Computational intelligence techniques for automatic detection of Wi-Fi attacks in wireless IoT networks

Nivaashini, M.; Thangaraj, P.

doi:10.1007/s11276-021-02594-2

Cited by 15 publications

(10 citation statements)

References 76 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Security in WiFi is also a relevant concern addressed in surveys [15], [23]. Considering that WiFi is ranked as the most deployed wireless technology, numerous attacks exploiting its vulnerabilities have been observed.…”

Section: A Wifi-related Surveysmentioning

confidence: 99%

“…Addressed WiFi feature Year WiFi [17] Large-scale network monitoring WiFi analytics 2020 [18] Quality indicators accounting for user satisfaction WiFi quality indicators 2020 [14] Indoor localization Application-oriented 2020 [19] 2019 [20] 2019 [21] 2018 [22] Human activity detection 2017 [15] Intrusion detection WiFi security 2021 [23] security issues. In the wireless networks area, the surveys in general provide few details concerning the use of ML models to improve performance of the 802.11 protocol family.…”

Section: Network Ref Main Scopementioning

confidence: 99%

“…Next, we briefly describe the datasets from the reviewed papers that were made available by the researchers on their individual webpages. Herzen et al [68] provided a dataset used to predict throughput based on basic performance metrics (e.g., received power, channel width) collected in a small testbed 15 . Cell vs. WiFi 16 is a publicly available dataset based on an Android application that collects packet-level traces of TCP downlink and uplink traffic between a mobile device and a server for both WiFi and cellular networks.…”

Section: B Datasetsmentioning

confidence: 99%

“…There has been broad adoption of ML in these areas and they deserve literature reviews of their own, such as [14], [15]. Furthermore, our survey does not describe how various ML methods operate.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Wi-Fi Meets ML: A Survey on Improving IEEE 802.11 Performance with Machine Learning

Szott,

Kosek-Szott,

Gawłowicz

et al. 2021

Preprint

View full text Add to dashboard Cite

Wireless local area networks (WLANs) empowered by IEEE 802.11 (WiFi) hold a dominant position in providing Internet access thanks to their freedom of deployment and configuration as well as affordable and highly interoperable devices. The WiFi community is currently deploying WiFi 6 and developing WiFi 7, which will bring higher data rates, better multi-user and multi-AP support, and, most importantly, improved configuration flexibility. These technical innovations, including the plethora of configuration parameters, are making next-generation WLANs exceedingly complex as the dependencies between parameters and their joint optimization usually have a non-linear impact on network performance. The complexity is further increased in the case of dense deployments and coexistence in shared bands. While classic optimization approaches fail in such conditions, machine learning (ML) is well known for being able to handle complexity. Much research has been published on using ML to improve WiFi performance and solutions are slowly being adopted in existing deployments. In this survey, we adopt a structured approach to describing the various areas where WiFi can be enhanced using ML. To this end, we analyze over 200 papers in the field providing readers with an overview of the main trends. Based on this review, we identify both open challenges in each WiFi performance area as well as general future research directions.

show abstract

Section: A Wifi-related Surveysmentioning

confidence: 99%

Section: Network Ref Main Scopementioning

confidence: 99%

Section: B Datasetsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Wi-Fi Meets ML: A Survey on Improving IEEE 802.11 Performance with Machine Learning

Szott,

Kosek-Szott,

Gawłowicz

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…L ATELY, the Internet of Things (IoT) has developed very fast [1]. One particular characteristic of IoT devices is widely interconnected through a wireless network with limited power and storage resources [2]. That being the case, information security issues have become more prominent and need to be addressed seriously.…”

Section: Introductionmentioning

confidence: 99%

Multi-Class Intrusion Detection Using Two-Channel Color Mapping in IEEE 802.11 Wireless Network

et al. 2022

View full text Add to dashboard Cite

The rise of interconnected devices through wireless networks provides two sides consequences. On one side, it helps many human tasks; on the other hand, the prone wireless medium opens the vulnerable system to be exploited by adversaries. An Intrusion Detection System (IDS) is one method to inspect the network traffic by leveraging state-of-the-art anomaly detection techniques. Deep learning models have been utilized to distinguish the benign and malicious traffic. However, projecting the tabular data into images before the image classification has been the main challenge of leveraging deep learning for IDS purposes. We propose the novel projection of tabular data into 2-coded color mapping for IDS purposes. The proposed method employs a feature selection method to ensure optimal dimensionality. We examined the different number of attribute subsets to obtain the relationship between the attributes. Furthermore, it takes advantage of the Convolutional Neural Network (CNN) model to classify the Wi-Fi attacks. We evaluate the proposed model using the most common Wi-Fi attacks dataset, Aegean Wi-Fi Intrusion Dataset (AWID2). The proposed method achieved an F1 score of 99.73% and a false positive rate of 0.24%. This study highlights the importance of addressing the mapping procedures from tabular data into grid-based data before deep learning training and validates the effectiveness of CNN to detect multiple types of wireless network attacks.

show abstract

Lightweight real-time WiFi-based intrusion detection system using LightGBM

Bhutta,

Nisa,

Mian

2023

Wireless Netw

View full text Add to dashboard Cite

Computational intelligence techniques for automatic detection of Wi-Fi attacks in wireless IoT networks

Cited by 15 publications

References 76 publications

Wi-Fi Meets ML: A Survey on Improving IEEE 802.11 Performance with Machine Learning

Wi-Fi Meets ML: A Survey on Improving IEEE 802.11 Performance with Machine Learning

Multi-Class Intrusion Detection Using Two-Channel Color Mapping in IEEE 802.11 Wireless Network

Lightweight real-time WiFi-based intrusion detection system using LightGBM

Contact Info

Product

Resources

About