Kodiyilthekkadil's starred repositories
tg-amp-03-get-samples-add-to-scd
Get samples from Threat Grid and add the SHA256 to AMP Simple Custom Detection
APThreatDetectionSys
Advanced Persistent Threat /Intrusion Detection Sys
awesome-ml-for-threat-detection
A curated list of resources to deep dive into the intersection of applied machine learning and threat detection.
Cheat-Sheets
Cheat sheets for threat hunting, detection and other stuff.
threatintelligenceaggregator
Threat Intelligence Aggregator API example
BlackHatAsia2020
Adversary Detection Pipelines: Finally Making Your Threat Intel Useful -- BlackHat Asia 2020 Resources and References
Threat-Detection-and-Reporting-System
to identify the threats, or mishaps beforehand. To achieve this goal, we are currently detecting weapons, firearms, and any suspicious activities, apart from that if any problem occurs, it is going to try to detect if any casualties had happened, and is producing the alert.
iot-security-module-preview
Azure Security Center (ASC) provides threat detection capabilities for Azure RTOS devices
Attack-and-Anomaly-Detection-in-IoT-Sensors-in-IoT-Sites-Using-Machine-Learning-Approaches
Attack and Anomaly detection in the Internet of Things (IoT) infrastructure is a rising concern in the domain of IoT. With the increased use of IoT infrastructure in every domain, threats and attacks in these infrastructures are also growing commensurately. Denial of Service, Data Type Probing, Malicious Control, Malicious Operation, Scan, Spying and Wrong Setup are such attacks and anomalies which can cause an IoT system failure. In this paper, performances of several machine learning models have been compared to predict attacks and anomalies on the IoT systems accurately. The machine learning (ML) algorithms that have been used here are Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and Artificial Neural Network (ANN). The evaluation metrics used in the comparison of performance are accuracy, precision, recall, f1 score, and area under the Receiver Operating Characteristic Curve. The system obtained 99.4% test accuracy for Decision Tree, Random Forest, and ANN. Though these techniques have the same accuracy, other metrics prove that Random Forest performs comparatively better.
blue_team_detection
This repository is the home of threat hunting and security monitoring notebooks
detection-stack
Repo for multiformat signatures for threat detection
JamfProThreatHunting
Scripts to aid intrusion and malware detection using the Jamf Agent and Jamf Server
OSNThreatGroups
Threat Network Detection in Online Social Networks
inthreatDNS
A open-source local endpoint DNS threat detection system
CloudConstableThreatDetection
NLP Threat Detection for Cloud Constable
thremulation-station
Small-scale threat emulation and detection range built on Elastic and Atomic Redteam.
insider-threat
Prototype development for Insider Threat Detection and Assessment tools
Buried-threat-detection-using-AI-on-GPR-data
We, Achin and Harekrissna worked as a team to complete the project given to us on Buried threat detection using ground penetrating radar. We applied Deep Learning techniques specifically CNN and transfer learning along with image processing techniques like color thresholding, augmentation and masking to identify the threats hidden underground by analysing the radar data. We implemented the techniques given in the research paper (Some Good Practices for Applying Convolutional Neural Networks to Buried Threat Detection in Ground Penetrating Radar, by Daniël Reichman, Leslie M. Collins, Jordan M)
data-driven-intrusion-detection
Data-driven detection of cybersecurity threats in IoT networks.
dynamite-nsm
DynamiteNSM is a free Network Security Monitor developed by Dynamite Analytics to enable network visibility and advanced cyber threat detection
attackintel
A python script to query the MITRE ATT&CK API for tactics, techniques, mitigations, & detection methods for specific threat groups.