Challenges in Net Neutrality Violation Detection: A Case Study of Wehe
Tool and Improvements
- URL: http://arxiv.org/abs/2102.04196v2
- Date: Sun, 24 Oct 2021 11:24:52 GMT
- Title: Challenges in Net Neutrality Violation Detection: A Case Study of Wehe
Tool and Improvements
- Authors: Vinod S. Khandkar and Manjesh K. Hanawal
- Abstract summary: We focus on Wehe,' the most recent tool developed to detect net-neutrality violations.
We highlight critical weaknesses in Wehe where its replay traffic is not being correctly classified as intended services.
We propose a new method in which the SNI parameter is set appropriately in the initial TLS handshake.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We consider the problem of detecting deliberate traffic discrimination on the
Internet. Given the complex nature of the Internet, detection of deliberate
discrimination is not easy to detect, and tools developed so far suffer from
various limitations. We study challenges in detecting the violations (focusing
on the HTTPS traffic) and discuss possible mitigation approaches. We focus on
`Wehe,' the most recent tool developed to detect net-neutrality violations.
Wehe hosts traffic from all services of interest in a common server and replays
them to mimic the behavior of the traffic from original servers. Despite Wehe's
vast utility and possible influences over policy decisions, its mechanisms are
not yet validated by others. In this work, we highlight critical weaknesses in
Wehe where its replay traffic is not being correctly classified as intended
services by the network middleboxes. We validate this observation using a
commercial traffic shaper. We propose a new method in which the SNI parameter
is set appropriately in the initial TLS handshake to overcome this weakness.
Using commercial traffic shapers, we validate that SNI makes the replay traffic
gets correctly classified as the intended traffic by the middleboxes. Our new
approach thus provides a more realistic method for detecting neutrality
violations of HTTPS traffic.
Related papers
- Detection of Malicious DNS-over-HTTPS Traffic: An Anomaly Detection Approach using Autoencoders [0.0]
We design an autoencoder that is capable of detecting malicious DNS traffic by only observing the encrypted DoH traffic.
We find that our proposed autoencoder achieves the highest detection performance, with a median F-1 score of 99% over several types of malicious traffic.
arXiv Detail & Related papers (2023-10-17T15:03:37Z) - DARTH: Holistic Test-time Adaptation for Multiple Object Tracking [87.72019733473562]
Multiple object tracking (MOT) is a fundamental component of perception systems for autonomous driving.
Despite the urge of safety in driving systems, no solution to the MOT adaptation problem to domain shift in test-time conditions has ever been proposed.
We introduce DARTH, a holistic test-time adaptation framework for MOT.
arXiv Detail & Related papers (2023-10-03T10:10:42Z) - Transaction Fraud Detection via an Adaptive Graph Neural Network [64.9428588496749]
We propose an Adaptive Sampling and Aggregation-based Graph Neural Network (ASA-GNN) that learns discriminative representations to improve the performance of transaction fraud detection.
A neighbor sampling strategy is performed to filter noisy nodes and supplement information for fraudulent nodes.
Experiments on three real financial datasets demonstrate that the proposed method ASA-GNN outperforms state-of-the-art ones.
arXiv Detail & Related papers (2023-07-11T07:48:39Z) - Classification and Explanation of Distributed Denial-of-Service (DDoS)
Attack Detection using Machine Learning and Shapley Additive Explanation
(SHAP) Methods [4.899818550820576]
Distinguishing between legitimate traffic and malicious traffic is a challenging task.
An inter-model explanation implemented to classify a traffic flow whether is benign or malicious is an important investigation of the inner working theory of the model.
We propose a framework that can not only classify legitimate traffic and malicious traffic of DDoS attacks but also use SHAP to explain the decision-making of the model.
arXiv Detail & Related papers (2023-06-27T04:51:29Z) - Using EBGAN for Anomaly Intrusion Detection [13.155954231596434]
We propose an EBGAN-based intrusion detection method, IDS-EBGAN, that classifies network records as normal traffic or malicious traffic.
The generator in IDS-EBGAN is responsible for converting the original malicious network traffic in the training set into adversarial malicious examples.
During testing, IDS-EBGAN uses reconstruction error of discriminator to classify traffic records.
arXiv Detail & Related papers (2022-06-21T13:49:34Z) - Machine Learning for Encrypted Malicious Traffic Detection: Approaches,
Datasets and Comparative Study [6.267890584151111]
In post-COVID-19 environment, malicious traffic encryption is growing rapidly.
We formulate a universal framework of machine learning based encrypted malicious traffic detection techniques.
We implement and compare 10 encrypted malicious traffic detection algorithms.
arXiv Detail & Related papers (2022-03-17T14:00:55Z) - Road Network Guided Fine-Grained Urban Traffic Flow Inference [108.64631590347352]
Accurate inference of fine-grained traffic flow from coarse-grained one is an emerging yet crucial problem.
We propose a novel Road-Aware Traffic Flow Magnifier (RATFM) that exploits the prior knowledge of road networks.
Our method can generate high-quality fine-grained traffic flow maps.
arXiv Detail & Related papers (2021-09-29T07:51:49Z) - DoS and DDoS Mitigation Using Variational Autoencoders [15.23225419183423]
We explore the potential of Variational Autoencoders to serve as a component within an intelligent security solution.
Two methods based on the ability of Variational Autoencoders to learn latent representations from network traffic flows are proposed.
arXiv Detail & Related papers (2021-05-14T15:38:40Z) - End-to-End Intersection Handling using Multi-Agent Deep Reinforcement
Learning [63.56464608571663]
Navigating through intersections is one of the main challenging tasks for an autonomous vehicle.
In this work, we focus on the implementation of a system able to navigate through intersections where only traffic signs are provided.
We propose a multi-agent system using a continuous, model-free Deep Reinforcement Learning algorithm used to train a neural network for predicting both the acceleration and the steering angle at each time step.
arXiv Detail & Related papers (2021-04-28T07:54:40Z) - Cooling-Shrinking Attack: Blinding the Tracker with Imperceptible Noises [87.53808756910452]
A cooling-shrinking attack method is proposed to deceive state-of-the-art SiameseRPN-based trackers.
Our method has good transferability and is able to deceive other top-performance trackers such as DaSiamRPN, DaSiamRPN-UpdateNet, and DiMP.
arXiv Detail & Related papers (2020-03-21T07:13:40Z) - Key Points Estimation and Point Instance Segmentation Approach for Lane
Detection [65.37887088194022]
We propose a traffic line detection method called Point Instance Network (PINet)
The PINet includes several stacked hourglass networks that are trained simultaneously.
The PINet achieves competitive accuracy and false positive on the TuSimple and Culane datasets.
arXiv Detail & Related papers (2020-02-16T15:51:30Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.