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AI in security
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
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AI in security
- 1. AI in Security SubratKumar Panda AI First Thought Leader, Director of Engineering, AI and Data Sciences, Capillary Technologies Bangalore
- 2. Agenda ● AI andIndustry 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 aboutme ● 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 canand cannot Do today ? https://hbr.org/2016/11/what-artificial-intelligence-can-and-cant-do-right-now
- 9. Supervised Learning 1. Beingable 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 MLSuccess
- 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 inCybersecurity https://www.ciab.com/resources/artificial-intell igence-cybersecurity/
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- 18. AI powered InformationSecurity https://blog.capterra.com/artificial- intelligence-in-cybersecurity/
- 19. Awesome ML Papersand Code for Cyber Security - https://github.com/jivoi/awesome-ml-for-cybersecurity - Datasets - Papers - Books - Talks - Tutorials - Courses
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- 23. Malware Detection Methodology -Problem Formulation - Binary Classification Problem - Dataset - Feature Extraction - Dimensionality Reduction - Model Building and Analysis
- 24. Datasets - Malicia Projectdata - 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 - Decodingthe 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 LearningModel - Exploration/Ensemble of multiple models - Random Forest - DNN-2L - DNN-4L - DNN-7L
- 27. Results - Achieved thehighest 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 Malwaredetector https://cse.iitk.ac.in/users/spramod/papers/date17.pdf
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