Big Data Analytics for User-Activity Analysis and User-Anomaly Detection in Mobile Wireless Network

Parwez, Salik; Rawat, Danda B.; Garuba, Moses

doi:10.1109/tii.2017.2650206

Cited by 173 publications

(133 citation statements)

References 12 publications

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“…Parwez et al [81] K-means & Hierarchical Clustering K-means and hierarchical clustering is used to detect anomalies in call detail records of mobile wireless networks data. Lorido et al [82] GMM GMM is used for detecting the anomalies that are affecting resources in cloud data centers.…”

Section: Anomaly/intrusion Detectionmentioning

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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Section: Anomaly/intrusion Detectionmentioning

confidence: 99%

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

et al. 2019

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“…-Case-a: we first fit the points in the geodesic with a cubic smoothing splinef (z) where z ∈ [0, 1] according to Eqn. (6) and Eqn. (7).…”

Section: Smoothing Geodesics Embeddingmentioning

confidence: 99%

“…Note that, a cubic smoothing spline is represented by θ = 2 in Eqn. (6). We discretize this spline into h segments z k1 = (k 1 − 1)/(h − 1); k 1 = 1, .…”

Section: Smoothing Geodesics Embeddingmentioning

confidence: 99%

A nonlinear dimensionality reduction framework using smooth geodesics

Gajamannage

Paffenroth

Bollt

2019

Pattern Recognition

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Existing dimensionality reduction methods are adept at revealing hidden underlying manifolds arising from high-dimensional data and thereby producing a low-dimensional representation. However, the smoothness of the manifolds produced by classic techniques over sparse and noisy data is not guaranteed. In fact, the embedding generated using such data may distort the geometry of the manifold and thereby produce an unfaithful embedding. Herein, we propose a framework for nonlinear dimensionality reduction that generates a manifold in terms of smooth geodesics that is designed to treat problems in which manifold measurements are either sparse or corrupted by noise. Our method generates a network structure for given high-dimensional data using a nearest neighbors search and then produces piecewise linear shortest paths that are defined as geodesics. Then, we fit points in each geodesic by a smoothing spline to emphasize the smoothness. The robustness of this approach for sparse and noisy datasets is demonstrated by the implementation of the method on synthetic and real-world datasets.

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“…For example, Traffica introduces itself as a real-time traffic monitoring tool that analyzes user behavior to gain network insights, similar approaches were presented in academia by the authors of [69,109] . The Wireless Network Guardian detects user anomalies in mobile networks where a comparable topic was discussed in [110] . Preventive Complaint Analysis makes use of big data analytics to detect behavioral anomalies in mobile network elements where the authors in [111] provided a similar approach.…”

Section: Big Data Analytics In the Industrymentioning

confidence: 99%

“…Preventive Complaint Analysis makes use of big data analytics to detect behavioral anomalies in mobile network elements where the authors in [111] provided a similar approach. Predictive Care utilizes big data analytics to identify anomalies in network elements before affecting the user, a comparable academic approach is presented in [110,112] . HP presented Vertica , a solution that exploits CDRs for network planning, optimization, and fault prediction purposes.…”

Section: Big Data Analytics In the Industrymentioning

confidence: 99%

Big data analytics for wireless and wired network design: A survey

et al. 2018

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a b s t r a c tCurrently, the world is witnessing a mounting avalanche of data due to the increasing number of mobile network subscribers, Internet websites, and online services. This trend is continuing to develop in a quick and diverse manner in the form of big data. Big data analytics can process large amounts of raw data and extract useful, smaller-sized information, which can be used by different parties to make reliable decisions.In this paper, we conduct a survey on the role that big data analytics can play in the design of data communication networks. Integrating the latest advances that employ big data analytics with the networks' control/traffic layers might be the best way to build robust data communication networks with refined performance and intelligent features. First, the survey starts with the introduction of the big data basic concepts, framework, and characteristics. Second, we illustrate the main network design cycle employing big data analytics. This cycle represents the umbrella concept that unifies the surveyed topics. Third, there is a detailed review of the current academic and industrial effort s toward network design using big data analytics. Forth, we identify the challenges confronting the utilization of big data analytics in network design. Finally, we highlight several future research directions. To the best of our knowledge, this is the first survey that addresses the use of big data analytics techniques for the design of a broad range of networks.

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Big Data Analytics for User-Activity Analysis and User-Anomaly Detection in Mobile Wireless Network

Cited by 173 publications

References 12 publications

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

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

A nonlinear dimensionality reduction framework using smooth geodesics

Big data analytics for wireless and wired network design: A survey

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