Improving Detection of Wi-Fi Impersonation by Fully Unsupervised Deep Learning

Aminanto, Muhamad Erza; Kim, Kwangjo

doi:10.1007/978-3-319-93563-8_18

Cited by 28 publications

(21 citation statements)

References 14 publications

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“…CICIDS2017 is one of the unique datasets that includes up-to-date attacks. Furthermore, the features are exclusive and matchless in comparison with other datasets such as UNSW-NB15 [30,31], AWID [32], GPRS [33], and CIDD-001 [34]. For this reason, CICIDS2017 was selected as the most comprehensive IDS benchmark to test and validate the proposed ideas.…”

Section: Cicids2017 Datasetmentioning

confidence: 99%

Features Dimensionality Reduction Approaches for Machine Learning Based Network Intrusion Detection

et al. 2019

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The security of networked systems has become a critical universal issue that influences individuals, enterprises and governments. The rate of attacks against networked systems has increased dramatically, and the tactics used by the attackers are continuing to evolve. Intrusion detection is one of the solutions against these attacks. A common and effective approach for designing Intrusion Detection Systems (IDS) is Machine Learning. The performance of an IDS is significantly improved when the features are more discriminative and representative. This study uses two feature dimensionality reduction approaches: (i) Auto-Encoder (AE): an instance of deep learning, for dimensionality reduction, and (ii) Principle Component Analysis (PCA). The resulting low-dimensional features from both techniques are then used to build various classifiers such as Random Forest (RF), Bayesian Network, Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA) for designing an IDS. The experimental findings with low-dimensional features in binary and multi-class classification show better performance in terms of Detection Rate (DR), F-Measure, False Alarm Rate (FAR), and Accuracy. This research effort is able to reduce the CICIDS2017 dataset’s feature dimensions from 81 to 10, while maintaining a high accuracy of 99.6% in multi-class and binary classification. Furthermore, in this paper, we propose a Multi-Class Combined performance metric C o m b i n e d M c with respect to class distribution to compare various multi-class and binary classification systems through incorporating FAR, DR, Accuracy, and class distribution parameters. In addition, we developed a uniform distribution based balancing approach to handle the imbalanced distribution of the minority class instances in the CICIDS2017 network intrusion dataset.

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Section: Cicids2017 Datasetmentioning

confidence: 99%

Features Dimensionality Reduction Approaches for Machine Learning Based Network Intrusion Detection

et al. 2019

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“…ANN) showed high detection accuracy, confirming the potential applications of deep algorithms in securing Wi-Fi networks from impersonation attacks. A similar study [235] used a combination of two unsupervised algorithms (SAE) for mining features and k-means clustering for categorising the input into two classes: benign and malicious.…”

Section: B Network Layermentioning

confidence: 99%

A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security

Al-Garadi

Mohamed

Al-Ali

et al. 2020

IEEE Commun. Surv. Tutorials

838

333

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The Internet of Things (IoT) integrates billions of smart devices that can communicate with one another with minimal human intervention. It is one of the fastest developing fields in the history of computing, with an estimated 50 billion devices by the end of 2020. On the one hand, IoT technologies play a crucial role in enhancing several real-life smart applications that can improve life quality. On the other hand, the crosscutting nature of IoT systems and the multidisciplinary components involved in the deployment of such systems have introduced new security challenges. Implementing security measures, such as encryption, authentication, access control, network security and application security, for IoT devices and their inherent vulnerabilities is ineffective. Therefore, existing security methods should be enhanced to secure the IoT ecosystem effectively. Machine learning and deep learning (ML/DL) have advanced considerably over the last few years, and machine intelligence has transitioned from laboratory curiosity to practical machinery in several important applications. The ability to monitor IoT devices intelligently provides a significant solution to new or zero-day attacks. ML/DL are powerful methods of data exploration for learning about 'normal' and 'abnormal' behaviour according to how IoT components and devices perform within the IoT environment. Consequently, ML/DL methods are important in transforming the security of IoT systems from merely facilitating secure communication between devices to security-based intelligence systems. The goal of this work is to provide a comprehensive survey of ML methods and recent advances in DL methods that can be used to develop enhanced security methods for IoT systems.IoT security threats that are related to inherent or newly introduced threats are presented, and various potential IoT system attack surfaces and the possible threats related to each surface are discussed. We then thoroughly review ML/DL methods for IoT security and present the opportunities, advantages and shortcomings of each method. We discuss the opportunities and challenges involved in applying ML/DL to IoT security. These opportunities and challenges can serve as potential future research directions.

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“…These attacks forge activities to take advantage of others, disguising a malicious device as a legitimate device in a WiFi network. 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. This method proved to be accurate at detecting these impersonation attacks with a detection rate of 92.18%, false alarm rate of 4.40%, and precision of 86.15%.…”

Section: Network Intrusion Detectionmentioning

confidence: 99%

A Survey of Deep Learning Methods for Cyber Security

et al. 2019

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This survey paper describes a literature review of deep learning (DL) methods for cyber security applications. A short tutorial-style description of each DL method is provided, including deep autoencoders, restricted Boltzmann machines, recurrent neural networks, generative adversarial networks, and several others. Then we discuss how each of the DL methods is used for security applications. We cover a broad array of attack types including malware, spam, insider threats, network intrusions, false data injection, and malicious domain names used by botnets.

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Improving Detection of Wi-Fi Impersonation by Fully Unsupervised Deep Learning

Cited by 28 publications

References 14 publications

Features Dimensionality Reduction Approaches for Machine Learning Based Network Intrusion Detection

Features Dimensionality Reduction Approaches for Machine Learning Based Network Intrusion Detection

A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security

A Survey of Deep Learning Methods for Cyber Security

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