The investigation will employ open-source implementations of both protocols.

Tom Azoulay, Eran Tavor

The aim of this project is to provide an accurate risk assessment of default for loans on Blockchain networks using solely past transactions of the user as a way to estimate.

Eran Tavor

As the demand for mobile high bandwidth – low latency is increasing, the access network must become smarter and automatically adapt to rapid changes. A scheduler is a module that observes the network, receive traffic requests from all users and allocates a bandwidth and a specific route to each requested connection. In order to accomplish the scheduling task quickly and efficiently, you will develop a Reinforcement Learning based scheduler that observes the network (the environment), receives traffic requests and allocates route and bandwidth to each traffic request. The main focus of this semester’s project is to develop an OMNET++ simulator that will be used as the digital twin of the real-world 6G access network and replace the Routenet model that was used in last semester project.

Barak Gahtan, Eran Tavor

Traffic classification, the process of categorizing network traffic into various classes, is essential for various purposes such as quality of service control, pricing, resource planning, malware detection, intrusion detection and more. In recent years, Deep Learning (DL) has emerged as a state-of-the-art technique for traffic classification due to its ability to automatically select features through training and its high capacity for learning complex patterns. DL can learn the nonlinear relationship between raw input and corresponding output without the need for feature selection and classification, but it requires a large amount of labeled data and computational power. QUIC is a transport protocol designed to improve the performance of encrypted HTTP traffic. It is a secure, highly encrypted, multiplexed protocol that encrypts both the payload and most of the header. Identifying the upper layer application that used the QUIC transport layer is useful for load balancing, Quality of Experience (QOE), Cyber attack mitigation etc. The goal of this project is to train a model to classify QUIC connection to several classes of upper-layer applications.