Browsing by Author "Gupta, Maanak"
Now showing 1 - 6 of 6
Results Per Page
Sort Options
Item Cyber Attacks on Smart Farming Infrastructure(IEEE, 2021-01-20) Sontowski, Sina; Gupta, Maanak; Chukkapalli, Sai Sree Laya; Abdelsalam, Mahmoud; Mittal, Sudip; Joshi, Anupam; Sandhu, RaviSmart farming also known as precision agriculture is gaining more traction for its promising potential to fulfill increasing global food demand and supply. In a smart farm, technologies and connected devices are used in a variety of ways, from finding the real-time status of crops and soil moisture content to deploying drones to assist with tasks such as applying pesticide spray. However, the use of heterogeneous internet-connected devices has introduced numerous vulnerabilities within the smart farm ecosystem. Attackers can exploit these vulnerabilities to remotely control and disrupt data flowing from/to on-field sensors and autonomous vehicles like smart tractors and drones. This can cause devastating consequences especially during a high-risk time, such as harvesting, where live-monitoring is critical. In this paper, we demonstrate a Denial of Service (DoS) attack that can hinder the functionality of a smart farm by disrupting deployed on-field sensors. In particular, we discuss a Wi-Fi deauthentication attack that exploits IEEE 802.11 vulnerabilities, where the management frames are not encrypted. A MakerFocus ESP8266 Development Board WiFiDeauther Monster is used to detach the connected Raspberry Pi from the network and prevent sensor data from being sent to the remote cloud. Additionally, this attack was expanded to include the entire network, obstructing all devices from connecting to the network. To this end, we urge practitioners to be aware of current vulnerabilities when deploying smart farming ecosystems and encourage the cybersecurity community to further investigate the domain-specific characteristics of smart farming.Item Knowledge Enrichment by Fusing Representations for Malware Threat Intelligence and Behavior(IEEE) Piplai, Aritran; Mittal, Sudip; Abdelsalam, Mahmoud; Gupta, Maanak; Joshi, Anupam; Finin, TimSecurity engineers and researchers use their disparate knowledge and discretion to identify malware present in a system. Sometimes, they may also use previously extracted knowledge and available Cyber Threat Intelligence (CTI), about known attacks to establish a pattern. To aid in this process, they need knowledge about malware behavior mapped to the available CTI. Such mappings enrich our CKG and also helps in the verification of the information. In this paper, we retrieve malware samples and execute them in a local system. The tracked malware behavior is represented in our Cybersecurity Knowledge Graph (CKG), so that a security professional can reason with behavioral information present in the knowledge graph, draw parallels with that information. We also merge the behavioral information with knowledge extracted from CTI sources like technical reports and blogs about the same malware so that we can significantly improve the reasoning capabilities of our CKG.Item Ontologies and Artificial Intelligence Systems for the Cooperative Smart Farming Ecosystem(IEEE, 2020-09-08) Chukkapalli, Sai Sree Laya; Mittal, Sudip; Gupta, Maanak; Abdelsalam, Mahmoud; Joshi, Anupam; Sandhu, Ravi; Joshi, KarunaCyber-Physical Systems (CPS) and Internet of Thing (IoT) generate large amounts of data spurring the rise of Artificial Intelligence (AI) based smart applications. Driven by rapid advancements in technologies that support smart devices, agriculture and farming sector is shifting towards IoT connected ecosystem to balance the increase in demand for food supply. As the number of smart farms reach critical mass, it is now possible to include AI assisted systems at a cooperative (co-op) farming level. Today, in the United States alone there are about 1,871 co-ops serving 1,890,057 member farmers. Hence, such advanced technologies and infrastructure when incorporated in the co-op farming ecosystem can immensely benefit small member farmers who operate and maintain these independent co-op entities. In this paper, we develop a connected cooperative ecosystem which defines sensors and their communication among different entities along with cloud supported co-op hub. We develop member farm and co-op ontologies to capture data and various interactions that happen between shared resources, member farms, and the co-op that are stored in the cloud. These can then help generate AI supported insights for farmers and the cooperative. Several co-op farming use case scenarios have been discussed to demonstrate the functioning of our smart cooperative ecosystem. Finally, the paper describes various AI applications that can be deployed at the co-op level to aid member farmers.Item Ontology driven AI and Access Control Systems for Smart Fisheries(Association for Computing Machinery, 2021-04-28) Chukkapalli, Sai Sree Laya; Aziz, Shaik; Alotaibi, Nouran; Mittal, Sudip; Gupta, Maanak; Abdelsalam, MahmoudIncreasing number of internet connected devices has paved a path for smarter ecosystems in various sectors such as agriculture, aquaculture, manufacturing, healthcare, etc. Especially, integrating technologies like big data, artificial intelligence (AI), blockchain, etc. with internet connected devices has increased efficiency and productivity. Therefore, fishery farmers have started adopting smart fisheries technologies to better manage their fish farms. Despite their technological advancements smart fisheries are exposed and vulnerable to cyber-attacks that would cause negative impact on the ecosystem both physically and economically. Therefore in this paper, we present a smart fisheries ecosystem where the architecture describes various interactions that happen between internet connected devices. We develop a smart fisheries ontology based on the architecture and implement Attribute Based Access Control System (ABAC) where access to resources of smart fisheries is granted by evaluating the requests. We also discuss how access control decisions are made in multiple use case scenarios of a smart fisheries ecosystem. Furthermore, we elaborate some AI applications that would enhance the smart fisheries ecosystem.Item A Smart-Farming Ontology for Attribute Based Access Control(IEEE, 2020-06-23) Chukkapalli, Sai Sree Laya; Piplai, Aritran; Mittal, Sudip; Gupta, Maanak; Joshi, AnupamWith the advent of smart farming, individual farmers have started adopting the concepts of agriculture 4.0. Modern smart farms leverage technologies like big data, Cyber Physical Systems (CPS), Artificial Intelligence (AI), blockchain, etc. The use of these technologies has left these smart farms susceptible to cyber-attacks. In order to help secure the smart farm ecosystem in this paper, we develop a smart farming ontology. Our ontology helps represent various physical entities like sensors, workers on the farm, and their interactions with each other. Using the expressive ontology we implement an Attribute Based Access Control (ABAC) system to dynamically evaluate access control requests. Furthermore, we discuss various use cases to showcase our access control model in various scenarios on a smart farm.Item YieldPredict: A Crop Yield Prediction Framework for Smart Farms(IEEE, 2020-11-01) Choudhary, Nitu Kedarmal; Chukkapalli, Sai Sree Laya; Mittal, Sudip; Gupta, Maanak; Abdelsalam, Mahmoud; Joshi, AnupamIn recent years, machine learning approaches are gaining popularity with the advent of big data. The massive amount of data generated, when served as an input to machine learning approaches, provides useful insights. Adoption of these approaches in the agricultural sector has immense potential to increase crop productivity and quality. In this paper, we analyze the crop data collected from an agriculture site in Rajasthan, India, that includes both Rabi and Kharif cropping patterns. In addition, we utilize a smart farm ontology that contains concepts and properties related to the agricultural domain. We link the collected data and our smart farm ontology to populate a knowledge graph. We utilize the generated knowledge graph to provide structural information and aggregate data by using SPARQL queries. The aggregated data is further used by our machine learning models to predict the crop yield to benefit farmers and various stakeholders. We also analyze and compare our results obtained for various machine learning models used.