S
Sai Sree Laya Chukkapalli
Researcher at University of Maryland, Baltimore County
Publications - 16
Citations - 226
Sai Sree Laya Chukkapalli is an academic researcher from University of Maryland, Baltimore County. The author has contributed to research in topics: Computer science & Artificial neural network. The author has an hindex of 6, co-authored 14 publications receiving 75 citations. Previous affiliations of Sai Sree Laya Chukkapalli include University of Maryland, College Park.
Papers
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Proceedings ArticleDOI
Cyber Attacks on Smart Farming Infrastructure
Sina Sontowski,Maanak Gupta,Sai Sree Laya Chukkapalli,Mahmoud Abdelsalam,Sudip Mittal,Anupam Joshi,Ravi Sandhu +6 more
TL;DR: A Denial of Service (DoS) attack that can hinder the functionality of a smart farm by disrupting deployed on-field sensors is demonstrated and a Wi-Fi deauthentication attack that exploits IEEE 802.11 vulnerabilities is discussed, where the management frames are not encrypted.
Journal ArticleDOI
Ontologies and Artificial Intelligence Systems for the Cooperative Smart Farming Ecosystem
Sai Sree Laya Chukkapalli,Sudip Mittal,Maanak Gupta,Mahmoud Abdelsalam,Anupam Joshi,Ravi Sandhu,Karuna Pande Joshi +6 more
TL;DR: A connected cooperative ecosystem which defines sensors and their communication among different entities along with cloud supported co-operative hub and develops member farm and co-op ontologies to capture data and various interactions that happen between shared resources, member farms, and the co-ops that are stored in the cloud.
Proceedings ArticleDOI
A Smart-Farming Ontology for Attribute Based Access Control
TL;DR: A smart farming ontology is developed that helps represent various physical entities like sensors, workers on the farm, and their interactions with each other and an Attribute Based Access Control (ABAC) system is implemented to dynamically evaluate access control requests.
Proceedings ArticleDOI
Context Sensitive Access Control in Smart Home Environments
Sofia Dutta,Sai Sree Laya Chukkapalli,Madhura Sulgekar,Swathi Krithivasan,Prajit Kumar Das,Anupam Joshi +5 more
TL;DR: The creation of the PALS system is proposed, that builds upon existing work in attribute based access control model, captures physical context collected from sensed data (attributes), and performs dynamic reasoning over these attributes and context driven policies using Semantic Web technologies to execute access control decisions.
Posted Content
NAttack! Adversarial Attacks to bypass a GAN based classifier trained to detect Network intrusion
TL;DR: This paper proposes a Generative Adversarial Network (GAN) based algorithm to generate data to train an efficient neural network based classifier, and it is shown that even if a classifier is built and trained with adversarial examples, it can be broken using adversarial attacks and successfully break the system.