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anomaly-detection-by-netflow-and-dns-analysis-of-alexa-1m-websites

The Domain Name System is a fundamental component of the internet since it maps the easy-to-remember domain names to IP addresses. Therefore, it is usually the primary target for most of the malicious attacks such as DNS Poisoning and Rogue DNS servers. With the help of 0x20 bit encoding, the problem of DNS Poisoning is mitigated to quite a large extent. Although, it has a minor requirement that the authoritative nameserver should be able to preserve the case of the DNS query. It is usually difficult to detect the rogue DNS server above the stub resolver. We propose an anomaly detection system which would be able to raise a red flag in case of DNS Poisoning and malicious DNS authority by passive DNS analysis of domain names and then comparing them with the 0th day cluster of the database. We perform the passive DNS analysis for 27 days by querying the whois server of CYMRU, compare the network profiles of the domain names crawled with the 0th day cluster and categorize the domain names as static, benign anomaly and malicious anomaly depending upon the cluster a domain name shifted. In the process, we also create a WHOIS repository for Alexa domain names which is faster to query than the WHOIS server.

callow

Dead simple brute force tool for website login forms

django-tutorial-blog

A simple blogging engine written using Django for a tutorial on my website

honbot-django

HoN Stats website. DJANGOOOOOOOO

movdut

newsworker

Advanced news feeds extractor and finder library. Helps to automatically extract news from websites without RSS/ATOM feeds

palanaeum

A website for collecting audio interviews and transcribing the questions & answers

risjbot

A scrapy project to extract the text and metadata of articles from news websites

ubuntu-mate.boutique

The website for Ubuntu MATE online shop.

wfes

A framework for evaluating Website Fingerprinting attacks/defences, accompaining the paper "Bayes, not Naïve: Security Bounds on Website Fingerprinting Defenses" (Cherubin, 2017)