AI and machine learning techniques are increasingly being used for security applications. Some key points: 1. AI is enabling new technologies like Industry 4.0 through applications in IoT, big data, and automated decision making. Security is evolving to use these new AI capabilities. 2. Common ML tasks used in security include classification, clustering, and anomaly detection. Deep learning models are also used for malware detection. 3. Areas where AI is applied to security include malware detection, threat detection, fraud detection, and network security monitoring. Both supervised and unsupervised learning techniques are used. 4. Building effective AI security solutions requires datasets, feature extraction, model selection and training, and evaluation of results. Transfer

1. AI in Security
Subrat Kumar Panda
AI First Thought Leader,
Director of Engineering, AI and Data Sciences,
Capillary Technologies
Bangalore

2. Agenda
● AI and Industry 4.0
● Brief intro AI, ML, IoT
● Security Evolution (AI related)
● Era of Data
● AI use cases in security
● Building and deploying a Intelligent Security Product

3. Brief Introduction about me
● BTech ( 2002) , PhD (2009) – CSE, IIT Kharagpur
● Synopsys (EDA), IBM (CPU), NVIDIA (GPU), Taro (Full Stack Engineer), Capillary (Principal Architect - AI)
● Applying AI to Retail
● Co-Founded IDLI (for social good) with Prof. Amit Sethi (IIT Bombay), Jacob Minz (Synopsys) and Biswa
Gourav Singh (Capillary)
● https://www.facebook.com/groups/idliai/
● Linked In - https://www.linkedin.com/in/subratpanda/
● Facebook - https://www.facebook.com/subratpanda
● Twitter - @subratpanda

4. Industry 4.0
https://en.wikipedia.org/wiki/Industry_4.0
1. Interoperability
2. Information
transparency
3. Technical assistance
4. Decentralized
decisions

5. Knowledge is Power - Sir Francis Bacon
- Industry 4.0 enabled by IoT, BigData and AI
- IoT is the intelligent sensor
- BigData will enable processing huge volumes of data
- AI will make sense of the data in decision making
- AI helps transform raw data into power - AI will transform businesses for sure
- Primarily Machine Learning and then the deeper aspects with Deep Learning
AI is the bedrock on which Industry 4.0 relies on.

6. The AI landscape - Nvidia

7. Machine Learning – http://techleer.com

8. What AI can and cannot Do today ?
https://hbr.org/2016/11/what-artificial-intelligence-can-and-cant-do-right-now

9. Supervised Learning
1. Being able to input A and output B will transform many industries.
2. The technical term for building this A→B software is supervised learning.
3. The best solutions today are built with a technology called deep learning or deep neural
networks, which were loosely inspired by the brain.
4. Basically labelled data is the most important requirement for Supervised Learning.
If a typical person can do a mental task with less than one second of thought, we can probably automate it
using AI either now or in the near future. - Andrew Ng

11. Transfer Learning - http://ruder.io/transfer-learning/

12. Transfer Learning - http://ruder.io/transfer-learning/

13. Drivers of ML Success

14. Machine Learning Tasks
● 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.

15. AI Funding in Cybersecurity
https://www.ciab.com/resources/artificial-intell
igence-cybersecurity/

16. Trends to Watch
https://www.ciab.com/resources/artificial-intell
igence-cybersecurity/

17. Future of AI
https://threatpost.com/artificial-intelligence-a-
cybersecurity-tool-for-good-and-sometimes-b
ad/137831/

18. AI powered Information Security
https://blog.capterra.com/artificial-
intelligence-in-cybersecurity/

19. Awesome ML Papers and Code for Cyber Security
- https://github.com/jivoi/awesome-ml-for-cybersecurity
- Datasets
- Papers
- Books
- Talks
- Tutorials
- Courses

20. ML

21. Applications
https://ccdcoe.org/uploads/2018/10/Art-19-On-the-Effectiveness-of-Machine-and-Deep-Learning-for-Cyber-Security.pdf

22. Malware Detection
https://arxiv.org/pdf/1904.02441.pdf

23. Malware Detection Methodology
- Problem Formulation - Binary Classification Problem
- Dataset
- Feature Extraction
- Dimensionality Reduction
- Model Building and Analysis

24. Datasets
- Malicia Project data
- Difference between the number of malware (11, 308) and benign executables (2, 819)
- Oversampling, Undersampling, Cluster based sampling helps
- Generalizability achieved by K-fold Cross Validation

25. Feature Extraction
- Decoding the executables
- Literature shows that various static attribute such as Windows API calls, strings, opcode, and
control flow graph are good feature vectors
- They used opcode frequency as a discriminatory feature
- Dimensionality Reduction
- Variance Threshold
- Autoencoders

26. Building the Learning Model
- Exploration/Ensemble of multiple models
- Random Forest
- DNN-2L
- DNN-4L
- DNN-7L

27. Results
- Achieved the highest accuracy of
99.78% with random forest and
variance threshold which is an
improvement of 1.26% on
previously reported the best
accuracy.
- In feature reduction, variance
threshold outplayed auto-encoders
in improving the model
performance.
- The best result did not come from
any of the deep learning models.
- DL was a overkill for Malicia
Dataset

28. Hardware Based Malware detector
https://cse.iitk.ac.in/users/spramod/papers/date17.pdf

29. Feature Sets
https://cse.iitk.ac.in/users/spramod/papers/date17.pdf

30. Reinforcement Learning

31. DQN architecture

32. Questions?