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/-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
Eagle1707/ableplayer
fully accessible cross-browser HTML5 media player.
Eagle1707/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!
Eagle1707/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.
Eagle1707/boofuzz
A fork and successor of the Sulley Fuzzing Framework
Eagle1707/BTLE
Bluetooth Low Energy (BLE) packet sniffer and generator for both standard and non standard (raw bit).
Eagle1707/cleverhans
An adversarial example library for constructing attacks, building defenses, and benchmarking both
Eagle1707/clusterfuzz
Scalable fuzzing infrastructure.
Eagle1707/connectedhomeip
Matter (formerly Project CHIP) is creating more connections between more objects, simplifying development for manufacturers and increasing compatibility for consumers, guided by the Connectivity Standards Alliance (formerly Zigbee Alliance).
Eagle1707/covid-19-data
Data on COVID-19 (coronavirus) cases, deaths, hospitalizations, tests • All countries • Updated daily by Our World in Data
Eagle1707/Decept
Decept Network Protocol Proxy
Eagle1707/Files
A modern file explorer that pushes the boundaries of the platform.
Eagle1707/Fuzzing-Survey
The Art, Science, and Engineering of Fuzzing: A Survey
Eagle1707/fuzzingbook
Project page for "The Fuzzing Book"
Eagle1707/hackrf-nightly
Nightly build repository for HackRF - builds everything pushed to mossmann/hackrf master branch.
Eagle1707/intermediatePython
Eagle1707/IoTBench-test-suite
A micro-benchmark suite to assess the effectiveness of tools designed for IoT apps
Eagle1707/IoTSecurity101
A Curated list of IoT Security Resources
Eagle1707/iSniff-GPS
Passive sniffing tool for capturing and visualising WiFi location data disclosed by iOS devices
Eagle1707/LaTeX-examples
Examples for the usage of LaTeX
Eagle1707/mutiny-fuzzer
Eagle1707/OvenPlayer
OvenPlayer is Open-Source HTML5 Player. OvenPlayer supports WebRTC Signaling from OvenMediaEngine for Sub-Second Latency Streaming.
Eagle1707/python-patterns
A collection of design patterns/idioms in Python
Eagle1707/PyZwaver
Z-Wave library written in Python3
Eagle1707/RFCrack
A Software Defined Radio Attack Tool
Eagle1707/sec-deadlines.github.io
Deadline countdowns for academic conferences in Security and Privacy
Eagle1707/SmartThingsPublic
SmartThings open-source DeviceTypeHandlers and SmartApps code
Eagle1707/thepiratebay
:skull: The Pirate Bay node.js client
Eagle1707/VFuzz-public
Eagle1707/zwavejs2mqtt
Zwave to Mqtt gateway and Control Panel Web UI. Built using Nodejs, and Vue/Vuetify