Projects Current Projects

Please, see the list of LCCN offered projects:

Project Title:

Semi Line Rate Deep Learning Traffic Classifier Using NVIDIA Programmable switch

Supervisors:
Idan Barnea, Matty Kadosh, Aviel Glam, Eran Tavor
Description:
In this project the students will use NVIDIA (Mellanox) programmable switch and Deep Learning methods to classify network traffic at semi-line rate. The students will use cutting-edge technology: data plane programming using P4. The main effort in this project will be to leverage NVIDIA's technology and an existing AI model to do semi line rate classification of encrypted traffic.
Picture of Semi Line Rate Deep Learning Traffic Classifier Using NVIDIA Programmable switch Project
 
Project Title:

Ethereum Blockchain Mempool Activity Recorder

Supervisors:
Avi Mizrahi, Eran Tavor
Description:
The popular cryptocurrency Bitcoin attracts attention to its underlying technology, the Blockchain. Blockchain systems are databases based on chain of blocks, and each block is composed of set of transactions. To form a new block at the top of the chain, nodes in the system share new transactions between them, until one of them can pack a set into new block. For that, nodes in the network maintain a local pool of pending transactions, usually called ‘mempool’. Mempool synchronization takes major part of the network bandwidth, hence it is valuable to find an efficient algorithm for it. The purpose of the project is to generate a dataset for transaction arrival times for at least two nodes and analyze it.
Picture of Ethereum Blockchain Mempool Activity Recorder Project
 
Project Title:

QUIC SPIN Bit implementation and Analysis

Supervisors:
Eran Tavor
Description:
QUIC is a secure general-purpose, encrypted, multiplexed, and low-latency transport protocol designed from the ground up to improve transport performance for HTTPS traffic. QUIC has recently (May 2021) became RFC standard (RFC 9000) and is expected to become the dominant transport protocol in the Internet over TCP. Most of QUIC packets are encrypted. Not only the payload is encrypted but also most of the header. This situation makes it very difficult for the ISP (Internet Service Provider) or any other middlebox to monitor the network and enforce regulatory measures to keep the network up and running. Important parameters for ISPs to monitor network health RTT (Round Trip Time) and packet Loss Rate estimations . When RTT or Loss Rate get too high it may indicate that queues are being build-up and an the network may be close to a collapse. In such situations the ISP may limit heavy users and keep the network alive. Therefore, an efficient estimations of RTT and Loss Rate are important to en efficient network management. The purpose of the project is to set an observer that estimates the RTT and the Loss Rate of connections by identify a connection and monitoring dedicated bits of the headers.
Picture of QUIC SPIN Bit implementation and Analysis Project
 
Project Title:

Graph Neural Networks for Service Assignment

Supervisors:
Dor Harris, Eran Tavor
Description:
Graph Neural Networks (GNN) is a novel approach and a class of deep learning methods designed to perform inference on data described by graphs. These are neural networks that can be directly applied to graphs, and provide an easy way to do node-level, edge-level, and graph-level prediction tasks. In this project you will use GNN in order to solve a graph theory problem that have a strong connection to realistic problems, you will need to learn how to use GNNs and implement such network that apply to the problem that will be specifically presented to the students that do the project.
Picture of Graph Neural Networks for Service Assignment Project
 
Project Title:

RouteNet - Deep Learning Network Performance Estimation Tool

Supervisors:
Barak Gahten, Eran Tavor
Description:
RouteNet is an innovative network performance prediction tool. It is a novel network model based on Graph Neural Network (GNN) that is able to understand the complex relationship between topology, routing, and input traffic to produce accurate estimates of the per-source/destination per-packet delay distribution and loss. The purpose of this project is to validate the RouteNet tool and adapt it to new networks.
Picture of RouteNet - Deep Learning Network Performance Estimation Tool Project
 
Project Title:

Wifi / BT packet analyzer

Supervisors:
Eran Tavor
Description:
Wifi and Bluetooth devices are everywhere: Laptops, Tablets, Watches, Security cameras, Smart-House devices, Vehicles, Public and non-Public hotspots … The purpose of this project is to build a Wifi and BlueTooth packet analyzer and explore the information that can be legally collected by walking around in public (Haifa/ Technion/…). The students will use RaspberryPi 4 or ESP32 and open-source software tools to build a MAC sniffer.
Picture of Wifi / BT packet analyzer Project
 
Project Title:

RaspberryPi4 IOT Switch

Supervisors:
Eran Tavor
Description:
P4PI is P4 (Data plain programming language) on Raspberry PI SBC (Single Board Computer used in many implementations including IOT). P4PI is a small scale functional programmable switch/Router based on open source code (T4P4S P4 Complier) and open source hardware. In this project the students will develope a private encrypted network over the P4PI infrastructure.
Picture of RaspberryPi4 IOT Switch Project
 
Project Title:

QUIC Sniffer - Efficient Cardinality Estimator

Supervisors:
Eran Tavor
Description:
QUIC is a secure general-purpose, encrypted, multiplexed, and low-latency transport protocol designed from the ground up to improve transport performance for HTTPS traffic. QUIC has recently (May 2021) became RFC standard (RFC 9000) and is expected to become the dominant transport protocol in the Internet over TCP. In this project the student will use a sniffer implemented in a preveous project, implement an efficient cardinality estimator of QUIC connections (how many different QUIC connections can be observed) and demonstrate the sniffer in the lab.
Picture of QUIC Sniffer - Efficient Cardinality Estimator Project
 
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