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ai-in-cyber-security

AI in Cyber Security - SVR, Time series plotting, captcha recognition, various cyber attack like DDOS prevention methods.

cyber-attack-prediction

Modern water distribution systems rely on computers, sensors and actuators for both monitoring and operational purposes. This combination of physical processes and embedded systems—cyber-physical systems, in short—improves the level of service of water distribution networks but exposes them to the potential threats of cyber attacks. We will be using statistical models to attempt to detect cyber attacks.

cyber-physical-attack-defense-system

Q-learning Algorithm for Detection and Defense Tasks in a Smart Power System

ddos

Implementation of Clustering Algorithm for detecting DDOS Attack based on paper : https://github.com/pranishd1/ddos/blob/master/IJRES-V1I2P102.pdf

ddos-ml-detection

Long Short-term Memory, Recurrent Neural Network method was used to detect the DDoS attack

ddos_cyberthreat_detection_ai_ml_algorithms

DDoS attack analysis using Machine Learning

deep-learning-4-ids

Modern and future vehicles are complex cyber-physical sys-tems. The connection to their outside environment raises many securityproblems that impact our safety directly. In this work, we propose a DeepCAN intrusion detection system framework. We propose a multivariatetime series representation for asynchronous CAN data. This represen-tation enhances the temporal modelling of deep learning architecturesfor anomaly detection. We study different deep learning tasks (super-vised/unsupervised) and compare different architectures, to propose anin-vehicle intrusion detection system that fits constraints of memory andcomputational power of the in-vehicle system. The proposed intrusiondetection system is time window wise: any given time frame is labelledeither anomalous or normal. We conduct experiments with many types ofattacks on an in-vehicle CAN using SynCAn dataset. We show that oursystem yields good results and allow to detect different kinds of attacks.

epanetcpa

epanetCPA is a MATLAB® toolbox for assessing the impacts of cyber-physical attacks on water distribution systems

iot-cyber-security-with-machine-learning

IoT networks have become an increasingly valuable target of malicious attacks due to the increased amount of valuable user data they contain. In response, network intrusion detection systems have been developed to detect suspicious network activity. UNSW-NB15 is an IoT-based network traffic data set with different categories for normal activities and malicious attack behaviors. UNSW-NB15 botnet datasets with IoT sensors' data are used to obtain results that show that the proposed features have the potential characteristics of identifying and classifying normal and malicious activity. Role of ML algorithms is for developing a network forensic system based on network flow identifiers and features that can track suspicious activities of botnets is possible. The ML model metrics using the UNSW-NB15 dataset revealed that ML techniques with flow identifiers can effectively and efficiently detect botnets’ attacks and their tracks.

ml-based-network-intrusion-detection-using-cyber-dataset-cse-cic-ids2018-to-classify-network-attacks

novahackathon

Using AI to detect cyber attacks

phishing-website-detection

It is a project of detecting phishing websites which are main cause of cyber security attacks. It is done using Machine learning with Python

phishing-website-detection-using-machine-learning-techniques

To develop a new machine learning algorithm to detect and classify phishing websites. Phishing is the most commonly used social engineering and cyber attack.

phr-model

Prepare, Hunt, and Respond - Conceptual model against cyber attacks by JYVSECTEC

predicting-of-cyber-attacks-based-on-graphs-and-time-series-using-tweets

researchlstm

Multivariate Industrial Time Series with Cyber-Attack Simulation: Fault Detection Using an LSTM-based Predictive Data Model

syncan