Manav Rachna Edge https://validator.w3.org/feed/docs/rss2.html Certification in Data Intelligence Batch 3 (JULY 26) Certification in Data Intelligence Batch 3 (JULY 26) Certificate in Cyber Security – Batch 1 (JULY 26) 3.4 Digital Signature Concept; Compare Digital Signature with Public Key Cryptography, Digital Signature Scheme 3.5 Hash functions Properties; Algorithm: MDC, MAC, HMAC, MD5, and SHA 512 Cart Checkout 3.6 SSL, IPSec, E-mail Security–PGP, Email attacks 3.7 Security requirements of database, Security in operating systems: Operating System Structure 3.8 Operating System Tools to Implement Security Functions and Rootkit: Phone Rootkit, Sony XCP Rootkit, TLBSS Rootkits 3.3 Biometric Authentication and Third Party Authentication using KDCs and Kerberos Version 5 3.2 Modes of Operation of Block Ciphers: ECB, CBC, OFB, CTR, DES, RSA 3.1 Concept of symmetric and asymmetric cryptography 2.5 Session Hijacking and Broken access control 2.6 Web Application Security Testing – WSDL, SOAP and REST 2.7 Mobile and Cloud web application security 2.8 Internet Protocol and Web Security Protocols 2.4 Authentication and Authorization vulnerabilities 2.3 Common web vulnerabilities like LDAP, XSS, CSRF 2.2 Server- side vs Client-side programming 2.1 Introduction to web application security and Threat Modelling 1.8 SQL Injection, Buffer Overflow, Attack on wireless Networks and Website Attacks 1.7 Password Cracking, Keyloggers and Spyware, Trojan and backdoors 1.6 Virtual Private Network, Linux Firewall and Windows Firewall 1.5 Scanning for web vulnerability, Software vulnerabilities, System scanning and MS17-010 Vulnerability 1.4 Reverse TCP connection and Android Hacking using Reverse TCP connection 1.3 Digital Forensics, Contemporary Crimes and Indian IT ACT 2000 1.2 Cyber Crimes and Types of Cybercrime 1.1 Information Gathering: Passive Information Gathering, Active Information Gathering 3.5 Decision Trees and Ensemble Models (Bagging and Boosting) 3.2 Reinforcement Learning and Dimensionality Reduction 3.3 Principal Component Analysis (PCA) 3.4 Generative Models: Linear Discriminative Analysis and Naïve Bayes Classifier 3.6 Graphical Models and Evaluation Measures 3.7 Applications of Ensemble Methods in Supervised and Unsupervised Learning MODULE 3 – QUIZ 3.1 Clustering, Gaussian Mixture Models, and Spectral Clustering MODULE 2 – QUIZ 2.9 Decision Trees, Hypothesis Testing, and Ensemble Methods 2.8 Parameter Estimation (Maximum Likelihood and Bayesian) 2.7 Neural Networks: Early Models, Backpropagation, Training & Validation 2.6 Linear Discriminant Analysis Optimization and Separating Hyperplanes Classification 2.5 Principal Components Regression, Linear Classification, and Logistic Regression 2.4 Dimensionality Reduction, Subset Selection, and Shrinkage Methods Main Demo 2.3 Kernel Functions and SMO Algorithm 2.2 Support Vector Machines (SVM), Large Margin Classifiers, and Nonlinear SVM 2.1 Logistic Regression and Multiclass Classification Quiz 1 Blog