publications

2022

1. CASE

   Congestion-Aware Routing for Multi-Class Mobility-on-Demand Service

   Niharika Shrivastava, and Malika Meghjani

   IEEE 18th International Conference on Automation Science and Engineering (CASE), 2022

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   Urban mobility solutions such as mobility-ondemand services have become prevalent given the convenience of door-to-door transport. However, a majority of these approaches are user-centric greedy solutions that cause traffic congestion. We propose a near social-optimal routing algorithm which accounts for the overall network traffic congestion. Specifically, we leverage on multi-class mobility options to dissipate traffic congestion while maintaining near social optimal travel time efficiency. We divide each route into three parts with micro-mobility options such as walking or cycling for the first and last parts and on-demand cars for the middle part of the route. In addition, we propose a computational and travel time efficient transit point search algorithm for switching between different modes of travel. We validate our approach by using a diverse set of road networks from different cities. We achieve an average of 84% increase in network utilization by susing our proposed multi-class social model compared to singleclass user-centric approach. Our proposed transit point search algorithm is on average 68% more computationally efficient with an insignificant maximum average travel time delay of less than 5 seconds compared to an optimal exhaustive routing solution.

   @article{congestion,
     title = {Congestion-Aware Routing for Multi-Class Mobility-on-Demand Service},
     author = {Shrivastava, Niharika and Meghjani, Malika},
     journal = {IEEE 18th International Conference on Automation Science and Engineering (CASE)},
     pages = {2093--2099},
     year = {2022},
     doi = {10.1109/CASE49997.2022.9926619},
     publisher = {IEEE},
     dimensions = {true},
   }

2020

1. IRJET

   Intrusion Detection and Attack Classification using an Ensemble Approach

   G. Ganapathy, N. Shrivastava, O.P. Vyas, and 3 more authors

   International Research Journal of Engineering and Technology (IRJET), Oct 2020

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   The challenges to ensure safe and trusted communication of information between various organizations have increased multifold in recent past. Intrusion Detection Systems such as firewall, message encryption and other approaches are being employed with partial success, however the risks and chances of malicious intrusions are still posing a threat. We are proposing to make use of recent advancements in the field of machine learning to develop an intrusion detection system. In our work, the machine learning classifiers namely, random forest, decision table, multi-layer perceptron and naive bayes were used in an ensemble model showing a significant improvement in the overall accuracy. The proposed approach was implemented using a bench-marking dataset from KDDCup.

   @article{intrusion,
     title = {Intrusion Detection and Attack Classification using an Ensemble Approach},
     author = {Ganapathy, G. and Shrivastava, N. and Vyas, O.P. and Singh, M. and Arora, R. and Mishra, S.},
     journal = {International Research Journal of Engineering and Technology (IRJET)},
     volume = {7},
     issue = {10},
     pages = {1616--1620},
     year = {2020},
     month = oct,
     dimensions = {true},
   }