7,806 research outputs found
A traffic classification method using machine learning algorithm
Applying concepts of attack investigation in IT industry, this idea has been developed to design
a Traffic Classification Method using Data Mining techniques at the intersection of Machine
Learning Algorithm, Which will classify the normal and malicious traffic. This classification will
help to learn about the unknown attacks faced by IT industry. The notion of traffic classification
is not a new concept; plenty of work has been done to classify the network traffic for
heterogeneous application nowadays. Existing techniques such as (payload based, port based
and statistical based) have their own pros and cons which will be discussed in this
literature later, but classification using Machine Learning techniques is still an open field to explore and has provided very promising results up till now
k-fingerprinting: a Robust Scalable Website Fingerprinting Technique
Website fingerprinting enables an attacker to infer which web page a client
is browsing through encrypted or anonymized network connections. We present a
new website fingerprinting technique based on random decision forests and
evaluate performance over standard web pages as well as Tor hidden services, on
a larger scale than previous works. Our technique, k-fingerprinting, performs
better than current state-of-the-art attacks even against website
fingerprinting defenses, and we show that it is possible to launch a website
fingerprinting attack in the face of a large amount of noisy data. We can
correctly determine which of 30 monitored hidden services a client is visiting
with 85% true positive rate (TPR), a false positive rate (FPR) as low as 0.02%,
from a world size of 100,000 unmonitored web pages. We further show that error
rates vary widely between web resources, and thus some patterns of use will be
predictably more vulnerable to attack than others.Comment: 17 page
Traffic Verification for Network Anomaly Detection in Sensor Networks
AbstractThe traffic that is being injected to the network is increasing every day. It can be either normal or anomalous. Anomalous traffic is variation in the communication pattern from the normal one and hence anomaly detection is an important procedure in ensuring network resiliency. Probabilistic models can be used to model traffic for anomaly detection. In this paper, we use Gaussian Mixture Model for traffic verification. The traffic is captured and is given to the model to verification. Traffic which obeys the model is normal and those which disobey are anomalies. Analysis shows that the proposed system has better performance in terms of delay, throughput and packet delivery rati
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