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100+ Machine Learning Resources for Cybersecurity to Protect us from Cyberattacks
- AI (Artificial Intelligence) - a broad concept.A Science of making things smart or, in other words, human tasks performed by machines (e.g., Visual Recognition, NLP, etc.). The main point is that AI is not exactly machine learning or smart things. It can be a classic program installed in your robot cleaner like edge detection. Roughly speaking, AI is a thing that somehow carries out human tasks.
- ML (Machine Learning) - an Approach(just one of many approaches) to AI that uses a system that is capable of learning from experience. It is intended not only for AI goals (e.g., copying human behavior) but it can also reduce the efforts and/or time spent for both simple and difficult tasks like stock price prediction. In other words, ML is a system that can recognize patterns by using examples rather than by programming them. If your system learns constantly, makes decisions based on data rather than algorithms, and change its behavior, it's Machine Learning.
- DL (Deep Learning) - a set of Techniques for implementing machine learning that recognizes patterns of patterns -Ã?? like image recognition. The systems identify primarily object edges, a structure, an object type, and then an object itself. The point is that Deep Learning is not exactly Deep Neural Networks. There are other algorithms, which were improved to learn patterns of patterns, such as Deep Q Learning in Reinforcement task.
- Regression (or prediction) - a task of predicting the next value based on the previous values.
- Classification - a task of separating things into different categories.
- Clustering - similar to classification but the classes are unknown, grouping things by their similarity.
- Association rule learning (or recommendation) - a task of recommending something based on the previous experience.
- Dimensionality reduction - or generalization, a task of searching common and most important features in multiple examples.
- Generative models - a task of creating something based on the previous knowledge of the distribution.
- Supervised learning. Task Driven approach. First of all, you should label data like feeding a model with examples of executable files and saying that this file is malware or not. Based on this labeled data, the model can make decisions about the new data. The disadvantage is the limit of the labeled data.
- Ensemble learning. This is an extension of supervised learning while mixing different simple models to solve the task. There are different methods of combining simple models.
- Unsupervised Learning. Data Driven approach. The approach can be used when there are no labeled data and the model should somehow mark it by itself based on the properties. Usually, it is intended to find anomalies in data and considered to be more powerful in general as it's almost impossible to mark all data. Currently, it works less precisely than supervised approaches.
- Semi-supervised learning. As the name implies, semi-supervised learning tries to combine benefits from both supervised and unsupervised approaches, when there are some labeled data.
- Reinforcement learning. Environment Driven approach can be used when the behavior should somehow react to the changing environment. It's like a kid who is learning environment by trial and error.
- Active learning. It's more like a subclass of Reinforcement learning that probably will grow into a separate class. Active learning resembles a teacher who can help correct errors and behavior in addition to environmental changes.
- Linear regression
- Polynomial regression
- Ridge regression
- Decision trees
- SVR (Support Vector Regression)
- Random forest
- Artificial Neural Network (ANN)
- Recurrent Neural Network (RNN)
- Neural Turing Machines (NTM)
- Differentiable Neural Computer (DNC)
- LogisticRegression (LR)
- K-Nearest Neighbors (K-NN)
- Support Vector Machine (SVM)
- KernelSVM
- NaiveBayes
- DecisionTreeClassification
- Random Forest Classification
- Artificial Neural Network
- Convolutional Neural Networks
- K-nearest neighbors (KNN)
- K-means
- Mixture model(LDA)
- DBSCn
- Bayesian
- GaussianMixtureModel
- Agglomerative
- Mean-shift
- Self-organized Maps (SOM) or Kohonen Networks
- Apriori
- Euclat
- FP-Growth
- Deep Restricted Boltzmann Machine (RBM)
- Deep Belief Network (DBN)
- Stacked Autoencoder
- Principal Component Analysis (PCA)
- Singular-value decomposition (SVD)
- T-distributed Stochastic Neighbor Embedding (T-SNE)
- Linear Discriminant Analysis (LDA)
- Latent Semantic Analysis (LSA)
- Factor Analysis (FA)
- Independent Component Analysis (ICA)
- Non-negative Matrix Factorization (NMF)
- Markov Chains
- Genetic algorithms
- Variational Autoencoders
- Generative adversarial networks (GANs)
- Boltzmann Machines
- prediction;
- prevention;
- detection;
- response;
- monitoring.
- network (network traffic analysis and intrusion detection);
- endpoint (anti-malware);
- application (WAF or database firewalls);
- user (UBA);
- process (anti-fraud).
- in transit in real time;
- at rest;
- historically;
- etc.
- regression to predict the network packet parameters and compare them with the normal ones;
- classification to identify different classes of network attacks such as scanning and spoofing;
- clustering for forensic analysis.
- Machine Learning Techniques for Intrusion Detection
- Long Short-Term Memory Networks for Anomaly Detection in Time Series
- Anomaly Detection Framework Using Rule Extraction for Efficient Intrusion Detection
- A survey of network anomaly detection techniques
- Shallow and Deep Networks Intrusion Detection System: A Taxonomy and Survey
- Deep Packet: A Novel Approach For Encrypted Traffic Classification Using Deep Learning
- Performance Comparison of Intrusion Detection Systems and Application of Machine Learning to Snort System
- Evaluation of Machine Learning Algorithms for Intrusion Detection System
- One Class collective Anomaly Detection based on LSTM
- Network Traffic Anomaly Detection Using Recurrent Neural Networks
- Sequence Aggregation Rules for Anomaly Detection in Computer Network Traffic
- Big collection of all approaches for IDS
- regression to predict the next system call for executable process and compare it with real ones;
- classification to divide programs into such categories as malware, spyware, and ransomware;
- clustering for malware protection on secure email gateways (e.g., to separate legal file attachments from outliers).
- regression to detect anomalies in HTTP requests (for example, XXE and SSRF attacks and auth bypass);
- classification to detect known types of attacks like injections (SQL, XSS, RCE, etc.);
- clustering user activity to detect DDOS attacks and mass exploitation.
- regression to detect anomalies in User actions (e.g., login in unusual time);
- classification to group different users for peer-group analysis;
- clustering to separate groups of users and detect outliers.
- regression to predict the next user action and detect outliers such as credit card fraud;
- classification to detect known types of fraud;
- clustering to compare business processes and detect outliers.
- AI for Cybersecurity by Cylance(2017)- Short but good introduction to basics of ML for Cybersecurity. Good practical examples.
- Machine Learning and Security by O'Reilly ( January 2018 ) - a Best book so far about this topic but very few examples of Deep Learning and mostly a general Machine Learning
- Machine Learning For Penetration Testers, by Packt ( July 2018 )- Less fundamental than the previous one, but have more Deep Learning approaches
- Malware Data Science: Attack Detection and Attribution(sept 2018) - As seen from the title, this book is focused on malware. It was just released by the time of writing this article so I can't give any feedback so far. But I bet it is a must for everyone from endpoint protection teams.