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Blockchain 2017 Workshop: Blockchain and Machine Learning

Saturday December 16, 2017 Centre for Excellence in Computational Engineering and Networking (CEN), Amrita Vishwa Vidyapeetham (Amrita University).

About Blockchain 2017

The AISec (Artificial Intelligence for CyberSecurity) group in the Computational Engineering and Networking (CEN) department at Amrita Vishwa Vidyapeetham is organizing a one-day workshop on 16th December, 2017. The workshop will cover Blockchain technologies including Machine Learning. The main aim of this workshop is to share the ideas of on-going research and exploratory topics, leading to possible collaborations between CEN faculty, Ph.D research scholars and students.

A blockchain is a distributed ledger for recording transactions, maintained by many nodes without central authority through a distributed cryptographic protocol. It is a technology suitable for decentralized and transactional sharing of data across a large network of untrusted participants. Blockchain uses consensus protocol to validate the information to be appended and also ensures that the nodes agree on a unique order in which entries are appended. Blockchain have good potential to transform the logging and management of transactions in different application areas. This technology is successfully used in Bitcoin virtual currency and it also has good potential in non-financial sectors such as Governance, healthcare, cyber security, automobiles, media, travel, hospitality, energy, smart cities etc.

Machine learning have connected different applications. There are many profound applications of blockchain and machine learning. In a shared ledger system, there are two patterns of machine learning use cases:

1. Silo machine learning and predictive models addressing a particular segment of the chain
2. Model chains addressing a segment or the whole chain

The silo machine learning or predictive model is no different from what we do today with data at hand. Model chains are more complex, since they must learn and adjust on the fly given chain dependence. In this workshop we explore the possibility of integrating blockchain technology and machine learning.

The aim of this workshop is to bring together researchers and practitioners working in machine learnnig, cryptography, and security, from academia and industry, who are interested in the technology and theory of blockchains and their protocols. The workshop also highlights the possibility of integrating machine learning and blockchain. The primary target audience of the workshop is Mtech students, research scholars and faculty. We hope to have an interactive session, with a free exchange of ideas, views and comments. Please fill all the required details during registration. For more queries, please contact vinayakumarr77[at], barathiganesh.hb[at], harikrishnannb07[at]

About AISec

AISec group at CEN understand the underlying mathematics knowledge required to apply Machine learning to Cyber security tasks at Scale

The ability to digitalize our lives has outpaced our ability to stay safe. One of the biggest challenges is to understand the volume, velocity and complexity of threatening activity inside the network. We call this cyber intelligence. We have been developing a self-learning intelligence system by understanding the mathematics and using the most advanced machine learning technologies such as deep learning. A self-learning intelligence system learns a unique pattern of normal and abnormal activities of every device and user on a network, and correlates these insights in order to spot emerging threats that would otherwise go unnoticed. AISec group is fortunate to have Cyber security experts and Researchers who have constantly smell the developments in Natural language processing, image processing, Speech recognition and many other areas and incorporate those novel approaches to self-learning system to enhance the system detection rate of malicious activities. We are involved in developing large scale Security projects that involves Big-data Security Intelligence, Cyber-Physical systems security, Machine learning for Security, Complex Binary analysis, IoT, SCADA and Hardware security, Application & Network security, Advanced Forensics and Incident handling. Some of the tasks that we think and solve daily are to apply various Data mining, Machine learning and Deep learning approaches to various cyber security tasks such as Traffic Analysis, Intrusion detection, Malware Analysis, Botnet Analysis, Anonymity Services, Domain Generation Algorithms, Advanced mathematics to Crypto Systems.

Venue: Murlikrishna hall, CIR Block

Registration is closed

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Check out the earlier workshops conducted by CEN

Why we conduct workshop frequently? Advancement in technologies make many conventional engineering syllabus obselete

Schedule Speaker Title of the Talk
9.30AM - 10.30AM Mr. Vijay Krishna Menon Basics of Bitcoin and Blockchain
10.45AM - 11.45AM Mr. Rohith Mohan, working in Caterpillar PUnderstanding Blockchain Technologies with Python
12.00PM - 1.30PM Mr. Bithin Alangot, PhD student at Amrita Vishwa Vidyapeetham Evolution of Blockchain Consensus Protocol
02.30PM - 06.00PM Mr. Vinayakumar R, Mr. Harikrishnan N B Demo: Blockchain and It’s Applications. Frameworks: TensorFlow, Theano, Keras, Scikit-learn, Node.js Programming languages : Java and Python, Packages: Numpy, Scipy, Pandas, Matplotlib, t-SNE, NLTK

Funded Projects in CyberSecurity Domain

1. Information Security Assurance funded by Paramount computer systems - Principal investigator Dr. Soman KP.

2. Early Warning Framework Phase 2 - Network Security Situational Awareness and Countermeasures using DNS, BGP, Netflow and Remote Content Inception funded by DIETY, Govt. of India - Principal investigator Dr. Prabaharan Poornachandran, Co-PI Dr. Soman KP.

Books published from CEN on Signal processing and Machine learning

1. Dr. K.P. Soman, Prabaharan Poornachandran, Sachin Kumar S and Neethu Mohan, "Convex Optimization based Signal Processing for IOT." [Upcoming Book]

2. Dr. K.P. Soman and Dr. Ramachandran K.I, "Insight into Wavelets From Theory to Practice.", Prentice-Hall India 2004.

3. Dr. K.P. Soman, Shyam Diwakar, Ajay V., "Insight into Data Mining From Theory to Practice.", Prentice-Hall India, 2006.

4. Dr. K.P. Soman, Ajay. V, Loganathan R., "Machine Learning with SVM and other Kernel Methods.",Prentice-Hall India, 2009.

5. Dr. K.P.Soman, and Ramanthan, "Digital signal and Image Processing-The Sparse Way." Elsevier Publications, 2012.

6. Dr. Deepa G., Dr. Krishnan Namboodiri, "Bioinformatics: Sequential and Structural Analysis.", Narosa Publications.

7. Dr. K.I Ramachandran., Dr. Deepa, Dr. Krishnan Namboori, "Computational Chemistry and Molecular Modelling." -Springer international.

8. "Fractals for Everyone." [Online version]( Manu Unni, Praveen Krishnan, Dr. K. P. Soman.

Books published from CEN