The Complete Artificial Intelligence for Cyber Security 2024

Explore supervised, unsupervised, and reinforcement learning, including regression and classification, SVMs and neural networks, clustering with k means and DBscan, PCI, and reinforcement methods like Q-learning and proximal policy optimization.

Explore scikit-learn, an open source Python library for machine learning, covering classification, regression, clustering, and PCA-based dimensionality reduction. Master model selection, training workflows with cross-validation and hyperparameter tuning.

Discover pandas, open-source Python library for data manipulation and analysis, with data frames and series, enabling cleaning, aggregation via group by, and merging for efficient handling of large data sets.

master numpy, a fast python library for numerical computing with multi-dimensional arrays and matrices, learn installation, array creation, operations, reshaping, indexing, and linear algebra for data science and machine learning.

Learn how standardization scales features to zero mean and unit variance to improve model performance and training speed; includes a Python example using scikit-learn.

Apply principal component analysis to transform standardized data using sklearn's decomposition. Assess dimensionality reduction with explained_variance_ratio to show how much variance each component explains.

Understand Markov chains, including states, transition probability, state transition matrix, and steady state distribution, with a weather example and Python simulation.

Implement markov chains in Python by loading CSV review data with pandas in Colab, building a markov chain model, and generating sentences of specified lengths.

Explore Plotly, an open source Python library for interactive visualizations across line, pie, scatter, heatmap, and 3D charts. Install with pip and build plots in Jupyter notebooks, Dash and Dask.

implement clustering on a malware dataset by preparing features with pandas, splitting data with train_test_split, applying k-means with two clusters, and visualizing results with a 3d plot.

Discover XGBoost, a fast, scalable gradient boosting classifier and regressor that handles missing values, prevents overfitting with regularization, and powers efficient, ensemble tree models for tabular data.

Explore scipy, a numpy-based open-source python library for optimization, linear algebra, interpolation, integration, statistics, curve fitting, and signal processing, with applications in machine learning and data science.

Matplotlib is a Python library for static, animated, and interactive visualizations in machine learning. It supports exploratory data analysis, model evaluation, and decision boundary visualization.

Learn how isolation forest uses random feature splits to isolate anomalies in a tree-based, unsupervised framework, with Python implementation for fraud, intrusion, manufacturing defects, and medical data.

Apply isolation forest to detect anomalies on synthetic data, building training and test sets with numpy and pandas, visualizing results with matplotlib and assessing model predictions.

Explore the k-nearest neighbors algorithm, a supervised, non-parametric lazy learner for classification and regression. Choose k, compute distances, and apply metrics like euclidean, manhattan, minkowski, and hamming.

Explore hashing vectorizer as a memory-efficient, fast feature transformation for text in NLP, contrasting it with count and tf-idf vectorizers, and noting hash collisions and streaming data use.

Explain tf-idf, including term frequency and inverse document frequency, and show how tf-idf scores reveal term importance across a corpus for information retrieval, text classification, and keyword extraction.

Instantiate the hashing vectorizer with unigrams and bigrams, then apply a tf-idf transformer from scikit-learn to create the x_train_tf matrix from a utf-8 encoded dataset loaded in a colab notebook.

Logistic regression is a supervised binary classifier using a linear combination of features with weights and a bias, and a sigmoid to predict probability, with Python implementation and evaluation metrics.

Explore how phishing attacks impersonate legitimate entities to steal credentials via emails and websites, and examine forms such as email, spear phishing, web links, voice, sms, clone phishing, and farming.

Explore spam detection: distinguish spam from ham, analyze content, headers, and sender reputation, and apply machine learning, natural language processing, and deep learning to filter emails, texts, and social posts.

Explore the perceptron, its input layer, weights, bias, and activation (step function), and learn how it trains, predicts, and limits like linear separability and noisy data, leading to multilayer perceptrons.

Learn how a support vector machine uses a hyperplane and margins to separate data, while the kernel trick enables non-linear classification and soft margins with the C parameter.

Identify malware as malicious software—viruses, worms, trojans, ransomware, spyware, rootkits—designed to disrupt or seize control, spreading via email, sites, and vulnerabilities; protect with antivirus, updates, and backups.

Compare static and dynamic malware analysis, then explain hybrid analysis that combines both for safer, deeper insights into malware behavior.

Explore the Markov process, a memoryless stochastic framework with states and transition probabilities, covering discrete and continuous time types, key concepts, and practical applications from weather to finance and computing.

Develop and implement entropy-based analysis of binary data by extracting n-grams with Python, using collections and NLTK to count and analyze byte sequences.

Explore metamorphic malware, a type that rewrites its own code to evade detection, lacking a fixed signature, using transformation and obfuscation, with techniques like instruction substitution and code reordering.

Apply k-means to partition data into k clusters by iteratively assigning points to the nearest centroid and updating centroids until convergence, highlighting unsupervised learning, scalability, and initial centroid dependence.

use k-means to cluster malware data into two groups using features like major linker version, major image version, major operating system version, and dll characteristics, and evaluate with a confusion matrix and clustering coefficient, achieving about 95%.

Develop a malware detector by building a decision tree classifier in Python, loading a malware dataset, performing train-test split, and achieving about 96% accuracy in Colab.

Explore automatic intrusion detection, including network-based, host-based, and hybrid IDS, and how sensors, analyzers, and alerts detect threats in real time using signature, anomaly, and machine-learning methods.

Explore what spam email is, its deceptive content and phishing risks, and how spam filtering uses content-based, rule-based, Bayesian, machine learning, and other methods to protect inboxes.

Explain what a phishing url is and how attackers mimic legitimate sites to steal data, with techniques like domain spoofing, typo squatting, and cloaking, plus detection and defense strategies.

Explore what a network is, its types from LAN to VPN, key components like routers, switches, NICs, and media, and core concepts in topology, architecture, and security.

Classify networks by size, scope, architecture, topology, medium, protocol, and purpose. Explore examples from personal area to wide area networks and topology options like bus, star, ring, and mesh.

Network behavior anomaly detection monitors traffic to identify unusual patterns, using baselines and real-time analysis, with machine learning and AI to detect unknown threats and enable proactive security.

Explore credit card fraud detection, including card not present and card present fraud, real-time challenges, data sources, and machine learning approaches.

Explain counterfeit banknote detection and highlight physical features such as watermarks, holograms, and UV. Discuss machine learning and image processing, manual inspection, and sensor based detection.

Explore what ad blocking is and how it works, including blocking ad server requests, CSS/HTML element blocking, and script suppression, and consider publisher strategies for privacy and revenue.

Explore Gaussian Naive Bayes, a fast, simple classifier that assumes Gaussian-distributed continuous features and conditional independence; learn its applications in spam detection, text classification, medical diagnosis, and fraud detection.

Learn botnet detection with a Colab machine learning workflow, loading data, performing train-test split, and comparing k-nearest neighbors, Gaussian Naive Bayes, and decision trees for accuracy.

Learn how to implement counterfeit banknote detection using pandas and a random forest classifier, from loading the dataset and preparing train-test splits to achieving around 99% accuracy.

Develop an ad blocking workflow by cleaning data with pandas, encoding labels, performing a train-test split, and training a random forest to distinguish ads from non-ads using 1558 numerical features.

Implement a phishing URL detection using a random forest classifier in Colab, loading train and test CSVs, preparing features and targets, and evaluating with accuracy and a confusion matrix.

Learn to implement spam detection by building an email corpus, performing train-test split, and applying NLP techniques with hashing vectorizer, tf-idf, and a balanced decision tree classifier, achieving high accuracy.

Explore password security, including strong passwords, hashing with bcrypt and Argon2, multifactor authentication, and password managers. Learn attacks like brute force and phishing and apply best practices to protect accounts.

Explore artificial neural networks, including neurons, layers, weights, biases, activation functions, forward and backpropagation, plus types like feedforward, CNN, RNN, LSTM, GANs, and applications like image recognition and autonomous vehicles.

Explain variance as the spread of a single variable, covariance as how two variables move together, and the covariance matrix summarizes these relationships for applications in finance and machine learning.

Learn the definitions, intuition, and calculation of eigenvectors and eigenvalues, explore their key properties, and see how they power applications from PCA and PageRank to vibration analysis and quantum states.

Learn the MLP classifier, a scikit-learn multi-layer perceptron for supervised classification, trained by backpropagation, with activation functions and hyperparameters, and applied to image, speech, medical, financial, and NLP tasks.

Explore how the XGBoost classifier uses gradient boosting, regularization, and missing-value handling to deliver fast, scalable predictions for classification, regression, time series, and anomaly detection.

Explore physical and cloneable functions that generate unique device identifiers and cryptographic keys for authentication, secure boot, anti-counterfeiting, and IoT hardware protection.

Explore challenge response pairs, CRP-based methods that use physical variations for authentication and key generation. Learn CRP properties—unpredictability, uniqueness, reliability, non-linearity—and their security benefits and challenges.

Assess password security by building a Colab workflow that loads a password dataset with pandas, utilizes a character tf-idf vectorizer and an xgboost classifier, and reports training accuracy.

Develop and evaluate keystroke detection models by loading data, visualizing average keystroke latency by subject, and comparing k-nearest neighbor, SVM, and MLP classifiers with a train-test split and confusion matrix.

Implement facial recognition using the labeled faces in the wild dataset, apply PCA for dimensionality reduction, and train an MLP classifier to predict identities.

Outline the course structure for AI in cyber security, covering basic machine learning for cyber security, email threat detection with AI, malware and network anomaly detection, and secure data practices.

Watch all video content and follow along with the code and logic, use the Q&A to ask questions and help others, to get the most out of this course.

Build artificial intelligence knowledge for cybersecurity through real-world data and practical projects from basics to advanced. Gain portfolio-ready machine learning, artificial intelligence, and deep learning skills applicable beyond cybersecurity.

Discover how the course works: start with project introductions, then coding, define problems (like DDoS attacks), review notebook notes, and follow flowchart-style, step-by-step explanations.

Explore the three types of machine learning—supervised, unsupervised, and reinforcement—illustrated by spam classification, clustering, and reward-based learning with Markov and Q-learning.

Discover fundamental machine learning techniques for cybersecurity, including clustering and gradient boosting trees, and address data challenges like imbalanced data and false positives.

Explore train test split in machine learning, separating training data from test data to assess model performance and mitigate overfitting, with emphasis on cybersecurity classifiers.

This lecture demonstrates implementing a train test split and a validation split to create 60% training, 20% validation, and 20% testing sets using sklearn and pandas in a Colab workflow.

Standardize data by shifting features to zero mean and unit variance to boost model performance. Apply standard scaling with scikit-learn to prepare datasets for neural networks.

Learn how to perform clustering with scikit-learn to group data by similarity, visualize 3d plots with plotly, and apply to cybersecurity tasks like distinguishing anomalous activity and classifying malware.

Train and evaluate an XGBoost classifier on a malware dataset, using a train-test split, fitting on the training data, predicting the test data, and reporting high accuracy.

Explore time series analysis to predict cyber attacks like insider exfiltration by modeling data collected at successive times. Apply autoregression, moving average, and simple exponential smoothing to forecast results.

Analyze a toy time series with statsmodels, comparing autoregressive, moving average, and simple exponential smoothing to forecast future values.

Explore anomaly detection with isolation forest to identify events that deviate from patterns, and learn how isolation forest isolates outliers to detect network intrusions and fraud.

Train an isolation forest on normal observations to perform anomaly detection, then test on normal data and outliers, visualizing and evaluating the model’s ability to separate anomalies.

Explore anomaly detection with isolation forest, training on normal data, identifying outliers, evaluating false positives, and tuning the classifier for robust cyber security insights.

Implement natural language processing with a hash vectorizer and tf-idf in scikit-learn, using 1-2-gram ranges and an idf transformer to build a sparse feature matrix.

Detect email-based cybersecurity threats using ai with machine learning, and explore detection strategies from linear classifier and bi filters to decision trees, logistic regression, and natural language processing.

Learn how to detect spam with the perceptron, a simple neural network, as one of the first successful AI applications in cybersecurity, using Spam Assassin.

Explore how a spam filter classifies emails using two keywords and weighted scores, determines a spam threshold, and introduces linear classifiers and perceptrons for detection.

Explore how a linear classifier scores emails using a weight vector and feature vector, sets a threshold to separate spam from ham, and expresses the model in compact matrix form.

Learn how the perceptron learns: activate via a threshold function, produce plus or minus one, initialize weights to zero, and update with delta omega_i = lambda (y - y_i) x_i.

Build a spam filter with a perceptron using scikit-learn on an sms spam dataset, preprocess text to numeric features, and train with a 70/30 split to reach 77% accuracy.

Discover how support vector machines use non-linear hyperplanes and margin optimization to separate classes in spam detection, comparing SVMs to perceptrons and highlighting margin maximization to reduce overfitting.

Demonstrates building an SVM spam filter using the sms_spam_svm dataset, training with a linear kernel, and evaluating with 84% accuracy, plus discussion of OCR, content-based, and non-content filtering.

Explore phishing detection with logistic regression and decision trees, comparing linear and logistic regression models for spam classification and phishing analysis. Analyze how features, weights, and the intercept shape predictions.

Implement logistic regression to build a phishing detector, leveraging a dataset in Colab and sklearn for classification, achieving about 91.7% accuracy, with advantages and limitations discussed.

Explore how decision trees handle quantitative and qualitative features, using numeric encoding and binary splits with metrics and stopping conditions, illustrated by the iris dataset.

Implement a phishing detection model with a decision tree in Colab, train on a CSV dataset, and achieve about 96% accuracy. Assess overfitting risks and sensitivity to training data.

Explore spam detection with naive Bayes, using Bayes rule to update posterior probabilities as new emails arrive. Analyze text features and independent event assumptions to dynamically recognize suspect keywords.

Explore natural language processing, from tokenization and structure analysis to semantic understanding. Build a spam detector using Naive Bayes with bag-of-words and tf-idf features.

Explore malware detection in cybersecurity using static and dynamic analysis, AI-powered triage, and automated tools to identify malicious files, counterfeit links, and phishing threats.

Discover common malware types, including legitimate-appearing executables, rootkits, trojans, ransomware, and downloaders, and learn how APTs and zero-day vulnerabilities tailor attacks and enable botnets.

Explore static malware analysis by identifying suspect artifacts in binaries, tracing instruction flow, and detecting patterns, system API calls, and strings revealing malware, C2, backdoors, and domains and IP addresses.

Learn dynamic malware analysis in sandboxed environments, complementing static analysis by observing runtime behavior, extracting artifacts from PE binaries, and updating AI datasets with shared signatures.

Explore hacking the PE file format and how executables load into memory. Identify how headers, section tables, and sections like text, data, and resources can conceal malware and enable backdoors.

Explore how decision trees classify malware threats with nonlinear boundaries, from root to branches, guided by Gini impurity, variance reduction, and information gain, and see how random forests reduce overfitting.

Demonstrates malware detection with a decision tree classifier in Colab, loading the dataset from Google Drive, selecting entry point and characteristics, splitting data, training, predicting, and achieving 96% accuracy.

Explore malware detection with a random forest classifier, an ensemble of decision trees that votes for final predictions. Achieve about 96.4% accuracy with an 80/20 train-test split.

Apply k-means clustering to malware artifacts, using features such as major image version and major operating system version, with two clusters, and evaluate results with a confusion matrix.

Identify centroids, assign data to the nearest cluster, and iteratively update centroids in k-means, highlighting its simplicity and scalability and risks of choosing k and high-dimensional data, with scikit-learn parameters.

Explore the limitations of signature-based malware detection and the shift to dynamic analysis for metamorphic malware. Learn mutation techniques, including dead code insertion, instruction order changes, and instruction replacements.

Explore polymorphic malware detection strategies, including code emulation in sandbox environments, behavior-based analysis for metamorphic and zero-day threats, and machine learning with hidden Markov models.

Implement a Hidden Markov model to distinguish malicious versus legitimate states from observing working or not working sequences, using forward and Viterbi algorithms with start, transition, and emission matrices.

Examine malware strategies and threat detection using diverse algorithms, including clustering, classification, and hash-based models, as analysts counter polymorphism and algorithmic tricks.