Welcome to Leon Reznik's Homepage

I am now with the Department of Computer Science at Rochester Institute of Technology, Rochester, NY.

You can find me in Room 3-521, Golisano Hall (Building 70),  or you can contact me by means given here Please, see my schedule this term.

Previously I worked at the Department of Computer Science, University of Texas at El Paso and School of Communications and Informatics at Victoria University, Melbourne, Australia.

Please, see my CV for more information.

my/thankyou.jpg Big THANK YOU to all of you who congratulated me:
with our NSF grant Data quality and security evaluation framework for mobile devices platform
AND our new grant from US Military Academy Self-learning capabilities for a mission oriented data quality and security assurance in military IoT systems.
I hope to enhance our work collaboration on these interesting topics.


Do you want to know what we are doing? Thank you for your interest.

If you missed our webinar , where we were describing our work on the NSF project, organized by the National Center for Supercomputing Applications at University of Illinois at Urbana-Champaign under auspices of NSF, on March 26, 2018 , please, watch its video recordings Data Quality & Security Evaluation Framework Development with Leon Reznik & Igor Khokhlov on the YouTube channel. Also, you can download the corresponding slides .

Please, see our publications and mobile applications for more information about our Data Quality and Security Evaluation framework and its implementations on mobile devices.

Igor Khokhlov, Leon Reznik, Sergey Chuprov Framework for Integral Data Quality and Security Evaluation in Smartphones published in IEEE Systems Journal in 2020, and available on IEEE Xplore (DOI: 10.1109/JSYST.2020.2985343) describes our novel systematic approach to the framework design and implementation, and introduces a few case studies

and also Igor Khokhlov and Leon Reznik, What is the Value of Data Value in Practical Security Applications, presented at IEEE/NDIA/INCOSE Systems Security Symposium 2020, Crystal City, Virginia, USA, July 2020 doi: 10.1109/SSS47320.2020.9174457 that adds up a new twist to our data quality research.

To validate the framework application, we employed it on the metric data-sets acquired from real smartphones and their sensors. We collected sensor DQ metrics and security metrics from more than a hundred Android smartphones and evaluated their quality and security. The results are available here. In addition, we collected DQ characteristics of sensors from 9443 mobile devices, including Android smartphones, wearable devices, and tablets, compared and classified them based on the evaluated quality of data they produced.

For the first time, we developed a knowledge base that contains various characteristics of sensors embedded into mobile devices and their quality analysis. We described our methodology, the knowledgebase design, content and user interface in our IEEE Sensors Letters Journal publication - see Igor Khokhlov, Leon Reznik, Sahil Ajmera Sensors in Mobile Devices Knowledge Base , IEEE Sensors Letters Journal , Volume 4, Issue 3, March 2020.

We developed a novel application of machine learning techniques to ensure the data quality delivery in sensor networks and systems - see Igor Khokhlov, Akshay Pudage, Leon Reznik Sensor selection optimization with genetic algorithms, Proc. IEEE Conference on Sensors, Montreal, Canada, October 27-30, 2019.

We investigated an employment of data quality evaluation to improve trust models in making Autonomous Vehicle's control more secure. Please, see Sergey Chuprov, Ilya Viksnin, Iulia Kim, Leon Reznik, and Igor Khokhlov Reputation and Trust Models with Data Quality Metrics for Improving Autonomous Vehicles Traffic Security and Safety, IEEE/NDIA/INCOSE Systems Security Symposium 2020, Crystal City, Virginia, USA, July 2020 doi: 10.1109/SSS47320.2020.9174269.

and Sergey Chuprov, Egor Marinenkov, Ilya Viksnin, Leon Reznik, and Igor Khokhlov Image Processing in Autonomous Vehicle Model Positioning and Movement Control, 2020 IEEE World Forum on Internet of Things, New Orleans, Louisiana, USA, June 2020. doi: 10.1109/WF-IoT48130.2020.9221258

Aiming at helping users to make their devices more secure, we continued our investigation of the colluded applications attack detection on Android devices: with recurrent neural networks - see Igor Khokhlov, Ninad Ligade, Leon Reznik Recurrent Neural Networks for Colluded Applications Attack Detection in Android OS Devices, IEEE World Congress on Computational Intelligence (WCCI) 2020, Glasgow, UK, July 2020. doi: 10.1109/IJCNN48605.2020.9207339. and other techniques - see Igor Khokhlov, Michael Perez, Leon Reznik Machine Learning in Anomaly Detection: Example of Colluded Applications Attack in Android Devices, 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA), Boca Raton, FL, USA, 2019, pp. 1328-1333. doi: 10.1109/ICMLA.2019.00216

my/image006.gif Again, to help ordinary people to make their smartphones more secure in practice , we developed a few Android smartphone security evaluation and enhancement apps. Please, feel free to download them from Google Play . We are working on a few more in pipeline, if you let us know how they work for you, we will really appreciate your advice. If you missed our demonstration of our first Android app for Android System Security Evaluation, at the 2018 15th IEEE Annual Consumer Communications & Networking Conference (CCNC), January 2018 in Las Vegas, NV you are welcome to download it. Or you might like to download a more advanced cloud version that compares your smartphone security with others from Google Play.

Another app Detector of Unverified Apps specifically check all the apps on your smartphone to verify if 'bad' ones are present.

Please, see my key presentations about my two current (and probably, future) areas of research.

Direction 1: Security and data quality evaluation, Big Data phenomenon: see my key address From Big Data to Quality Data: What is the emerging sensor and network technology going to deliver next? to NetWare 2014 , an umbrella event incorporating a few international conferences on November 20, 2014 in Lisbon, Portugal. Also, at the same conference I chaired the panel on November 19 on the topic Information Privacy: Does it really matter?

Direction 2: Intelligent systems and sensor networks design, machine learning and neural networks techniques and their implementation, Big Data analysis: see a joint presentation with my former student Dmitri Yudanov Heterogeneous Implementation of Neural Network Algorithms to the AMD Developer Summit, San Jose, November 11-13, 2013

Want to learn even more?? Please, look below for my latest publications and other information. If you are interested in collaboration, please, contact me (see above).

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We continued our work on developing intelligent applications. Recently, we presented two papers at the IEEE World Congress on Computational Intelligence in Rio de Janeiro, Brazil.
Igor Khokhlov, Chinmay Jain, Ben Miller-Jacobson, Andrew Heyman, Leon Reznik, Robert St. Jacques MeetCI: A Computational Intelligence Software Design Automation Framework. In IEEE World Congress on Computational Intelligence, Rio de Janeiro, Brazil, July 2018 (pp. 1499-1506). introduces MeetCI, our open source software framework for computational intelligence software design automation that facilitates the application design decisions and their software implementation process. MeetCI abstracts away specific framework details of CI techniques designed within a variety of libraries. This allows CI users to benefit from a variety of current frameworks without investigating the nuances of each library/framework.
Akhil Killawala, Igor Khokhlov, Leon Reznik Computational Intelligence Framework for Automatic Quiz Question Generation. In IEEE World Congress on Computational Intelligence, Rio de Janeiro, Brazil, 2018 (pp. 76-83). describes our framework that should automate or semi-automate the process of quiz and exam question generation. The framework operation is based on information retrieval and NLP algorithms. It incorporates the application of production rules, LSTM neural network models, and other intelligent techniques.

We have continued our investigation of machine learning techniques application for human activity recognition with mobile devices - see our paper I. Khokhlov, L. Reznik, J. Cappos and R. Bhaskar Design of activity recognition systems with wearable sensors 2018 IEEE Sensors Applications Symposium (SAS), Seoul, Korea (South), 2018, pp. 1-6. doi: 10.1109/SAS.2018.8336752 for the analysis of our empirical study results.

My student, my colleague and I tried to employ machine learning techniques, in particular convolutional neural networks, to see if there are any patterns in numbers that could be found out. The results are, definitely, not final so far but you can see an idea in M. Potter, L. Reznik and S. Radziszowski, "Neural networks and the search for a quadratic residue detector," 2017 International Joint Conference on Neural Networks (IJCNN), Anchorage, AK, May 2017, pp. 1887-1894, which is available for download .

With my students, we continued our investigation of data quality evaluation and described an application of our framework being developed - see I. Khokhlov, L. Reznik, A. Kumar, A. Mookherjee, R. Dalvi Data Security and Quality Evaluation Framework: Implementation Empirical Study on Android Devices, 2017 20th IEEE Conference of Open Innovations Association and Seminar on Information Security and Protection of Information Technology (FRUCT-ISPIT), St.Petersburg, April 3-7, 2017, ISSN 2305-7254 , pp. 161-168 and L. Herlihy, E. Golen, L. Reznik and S. E. Lyshevski, "Secure communication and signal processing in inertial navigation systems," 2017 IEEE 37th International Conference on Electronics and Nanotechnology (ELNANO), Kiev, 2017, pp. 414-419.

My PhD student and I published two new papers on mobile device security. One ( I. Khokhlov, L. Reznik Data Security Evaluation for Mobile Android Devices, 2017 20th IEEE Conference of Open Innovations Association and Seminar on Information Security and Protection of Information Technology (FRUCT-ISPIT), St.Petersburg, April 3-7, 2017, ISSN 2305-7254, pp. 154-160 ) presents a further development of the Android smartphone security evaluation.
Another one ( I. Khokhlov and L.Reznik, Colluded Applications Vulnerabilities in Android Devices, The 15th IEEE International Conference on Dependable, Autonomic and Secure Computing (DASC 2017), Orlando, FL, November 2017) opens up a discussion of a novel topic of colluded applications attack on Android system.
I presented a new paper on mobile device security evaluation R. Weiss, L. Reznik, Y. Zhuang, A. Hoffman, D. Pollard , A. Rafetseder, T. Li and J. Cappos Trust Evaluation in Mobile Devices: An Empirical Study, The 14th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (IEEE TrustCom-15), Helsinki, Finland, 20-22 August, 2015

We published two papers in the International Journal of Uncertainty, Fuzziness, and Knowledge Based Systems issue devoted to the 50th anniversary of the fuzzy logic invention. Both show how fuzzy logic and systems ideas could be explored in real life. In the first paper: A. Heyman, L.Reznik, M.Negnevitsky, A. Hoffman Fuzzy System Design for Security and Environment Control Applications, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, Vol. 23, Suppl. 1 (December 2015) pp. 43-56 we demonstrate two major applications of rules systems. In the first one rules that are explicitly formulated by experts are employed to evaluate a security level of mobile devices. The second case that considers the rules derived from empirical data discusses air quality evaluation and prediction with neuro-fuzzy systems. The second paper: K. K. Semenov, L. K. Reznik, and G. N. Solopchenko Fuzzy Intervals Application for Software Metrological Certification in Measurement and Information Systems International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, Vol. 23, Suppl. 1 (December 2015) pp. 95-104 examines fuzzy interval applications in software quality evaluation.

We continued to investigate data quality evaluation in Big Data phenomenon and published three new papers on this topic:

L. Reznik, S.E. Lyshevski Data Quality Indicators Composition and Calculus: Engineering and Information Systems Approaches, Sensors & Transducers, Vol. 185, Issue 2, February 2015, pp. 140-148

Lyshevski, S.E. ; Reznik, L. ; Smith, T.C. ; Beisenbi, M.A. ; Jarasovna, J.Y. ; Mukataev, N.S. ; Omarov, A.N. Estimates and measures of data communication and processing in nanoscaled classical and quantum physical systems, 2014 IEEE 14th International Conference on Nanotechnology (IEEE-NANO), Toronto, 2014 , pp.1044 1047

L. Reznik, S. Lyshevski Data Quality and Security Evaluation Tool for Nanoscale Sensors, SECURWARE 2014, The Eighth International Conference on Emerging Security Information, Systems and Technologies, Lisbon, November 16-21, 2014 in NetWare 2014, ISBN: 978-1-61208-047-5, pp. 118-122

Recently, in our research we moved from specifying the concept of data quality evaluation and assurance to investigating its various aspects. In ' L.Reznik and E.Bertino Data Quality Evaluation: Integrating Security and Accuracy, CCS '13: Proceedings of the 2013 ACM SIGSAC conference on Computer & Communications Security, Berlin, November 2013' we looked at the relationship between the accuracy and security indicators and their role in data quality, and how this knowledge can help improve the design of complex cyber-physical sytems and networks

while in ' S.E.Lyshevsky and L.Reznik Information-theoretic estimates of communication and processing in nanoscale and quantum optoelectronic systems, 2013 IEEE XXXIII International Scientific Conference on Electronics and Nanotechnology (ELNANO), 16-19 April 2013, pp. 33-37' we looked for data quality indicators, which might work in nano-scale systems.

At the same time, I continue my research with my former students on the topics of:

  • intrusion detection design - see 'J.Bacaj and L.Reznik Signal Anomaly Based Attack Detection in Wireless Sensor Networks, CCS '13: Proceedings of the 2013 ACM SIGSAC conference on Computer & communications security, Berlin, November 2013', where we investigated typical attacks in sensor networks and cyber-physical systems and their possible detection based on the analysis of the generic communication signals and data
  • and
  • cognitive systems development and implementation - see ' D. Yudanov and L. Reznik Scalable multi-precision simulation of spiking neural networks on GPU with OpenCL The 2012 International Joint Conference on Neural Networks (IJCNN), 2012', where we investigate an implementation of the SNN models on novel GPU platforms.
  • We continued our collaboration (see below) on data quality assessment in cyber-physical data acquisition and control systems with my colleagues Greg Timms, Paolo de Souza from Tasmanian Information and Communication Centre, CSIRO, Australia that resulted in a new paper G.P. Timms, P.A. de Souza, Jr., L. Reznik and D. V. Smith Automated Data Quality Assessment of Marine Sensors, Sensors 2011, 11(10), p.9589-9602

    Also, I presented a more generic approach to the data quality assessment to the 2012 IEEE Instrumentation and Measurement Conference. See L.Reznik Integral Instrumentation Data Quality Evaluation: the Way to Enhance Safety, Security, and Environment Impact, 2012 IEEE International Instrumentation and Measurement Technology Conference, Graz, Austria, May 13-16, 2012, IEEE, 2012, pp. 2138 - 2143 in IEEExplore on-line library (you should have an access to IEEExplore).

    Cognition is a fundamental feature of natural intelligence, which a modern technology has not yet been able to reproduce in full capacity. Sensor networks provide a new technological support for a substantial increase in an amount and quality of information that might be collected and communicated in complex adaptive systems. Their application may significantly raise the degree of intelligence in system design and implementation into the levels where effects of cognition will start kicking in. The paper, which we just published in IEEE SENSORS Journal, with my students Greg Von Pless (BS/MS class of 2007) and Tay Karim (BS/MS class of 2006) describes the results of an empirical study aiming to demonstrate that a cognition ability may be treated as a generic sensor network feature. See L. Reznik, G. Von Pless, and T.Karim Distributed Neural Networks for Signal Change Detection: On the Way to Cognition in Sensor Networks, IEEE Sensors Journal, vol.11, issue 3, March 2011 . It is already available in IEEExplore on-line library (you should have an access to IEEExplore).

    With my students Mike J. Adams (BS/MS class of 2008) and Brian Woodard (BS/MS class of 2009) we have published a paper that improves neural network based intelligent design of intrusion detection systems by dynamically modifying them with genetic algorithms. This approach allows to optimize an intrusion detection based not on performance only but also on a resource consumption. See L. Reznik, M. J. Adams, and B. Woodard Intelligent Intrusion Detection Based on Genetically Tuned Artificial Neural Networks, Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol.14, No.6 pp. 708-713, 2010 . It is already available in this Japanese journal on-line library.

    An application of intelligent techniques in measurement result processing is discussed in our recent paper G.N.Solopchenko, K.K. Semenov, V.Kreinovich and L.Reznik Measurement's Result and its Error as Fuzzy Variables: Background and Perspectives published in Key Engineering Materials, vol. 437, May 2010, pp 487-491 journal.

    How to make sensor networks and systems communicate not only the data they collect but also automatically calculate and provide some characteristics of these data quality? My colleagues Greg Timms, Paolo de Souza from Tasmanian Information and Communication Centre, CSIRO, Australia and myself answer this question in the paper G.P. Timms, P.A. de Souza, L. Reznik Automated assessment of data quality in marine sensor networks, IEEE International Conference OCEANS 2010 IEEE Sydney, Australia, 24-27 May 2010. It is available in IEEE Xplore library.

    Another paper on intelligent sensor networks authored by my students Jody Podpora (class of 2006), Greg Von Pless (class of 2007) and myself was published in IEEE SENSORS Journal, see J.Podpora, L. Reznik, G. Von Pless Intelligent Real-Time Adaptation for Power Efficiency in Sensor Networks, IEEE Sensors Journal, vol.8, issue 12, December 2008, pp.2066-2073 . It is available in IEEExplore on-line library (you should have an access to IEEExplore).

    Our paper L.Reznik and V.Kreinovich "Fuzzy Prediction Models in Measurement" is published in IEEE Transactions on Fuzzy Systems, volume 16, No.4, August 2008, pp. 851-862 . It is available on-line in IEEExplore library (you should have an access right to IEEExplore)

    My students Dima Novikov (class of 2006), Roman Yampolskiy (class of 2005) and myself published a paper on intrusion detection. D. Novikov, R. V. Yampolskiy, L. Reznik Traffic Analysis Based Identification of Attacks. International Journal of Computer Science & Applications (IJCSA), vol. 5, Issue 2, 2008, pp.69-88 It is already available on-line in IJCSA repository

    Wiley and Sons just released Handbook of Granular Computing /Ed: W. Pedrycz, A.Skowron and V.Kreinovich , Wiley and Sons , Chichester, England, 2008, ISBN 978-0-470-03554-2 On pp.517-532 you can find my new paper L. Reznik Measurement Theory and Uncertainty in Measurements: Application of Interval Analysis and Fuzzy Sets Methods