Intrusion Detection in 802.11 Networks: Empirical Evaluation of Threats and a Public Dataset

By Constantinos Kolias, Georgios Kambourakis, Angelos Stavrou, Stefanos Gritzalis. IEEE Communications Surveys and Tutorials, 2016

490 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/5d30653a2937f6b58ed29aa14303d74477090e1c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computer Networks and Communications

Citations in the corpus

Adversarial Machine Learning for Network Intrusion Detection Systems: A Comprehensive Survey (2023, background): “As a result, NIDS developed explicitly for IoT environments are evaluated on a different set of datasets, such as AWID [64], Kitsune [47], Bot-IoT [65], and TON_IoT [66].”
Network Intrusion Detection for IoT Security Based on Learning Techniques (2019, background): “As mentioned in [43], DoS attacks are the most frequent attacks especially in IoT/Fog networks related to social IoT such as smart cities, etc.”
A Review of Intrusion Detection Systems Using Machine and Deep Learning in Internet of Things: Challenges, Solutions and Future Directions (2020, background): “The common attacks in this category are PTW attacks, FMS attack, KoreK Family Attacks, Dictionary Attack and ARP Injection [80].”
Cellular, Wide-Area, and Non-Terrestrial IoT: A Survey on 5G Advances and the Road Toward 6G (2021, methodology): “[321] AE WiFi-connected IoT Attack detection using deep AE and feature abstraction based on WiFi networks database [322]”
A Survey of Network-based Intrusion Detection Data Sets (2019, background): “AWID is a publicly available data set3 which is focused on 802.11 networks.”
Survey of intrusion detection systems: techniques, datasets and challenges (2019, methodology): “Methods used by attackers to escape detection by hiding attacks as legitimate traffic are fragmentation overlap, overwrite, and timeouts (Ptacek & Newsham, 1998; Kolias et al., 2016).”
Benchmarking of Machine Learning for Anomaly Based Intrusion Detection Systems in the CICIDS2017 Dataset (2021, methodology): “Therefore, a weighted feature selection technique is recommended [25] for identifying Wi-Fi impersonation through the AWID dataset [26].”
Explainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Research (2022, background): “[107] AWID 2016 365 A labeled dataset with an emphasis on 802.”
A Survey of Random Forest Based Methods for Intrusion Detection Systems (2018, background): “Additionally, Kumar and Dutta (2016) present a survey for wireless intrusion detection approaches.”
TON_IoT Telemetry Dataset: A New Generation Dataset of IoT and IIoT for Data-Driven Intrusion Detection Systems (2020, methodology): “Backdoor [16] is a passive attack that allows an adversary to gain unauthorised remote access to the infected IIoT devices by a backdoor malware.”
A Survey of Deep Learning Methods for Cyber Security (2019, methodology): “Using the Aegean WiFi Intrusion Dataset (AWID) [156], a comprehensive WiFi network benchmark dataset, Aminanto and Kim [157] trained an unsupervised stacked autoencoder with two hidden layers as a feature extractor, which is fed into a k-means clustering algorithm with two centroids.”
Building an efficient intrusion detection system based on feature selection and ensemble classifier (2019, background): “Aegean WiFi Intrusion Dataset (AWID) dataset AWID was publicly available in 2015 as a collection of sets of WiFi network data, which contain real traces of both normal and intrusive data collected from real network environments [48].”