Dimensionality Reduction for Intrusion Detection Systems in Multi-data Streams—A Review and Proposal of Unsupervised Feature Selection Scheme

Almusallam, Naif; Tari, Zahir; Bertók, Péter; Zomaya, Albert Y.

doi:10.1007/978-3-319-46376-6_22

Cited by 15 publications

(8 citation statements)

References 76 publications

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“…This is based on the experiments of Eskin et al [52], in which anomalies usually form small clusters in sparse areas of a feature space. Moreover, dense and large clusters usually contain benign data [21]. For the sparsity of AE, we first observe the average output activation value of a neuron i, as expressed by Eq.…”

Section: Methodsmentioning

confidence: 99%

“…Puthran et al [20] also worked on relevant features in the KDD'99 Dataset and improved the decision tree by using binary and quad splits. Almusallam et al [21] leveraged a filter-based feature selection method. Zaman and Karray [22] categorized IDSs based on the Transmission Control Protocol/Internet Protocol (TCP/IP) network model using a feature selection method known as the Enhanced Support Vector Decision Function (ESVDF).…”

Section: Related Workmentioning

confidence: 99%

“…The ratio between the normal and attack instances is 10:1 for both unbalanced training and the test dataset as shown in Table I. This property might be biased to the training model and affect the model performance [21] [64]. To alleviate this, we balance the dataset by selecting 10% of the normal instances randomly.…”

Section: A Dataset Pre-processingmentioning

confidence: 99%

See 2 more Smart Citations

Deep Abstraction and Weighted Feature Selection for Wi-Fi Impersonation Detection

Aminanto

Choi

Tanuwidjaja

et al. 2018

IEEE Trans.Inform.Forensic Secur.

185

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The recent advances in mobile technologies have resulted in IoT-enabled devices becoming more pervasive and integrated into our daily lives. The security challenges that need to be overcome mainly stem from the open nature of a wireless medium such as a Wi-Fi network. An impersonation attack is an attack in which an adversary is disguised as a legitimate party in a system or communications protocol. The connected devices are pervasive, generating high-dimensional data on a large scale, which complicates simultaneous detections. Feature learning, however, can circumvent the potential problems that could be caused by the large-volume nature of network data. This study thus proposes a novel Deep-Feature Extraction and Selection (D-FES), which combines stacked feature extraction and weighted feature selection. The stacked autoencoding is capable of providing representations that are more meaningful by reconstructing the relevant information from its raw inputs. We then combine this with modified weighted feature selection inspired by an existing shallow-structured machine learner. We finally demonstrate the ability of the condensed set of features to reduce the bias of a machine learner model as well as the computational complexity. Our experimental results on a well-referenced Wi-Fi network benchmark dataset, namely, the Aegean Wi-Fi Intrusion Dataset (AWID), prove the usefulness and the utility of the proposed D-FES by achieving a detection accuracy of 99.918% and a false alarm rate of 0.012%, which is the most accurate detection of impersonation attacks reported in the literature.

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Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Deep Abstraction and Weighted Feature Selection for Wi-Fi Impersonation Detection

Aminanto

Choi

Tanuwidjaja

et al. 2018

IEEE Trans.Inform.Forensic Secur.

185

View full text Add to dashboard Cite

show abstract

“…To improve the performance of intrusion detection scheme, another algorithm based on dimensionality reduction for new feature learning using PCA is presented in [264] [265]. Almusallam et al [266] have reviewed the dimensionality reduction schemes for intrusion detection in multimedia traffic and proposed an unsupervised feature selection scheme based on the dimensionality reduced multimedia data.…”

Section: Dimensionality Reduction and Visualizationmentioning

confidence: 99%

Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges

et al. 2019

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While machine learning and artificial intelligence have long been applied in networking research, the bulk of such works has focused on supervised learning. Recently there has been a rising trend of employing unsupervised machine learning using unstructured raw network data to improve network performance and provide services such as traffic engineering, anomaly detection, Internet traffic classification, and quality of service optimization. The interest in applying unsupervised learning techniques in networking emerges from their great success in other fields such as computer vision, natural language processing, speech recognition, and optimal control (e.g., for developing autonomous self-driving cars). Unsupervised learning is interesting since it can unconstrain us from the need of labeled data and manual handcrafted feature engineering thereby facilitating flexible, general, and automated methods of machine learning. The focus of this survey paper is to provide an overview of the applications of unsupervised learning in the domain of networking. We provide a comprehensive survey highlighting the recent advancements in unsupervised learning techniques and describe their applications for various learning tasks in the context of networking. We also provide a discussion on future directions and open research issues, while also identifying potential pitfalls. While a few survey papers focusing on the applications of machine learning in networking have previously been published, a survey of similar scope and breadth is missing in literature. Through this paper, we advance the state of knowledge by carefully synthesizing the insights from these survey papers while also providing contemporary coverage of recent advances.

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“…2 Stepwise procedure of D-FES with two target classes: normal and impersonation attack reduced "CLS" data are a good representation of a real network, in which normal instances significantly outnumber attack instances. This property might be biased to the training model and affect the model performance [59], [60]. To alleviate this, the dataset was balanced by selecting 10% of the normal instances randomly.…”

Section: Wi-fi Datasetmentioning

confidence: 99%

Intrusion Detection System Using Deep Learning and Its Application to Wi-Fi Network

Kim

2020

IEICE Trans. Inf. & Syst.

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Deep learning is gaining more and more lots of attractions and better performance in implementing the Intrusion Detection System (IDS), especially for feature learning. This paper presents the state-of-theart advances and challenges in IDS using deep learning models, which have been achieved the big performance enhancements in the field of computer vision, natural language processing, and image/audio processing than the traditional methods. After providing a systematic and methodical description of the latest developments in deep learning from the points of the deployed architectures and techniques, we suggest the pros-and-cons of all the deep learning-based IDS, and discuss the importance of deep learning models as feature learning approach. For this, the author has suggested the concept of the Deep-Feature Extraction and Selection (D-FES). By combining the stacked feature extraction and the weighted feature selection for D-FES, our experiment was verified to get the best performance of detection rate, 99.918% and false alarm rate, 0.012% to detect the impersonation attacks in Wi-Fi network which can be achieved better than the previous publications. Summary and further challenges are suggested as a concluding remark.

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Dimensionality Reduction for Intrusion Detection Systems in Multi-data Streams—A Review and Proposal of Unsupervised Feature Selection Scheme

Cited by 15 publications

References 76 publications

Deep Abstraction and Weighted Feature Selection for Wi-Fi Impersonation Detection

Deep Abstraction and Weighted Feature Selection for Wi-Fi Impersonation Detection

Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges

Intrusion Detection System Using Deep Learning and Its Application to Wi-Fi Network

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