1. AI in Cyber-security: Examples of Algorithms & Techniques
Tapati Bandopadhyay

2. The world of AI Tools, Orchestration & Robotic Process Automation
Complex task, Invasive (not ported across platforms), modifications requires changes in code, set of instructions to perform simple tasks
Orchestration: work-flow driven, Semi-invasive (Needs integration)
RPA: Fixed rules based, Non-invasive (works without customizations), Easily scalable, best fit for business process automation
Cognitive (supervised machine learning)
NLP – training with annotated/ labelled corpus, ontologies, basic sentiment analysis, bagging/ boosting, Bag of Words, Word2Vec
Forecasting: ML toolboxes, predictive analytics, time-series- ARIMA, MDP, Bayesian networks, Semantic Knowledge Models
Pattern recognition: Random forests, NN- CNN/ RNN, recommendation engines & classifiers [most common use-cases], clustering
Deep learning, Unsupervised learning & other advanced ML
Context Aware, Deep learning from observations, reasoning based advance decision makings and problem resolutions
LSTM, GRU, HTM, reinforcement learning, transfer learning, explainable AI- LRP/ LIME, Meta-learning, GAN, TOMNet
Machine vision, 3D extrapolation, Capsule networks, I2A algorithms

3. New types of Malware & Preventive Systems -1
Generative Adversarial Network
MalGAN:
Generate adversarial malware examples, to bypass black-box machine learning based detection models.
Uses a substitute detector to fit the black-box malware detection system.
Trained to minimize the generated adversarial examples’ malicious probabilities predicted by the substitute detector.
Better than gradient based adversarial example generation algorithms - decrease the detection rate to nearly zero, make the retraining based defensive method against adversarial examples hard to work.
Generating Adversarial Malware Examples for Black-Box Attacks Based on GAN, Weiwei Hu and Ying Tan
fweiwei.hu,

4. New types of Malware & Preventive Systems - 2
Neural Network-based
Example: MtNet
multi-task, deep learning architecture for malware classification for the binary (i.e. malware versus benign) malware classification task.
trained with data extracted from dynamic analysis of malicious and benign files.
improvements using multiple layers in a deep neural network architecture
trained on 4.5 million files and tested on a holdout test set of 2 million files
MtNet: A Multi-Task Neural Network for Dynamic Malware Classification, Wenyi Huang and Jack W. Stokes
Pennsylvania State University; Microsoft Research

5. Deep Sentinel: Deep learning in home security – Raw video streams
Keep an eye on…
AI2- MIT start-up: Unsupervised ML on raw data -detects abnormal activities- with human feedback, accuracy can reach >86%
Deep Sentinel: Deep learning in home security – Raw video streams
Cloudfare: IoT security – Orbit, Obert

6. NLP in Information Security
To detect malicious code- source code vulnerability analysis
To detect malicious DNS- Domain generation algorithms classifiers
To detect phishing- Bag-of-Words model – phishing s
Malware family analysis- Topic Modelling