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2020-11-02
Ermakov, Anton D., Prokopenko, Svetlana A., Yevtushenko, Nina V..  2018.  Security Checking Experiments with Mobile Services. 2018 19th International Conference of Young Specialists on Micro/Nanotechnologies and Electron Devices (EDM). :139—141.
In this paper, we continue to investigate the problem of software security. The problem is to check if software under test has some vulnerabilities such as exceeding of admissible values of input/output parameters or internal variables or can reach states where the software (service) behavior is not defined. We illustrate by experiments that the well-known verifier Java Path Finder (JPF) can be utilized for this purpose. We apply JPF-mobile to Android applications and results of security checking experiments are presented.
2019-11-26
Shukla, Anjali, Rakshit, Arnab, Konar, Amit, Ghosh, Lidia, Nagar, Atulya K..  2018.  Decoding of Mind-Generated Pattern Locks for Security Checking Using Type-2 Fuzzy Classifier. 2018 IEEE Symposium Series on Computational Intelligence (SSCI). :1976-1981.

Brain Computer Interface (BCI) aims at providing a better quality of life to people suffering from neuromuscular disability. This paper establishes a BCI paradigm to provide a biometric security option, used for locking and unlocking personal computers or mobile phones. Although it is primarily meant for the people with neurological disorder, its application can safely be extended for the use of normal people. The proposed scheme decodes the electroencephalogram signals liberated by the brain of the subjects, when they are engaged in selecting a sequence of dots in(6×6)2-dimensional array, representing a pattern lock. The subject, while selecting the right dot in a row, would yield a P300 signal, which is decoded later by the brain-computer interface system to understand the subject's intention. In case the right dots in all the 6 rows are correctly selected, the subject would yield P300 signals six times, which on being decoded by a BCI system would allow the subject to access the system. Because of intra-subjective variation in the amplitude and wave-shape of the P300 signal, a type 2 fuzzy classifier has been employed to classify the presence/absence of the P300 signal in the desired window. A comparison of performances of the proposed classifier with others is also included. The functionality of the proposed system has been validated using the training instances generated for 30 subjects. Experimental results confirm that the classification accuracy for the present scheme is above 90% irrespective of subjects.