Cyber Security Analytics

Learn about Hypothesis testing, the many Hypothesis testing Statistics, work with the Null Hypothesis & Alternative hypothesis and Types of hypothesis testing. Interpret the results of Hypothesis test and probabilities of Alpha error, understand Type I and Type II errors. Get introduced to Linear regression, various components of Linear regression viz regression line, Linear regression equation, the concept of Ordinary Least Square. Get introduced to Linear regression analysis, and Linear regression examples

Understand the Linear regression in a multivariate scenario, understand collinearity and how to deal with it. Get introduced to the analysis of Attribute Data, understand the principles of Logistic regression, Binary Logistic regression analysis. Learn about the Multiple Logistic regression, Probability measures, and its interpretation. Get clarity on the confusion matrix and its elements. Get introduced to “Cut off value” estimation using AUC and ROC curve, understand False Positive Rate, False Negative Rate, Sensitivity, Specificity. Gain a birds-eye view to various advanced regression techniques and analysis of count data namely Poisson regression, Negative binomial regression. Learn when to use Poisson regression and negative binomial regression for predicting count data.

Understand the various problems and challenges in the space of cybersecurity and how Machine Learning is shaping cybersecurity analytics. Understand the industry trends and opportunities. Learn how cyber security can be pivotal for the success of your organization.

Understand the applications of data science and predictive modelling in cyber security. This includes Spam Detection, Phishing Page Detection, Malware Detection, Anomaly Detection, Software Vulnerabilities etc.

Understand the overall flow of steps involved in Project management methodology for handling data related projects in Data Science, Machine Learning, Data Visualization, Artificial Intelligence, etc.

Understand about Malware detection and application of Kernel based method – SVM for Android Malware Detection. Also learn about application of ensemble techniques in detecting cyber-attacks.

Understand about lazy learning techniques such as KNN and application of the same in Biometrics including Brute Force Classification.

Learn about the various types of Adversarial attacks and applications of GANs in Image classification/misclassification as well as Malware detection. Also learn about application of AI in knocking down CAPTCHAs.

Understand about the various components and seasonal spikes in identifying DDoS (Distributed Denial-of-Service) attacks.

Understand about the various network anomaly detection algorithms & types of network attacks. Finally, one will learn to detect botnet topology.

Learn about application Regression based techniques – Logistic as well as Blackbox Technique – Support Vector Machines in detecting malicious URLs.

Understand about application of clustering – K-Means in detecting anomalies by using Windows event logs & Windows active directory data.

Ransomware, Spyware, Different types of viruses and its detection using AI applications. Malicious virus detection in databases as well as IoT sensors. Detecting hackers & impersonators will also be explained.