2021
2.
Nadig, Deepak; Alaoui, Sara El; Ramamurthy, Byrav; Pitla, Santosh
ERGO: A Scalable Edge Computing Architecture for Infrastructureless Agricultural Internet of Things Proceedings Article
In: 2021 IEEE International Symposium on Local and Metropolitan Area Networks (LANMAN), pp. 1–2, 2021, (ISSN: 1944-0375).
Abstract | BibTeX | Tags: Ag-IoT, cloud computing, Computer architecture, Edge Computing, Infrastructureless, Instruments, machine learning, Metropolitan area networks, Performance evaluation, Throughput | Links:
@inproceedings{nadig_ergo_2021,
title = {ERGO: A Scalable Edge Computing Architecture for Infrastructureless Agricultural Internet of Things},
author = {Deepak Nadig and Sara El Alaoui and Byrav Ramamurthy and Santosh Pitla},
url = {https://deepaknadig.com/wp-content/uploads/2021/09/Nadig-et-al.-2021-ERGO-A-Scalable-Edge-Computing-Architecture-for-I.pdf},
doi = {10.1109/LANMAN52105.2021.9478811},
year = {2021},
date = {2021-07-01},
urldate = {2021-07-01},
booktitle = {2021 IEEE International Symposium on Local and Metropolitan Area Networks (LANMAN)},
pages = {1--2},
abstract = {In this paper, we propose ERGO (edge architecture for Ag-IoT), an edge-computing architecture for infrastructureless smart agriculture environments. We also develop Ag-IoT application APIs and the associated microservice infrastructure. Our implementation and evaluations show that ERGO can operate independently of cloud-backed assistance, is highly scalable, modular, and affords composability benefits to Ag-IoT systems. We also demonstrate that ERGO outperforms traditional infrastructure in response latencies and transactional throughput, on average, by over 54% and 77%, respectively.},
note = {ISSN: 1944-0375},
keywords = {Ag-IoT, cloud computing, Computer architecture, Edge Computing, Infrastructureless, Instruments, machine learning, Metropolitan area networks, Performance evaluation, Throughput},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper, we propose ERGO (edge architecture for Ag-IoT), an edge-computing architecture for infrastructureless smart agriculture environments. We also develop Ag-IoT application APIs and the associated microservice infrastructure. Our implementation and evaluations show that ERGO can operate independently of cloud-backed assistance, is highly scalable, modular, and affords composability benefits to Ag-IoT systems. We also demonstrate that ERGO outperforms traditional infrastructure in response latencies and transactional throughput, on average, by over 54% and 77%, respectively.
2019
1.
Nayak, Sampashree; Nadig, Deepak; Ramamurthy, Byrav
Analyzing Malicious URLs using a Threat Intelligence System Proceedings Article
In: 2019 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS), pp. 1–4, 2019, (ISSN: 2153-1684).
Abstract | BibTeX | Tags: k-means clustering, machine learning, threat intelligence feeds, URL analysis | Links:
@inproceedings{nayak_analyzing_2019,
title = {Analyzing Malicious URLs using a Threat Intelligence System},
author = {Sampashree Nayak and Deepak Nadig and Byrav Ramamurthy},
url = {https://deepaknadig.com/wp-content/uploads/2021/09/Nayak-et-al.-2019-Analyzing-Malicious-URLs-using-a-Threat-Intelligen.pdf},
doi = {10.1109/ANTS47819.2019.9118051},
year = {2019},
date = {2019-12-01},
urldate = {2019-12-01},
booktitle = {2019 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS)},
pages = {1--4},
abstract = {Threat intelligence and management systems form a vital component of an organization's cybersecurity infrastructure. Threat intelligence, when used with active monitoring of network traffic, can be critical to ensure reliable data communication between endpoints. Threat intelligence systems are well suited for analyzing anomalous behaviors in network traffic and can be employed to assist organizations in identifying and successfully responding to cyber-attacks. In this paper, we present a machine learning approach for clustering malicious uniform resource locators (URLs). We focus on a URL dataset gathered from a threat intelligence feeds framework. We implement a k-means clustering solution for grouping malicious URLs obtained from open source threat intelligence feeds. We demonstrate the effectiveness of our unsupervised learning technique to discover the hidden structures in the malicious URL dataset. Our URL keyword/text clustering solution provides valuable insights about the malicious URLs and aids network operators in policy decisions to mitigate cyber-attacks. The clusters obtained using our approach has a silhouette coefficient of 0.383 for a dataset containing over 11,000 malicious URLs. Lastly, we develop a probabilistic scoring model to calculate the percentage of malicious keywords present in a given URL. After analyzing over 72,000 malicious keywords, our model successfully identifies over 80% of the URLs in a test dataset as malicious.},
note = {ISSN: 2153-1684},
keywords = {k-means clustering, machine learning, threat intelligence feeds, URL analysis},
pubstate = {published},
tppubtype = {inproceedings}
}
Threat intelligence and management systems form a vital component of an organization's cybersecurity infrastructure. Threat intelligence, when used with active monitoring of network traffic, can be critical to ensure reliable data communication between endpoints. Threat intelligence systems are well suited for analyzing anomalous behaviors in network traffic and can be employed to assist organizations in identifying and successfully responding to cyber-attacks. In this paper, we present a machine learning approach for clustering malicious uniform resource locators (URLs). We focus on a URL dataset gathered from a threat intelligence feeds framework. We implement a k-means clustering solution for grouping malicious URLs obtained from open source threat intelligence feeds. We demonstrate the effectiveness of our unsupervised learning technique to discover the hidden structures in the malicious URL dataset. Our URL keyword/text clustering solution provides valuable insights about the malicious URLs and aids network operators in policy decisions to mitigate cyber-attacks. The clusters obtained using our approach has a silhouette coefficient of 0.383 for a dataset containing over 11,000 malicious URLs. Lastly, we develop a probabilistic scoring model to calculate the percentage of malicious keywords present in a given URL. After analyzing over 72,000 malicious keywords, our model successfully identifies over 80% of the URLs in a test dataset as malicious.