FloRa: Flow Table Low-Rate Overflow Reconnaissance and Detection in SDN Article Swipe
Ankur Mudgal
,
Abhishek Verma
,
Munesh Singh
,
Kshira Sagar Sahoo
,
Erik Elmroth
,
Monowar Bhuyan
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/tnsm.2024.3446178
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/tnsm.2024.3446178
Software Defined Networking (SDN) has evolved to revolutionize next-generation networks, offering programmability for on-the-fly service provisioning, primarily supported by the OpenFlow (OF) protocol. The limited storage capacity of Ternary Content Addressable Memory (TCAM) for storing flow tables in OF switches introduces vulnerabilities, notably the Low-Rate Flow Table Overflow (LOFT) attacks. LOFT exploits the flow table's storage capacity by occupying a substantial amount of space with malicious flow, leading to a gradual degradation in the flow-forwarding performance of OF switches. To mitigate this threat, we propose FloRa, a machine learning-based solution designed for monitoring and detecting LOFT attacks in SDN. FloRa continuously examines and determines the status of the flow table by closely examining the features of the flow table entries. Upon detecting an attack FloRa promptly activates the detection module. The module monitors flow properties, identifies malicious flows, and blacklists them, facilitating their eviction from the flow table. Incorporating novel features such as Packet Arrival Frequency, Content Relevance Score, and Possible Spoofed IP along with Cat Boost employed as the attack detection method. The proposed method reduces CPU overhead, memory overhead, and classification latency significantly and achieves a detection accuracy of 99.49%, which is more than the state-of-the-art methods to the best of our knowledge. This approach not only protects the integrity of the flow tables but also guarantees the uninterrupted flow of legitimate traffic. Experimental results indicate the effectiveness of FloRa in LOFT attack detection, ensuring uninterrupted data forwarding and continuous availability of flow table resources in SDN.
Related Topics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/tnsm.2024.3446178
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- green
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All OpenAlex metadata
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https://openalex.org/W4402159133Canonical identifier for this work in OpenAlex
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https://doi.org/10.1109/tnsm.2024.3446178Digital Object Identifier
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FloRa: Flow Table Low-Rate Overflow Reconnaissance and Detection in SDNWork title
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articleOpenAlex work type
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enPrimary language
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2024Year of publication
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2024-08-20Full publication date if available
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Ankur Mudgal, Abhishek Verma, Munesh Singh, Kshira Sagar Sahoo, Erik Elmroth, Monowar BhuyanList of authors in order
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https://doi.org/10.1109/tnsm.2024.3446178Publisher landing page
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2410.19832Direct OA link when available
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Computer science, Table (database), Computer network, Computer security, Data miningTop concepts (fields/topics) attached by OpenAlex
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16Total citation count in OpenAlex
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2025: 15, 2024: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.Packet | 153 |
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| institutions_distinct_count | 6 |
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| citation_normalized_percentile.is_in_top_10_percent | True |