Anomaly Detection for Mixed Packet Sequences

Meghdouri, Fares; Vazquez, Felix Iglesias; Zseby, Tanja

doi:10.1109/lcnsymposium50271.2020.9363264

Cited by 2 publications

(2 citation statements)

References 37 publications

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“…Torroledo et al [90] proposed a deep learning based identification system to detect legitimate and malicious TLS traffic certificates by utilizing the TLS certificate content. Meghdouri et al [80] proposed an RF detection model for TLS and Internet Protocol Security (IPSec) protocols with novel cross-layer features, which comprise of features in the application level, conversation level and endpoint behaviour level at the same time.…”

Section: A Research Objectivementioning

confidence: 99%

“…On supervised learning, Meghdouri et al [80] proposed an RF classification model based on a multi-key based approach, which is a novel cross-layer feature representation of traffic data under TLS and IPSec protocols. They tested the model using three different datasets, CICIDS-2017 [54], UNSW0-NB15 [22] and ISCX-bot-2014 [72], which achieved 100%, 92.6% and 99.2% F1 scores, respectively.…”

Section: E Algorithms Selectionmentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning for Encrypted Malicious Traffic Detection: Approaches, Datasets and Comparative Study

Wang,

Fok,

Thing

2022

Preprint

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As people's demand for personal privacy and data security becomes a priority, encrypted traffic has become mainstream in the cyber world. However, traffic encryption is also shielding malicious and illegal traffic introduced by adversaries, from being detected. This is especially so in the post-COVID-19 environment where malicious traffic encryption is growing rapidly. Common security solutions that rely on plain payload content analysis such as deep packet inspection are rendered useless. Thus, machine learning based approaches have become an important direction for encrypted malicious traffic detection. In this paper, we formulate a universal framework of machine learning based encrypted malicious traffic detection techniques and provided a systematic review. Furthermore, current research adopts different datasets to train their models due to the lack of well-recognized datasets and feature sets. As a result, their model performance cannot be compared and analyzed reliably. Therefore, in this paper, we analyse, process and combine datasets from 5 different sources to generate a comprehensive and fair dataset to aid future research in this field. On this basis, we also implement and compare 10 encrypted malicious traffic detection algorithms. We then discuss challenges and propose future directions of research.

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Section: A Research Objectivementioning

confidence: 99%