More than 75% of Internet traffic is now encrypted, and this percentage is constantly increasing. The majority of communications are secured using common encryption protocols such as SSL/TLS and IPsec to ensure security and protect the privacy of Internet users. However, encryption can be exploited to hide malicious activities, camouflaged into normal network traffic. Traditionally, network traffic inspection is based on techniques like deep packet inspection (DPI). Common applications for DPI include but are not limited to firewalls, intrusion detection and prevention systems, L7 filtering, and packet forwarding. With the widespread adoption of network encryption though, DPI tools that rely on packet payload content are becoming less effective, demanding the development of more sophisticated techniques in order to adapt to current network encryption trends. In this work, we present HeaderHunter, a fast signature-based intrusion detection system even for encrypted network traffic. We generate signatures using only network packet metadata extracted from packet headers. In addition, we examine the processing acceleration of the intrusion detection engine using different heterogeneous hardware architectures
", "languages": [ { "id": "eng", "title": { "en": "English" } } ], "publication_date": "2021-02-01", "publisher": "Zenodo", "resource_type": { "id": "publication-article", "title": { "de": "Zeitschriftenartikel", "en": "Journal article" } }, "rights": [ { "description": { "en": "The Creative Commons Attribution license allows re-distribution and re-use of a licensed work on the condition that the creator is appropriately credited." }, "icon": "cc-by-icon", "id": "cc-by-4.0", "props": { "scheme": "spdx", "url": "https://creativecommons.org/licenses/by/4.0/legalcode" }, "title": { "en": "Creative Commons Attribution 4.0 International" } } ], "subjects": [ { "subject": "network intrusion detection" }, { "subject": "encrypted network traffic" }, { "subject": "encrypted network traffic inspection" }, { "subject": "network packet metadata" }, { "subject": "GPGPUs" }, { "subject": "integrated GPU" }, { "subject": "OpenCL" } ], "title": "Acceleration of Intrusion Detection in Encrypted Network Traffic Using Heterogeneous Hardware" }, "parent": { "access": { "owned_by": { "user": "368574" } }, "communities": { "default": "62fd7212-8ef9-45f8-bb3c-73428889cb40", "entries": [ { "access": { "member_policy": "open", "members_visibility": "public", "record_submission_policy": "open", "review_policy": "open", "visibility": "public" }, "children": { "allow": false }, "created": "2021-01-06T12:42:15.226756+00:00", "custom_fields": {}, "deletion_status": { "is_deleted": false, "status": "P" }, "id": "62fd7212-8ef9-45f8-bb3c-73428889cb40", "links": {}, "metadata": { "curation_policy": "", "page": "The C4IIoT project is funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 833828.
\r\n\r\nC4IIoT will build and demonstrate a novel and unified IIoT cybersecurity framework for malicious and anomalous behavior anticipation, detection, mitigation, and end-user informing. The framework provides a holistic and disruptive security-enabling solution for minimizing attack surfaces in IIoT systems, by exploiting i) emerging security software and hardware protection mechanisms; ii) state of the art machine and deep learning and privacy-aware analytics; iii) novel encrypted network flow analysis; iv) secure-by-design IIoT device fabrication; and v) blockchain technologies, to provide a viable scheme for enabling security and accountability, preserving privacy, enabling reliability and assuring trustworthiness within IIoT applications. The C4IIoT framework will be demonstrated and validated on two carefully selected use cases in real world environments, namely Enabling security IIoT in i) Inbound Logistics and ii) a Smart Factory
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https://www.c4iiot.eu
", "title": "H2020 C4IIoT project - Cyber security 4.0: protecting the Industrial Internet Of Things" }, "revision_id": 0, "slug": "c4iiot", "updated": "2022-06-01T09:38:32.320080+00:00" } ], "ids": [ "62fd7212-8ef9-45f8-bb3c-73428889cb40" ] }, "id": "6787350", "pids": { "doi": { "client": "datacite", "identifier": "10.5281/zenodo.6787350", "provider": "datacite" } } }, "pids": { "doi": { "client": "datacite", "identifier": "10.5281/zenodo.6787351", "provider": "datacite" }, "oai": { "identifier": "oai:zenodo.org:6787351", "provider": "oai" } }, "revision_id": 7, "stats": { "all_versions": { "data_volume": 310315262.0, "downloads": 146, "unique_downloads": 146, "unique_views": 161, "views": 168 }, "this_version": { "data_volume": 310315262.0, "downloads": 146, "unique_downloads": 146, "unique_views": 161, "views": 168 } }, "status": "published", "swh": {}, "updated": "2024-07-16T12:24:27.236912+00:00", "versions": { "index": 1, "is_latest": true } }