Blockchain 2017 Workshop: Blockchain and Machine Learning

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

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], 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.

Academics at CEN

For earlier workshops conducted by CEN click here

Venue: Murlikrishna hall, CIR Block

09:00 - 06:00   Saturday December 16, 2017

16, December 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

Understanding 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 (Research Scholar) and Mr. Harikrishnan N B (MTech-CEN)

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


Register here

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: (link is external) Manu Unni, Praveen Krishnan, Dr. K. P. Soman.
Text books

List of recent papers published from CEN on Deep learning based CyberSecurity
  1. Book chapter

    • "Scalable Framework for Cyber Threat Situational Awareness based on Domain Name Systems Data Analysis." will appear in Big data in Engineering Applications (Springer). [under print]

  2. Journals

    • "Detecting Android Malware using Long Short-term Memory-LSTM." will appear in Journal of Intelligent and Fuzzy Systems - IOS Press. [under print]

    • "Evaluating Deep Learning Approaches to Characterize and Classify the DGAs at Scale." will appear in Journal of Intelligent and Fuzzy Systems - IOS Press. [under print]

    • "Evaluating Deep learning Approaches to Characterize, Signalize and Classify malicious URLs." will appear in Journal of Intelligent and Fuzzy Systems - IOS Press. [under print]

    • "Detecting Malicious Domain Names using Deep Learning Approaches at Scale." will appear in Journal of Intelligent and Fuzzy Systems - IOS Press. [under print]

  3. Springer proceedings

    • "Prediction of Malicious Domains Using Smith Waterman Algorithm." [paper]

    • "Fast Fourier Transform and Nonlinear Circuits Based Approach for Smart Meter Data Security." [paper]

    • "Deep Learning for Network Flow Analysis and Malware Classification." will appear in Springer CCIS. [under print]

  4. Conference papers

    • "Evaluating Shallow and Deep networks for SSH Traffic Analysis using Flow based mechanisms." will appear in IEEE Xplore. [under print]

    • "Evaluating Effectiveness of Shallow and Deep Networks to Intrusion Detection System." will appear in IEEE Xplore. [under print]

    • "Deep Android Malware Detection and Classification." will appear in IEEE Xplore. [under print]

    • "Long Short-Term Memory based Operation Log Anomaly Detection." will appear in IEEE Xplore. [under print]

    • "Deep Encrypted Text Categorization." will appear in IEEE Xplore. [under print]

    • "Applying Convolutional Neural Network for Network Intrusion Detection." will appear in IEEE Xplore. [under print]

    • "Secure Shell (SSH) Traffic Analysis with Flow based Features Using Shallow and Deep networks." will appear in IEEE Xplore. [under print]

    • "Applying Deep Learning Approaches for Network Traffic Prediction." will appear in IEEE Xplore. [under print]

    • "Evaluating Shallow and Deep Networks for Ransomware Detection and Classification." will appear in IEEE Xplore. [under print]

AISec group @ CEN. All rights reserved. Developed by Vinayakumar R and Harikrishnan N B