Pinned Repositories
-NETWORKING-NETWORK-INTRUSION-DETECTION-
BUSINESS CONTEXT: With the enormous growth of computer networks usage and the huge increase in the number of applications running on top of it, network security is becoming increasingly more important. All the computer systems suffer from security vulnerabilities which are both technically difficult and economically costly to be solved by the manufacturers. Therefore, the role of Intrusion Detection Systems (IDSs), as special-purpose devices to detect anomalies and attacks in the network, is becoming more important. The research in the intrusion detection field has been mostly focused on anomaly-based and misusebased detection techniques for a long time. While misuse-based detection is generally favored in commercial products due to its predictability and high accuracy, in academic research anomaly detection is typically conceived as a more powerful method due to its theoretical potential for addressing novel attacks. Conducting a thorough analysis of the recent research trend in anomaly detection, one will encounter several machine learning methods reported to have a very high detection rate of 98% while keeping the false alarm rate at 1%. However, when we look at the state of the art IDS solutions and commercial tools, there is no evidence of using anomaly detection approaches, and practitioners still think that it is an immature technology. To find the reason of this contrast, lots of research was done done in anomaly detection and considered various aspects such as learning and detection approaches, training data sets, testing data sets, and evaluation methods. BUSINESS PROBLEM: Your task to build network intrusion detection system to detect anamolies and attacks in the network. There are two problems. 1. Binomial Classification: Activity is normal or attack 2. Multinomial classification: Activity is normal or DOS or PROBE or R2L or U2R
ableplayer
fully accessible cross-browser HTML5 media player.
AFLplusplus
afl++ is afl 2.56b with community patches, AFLfast power schedules, qemu 3.1 upgrade + laf-intel support, MOpt mutators, InsTrim instrumentation, unicorn_mode and a lot more!
algorithms
This repository is not maintained
avatar-python
Dynamic security analysis of embedded systems’ firmwares
Awesome-Fuzzing
A curated list of fuzzing resources ( Books, courses - free and paid, videos, tools, tutorials and vulnerable applications to practice on ) for learning Fuzzing and initial phases of Exploit Development like root cause analysis.
awesome-python
A curated list of awesome Python frameworks, libraries and software
boofuzz
A fork and successor of the Sulley Fuzzing Framework
BoopSuite
A Suite of Tools written in Python for wireless auditing and security testing.
BrundleFuzz
BrundleFuzz is a distributed fuzzer for Windows and Linux using dynamic binary instrumentation.
Eagle1707's Repositories
Eagle1707/algorithms
This repository is not maintained
Eagle1707/BoopSuite
A Suite of Tools written in Python for wireless auditing and security testing.
Eagle1707/cbook
the book of c
Eagle1707/cve-analysis
Tools for conducting analysis of CVE data in Elasticsearch
Eagle1707/DECAF
DECAF (short for Dynamic Executable Code Analysis Framework) is a binary analysis platform based on QEMU. This is also the home of the DroidScope dynamic Android malware analysis platform. DroidScope is now an extension to DECAF.
Eagle1707/fb-messenger-bot
A Facebook + Reddit bot
Eagle1707/firmadyne
Platform for emulation and dynamic analysis of Linux-based firmware
Eagle1707/FirmAFL
FIRM-AFL is the first high-throughput greybox fuzzer for IoT firmware.
Eagle1707/fuzzilli
A JavaScript Engine Fuzzer
Eagle1707/fuzzinator
Fuzzinator Random Testing Framework
Eagle1707/FuzzingPaper
Recent Fuzzing Paper
Eagle1707/fuzzowski
the Network Protocol Fuzzer that we will want to use.
Eagle1707/Gource
software version control visualization
Eagle1707/honggfuzz
Security oriented fuzzer with powerful analysis options. Supports evolutionary, feedback-driven fuzzing based on code coverage (software- and hardware-based)
Eagle1707/inception
Inception is a physical memory manipulation and hacking tool exploiting PCI-based DMA. The tool can attack over FireWire, Thunderbolt, ExpressCard, PC Card and any other PCI/PCIe interfaces.
Eagle1707/killerbeez
A distributed fuzzer which aims to pull in the best technologies, make them play nicely together, and run on multiple O/Ses.
Eagle1707/kitty
Fuzzing framework written in python
Eagle1707/ktrw
An iOS kernel debugger based on a KTRR bypass for A11 iPhones that works with LLDB.
Eagle1707/morph
An open source fuzzing framework for fun.
Eagle1707/netzob
Netzob: Protocol Reverse Engineering, Modeling and Fuzzing
Eagle1707/pulsar
Protocol Learning and Stateful Fuzzing
Eagle1707/python-afl
American Fuzzy Lop fork server and instrumentation for pure-Python code
Eagle1707/python-github-projects
Collect and classify python projects on Github (deprecated)
Eagle1707/radamsa
a general-purpose fuzzer
Eagle1707/root
The official repository for ROOT: analyzing, storing and visualizing big data
Eagle1707/scraper
Firmware scraper
Eagle1707/talks_odt
Slides and materials for most of my talks by year
Eagle1707/TriforceAFL
AFL/QEMU fuzzing with full-system emulation.
Eagle1707/wifi-project
Eagle1707/zzuf
🔀 Application fuzzer