A New Approach to Dimensionality Reduction for Anomaly Detection in Data Traffic

Huang, Tingshan; Sethu, Harish; Kandasamy, Nagarajan

doi:10.1109/tnsm.2016.2597125

Cited by 37 publications

(20 citation statements)

References 37 publications

(49 reference statements)

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“…Since dimensionality reduction usually provides an improvement in the detection of anomalies [ 41 , 42 ], a selection of the most relevant features was performed. First, after carrying out a variety of tests and analysing their results, we concluded that the data acquired from the magnetometer sensor were erratic, since the values depend on the geographical orientation of the device.…”

Section: Methodsmentioning

confidence: 99%

Improving the Security and QoE in Mobile Devices through an Intelligent and Adaptive Continuous Authentication System

Valero

Sánchez

Maimó

et al. 2018

Sensors

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Continuous authentication systems for mobile devices focus on identifying users according to their behaviour patterns when they interact with mobile devices. Among the benefits provided by these systems, we highlight the enhancement of the system security, having permanently authenticated the users; and the improvement of the users’ quality of experience, minimising the use of authentication credentials. Despite the benefits of these systems, they also have open challenges such as the authentication accuracy and the adaptability to new users’ behaviours. Continuous authentication systems should manage these challenges without forgetting critical aspects of mobile devices such as battery consumption, computational limitations and response time. With the goal of improving these previous challenges, the main contribution of this paper is the design and implementation of an intelligent and adaptive continuous authentication system for mobile devices. The proposed system enables the real-time users’ authentication by considering statistical information from applications, sensors and Machine Learning techniques based on anomaly detection. Several experiments demonstrated the accuracy, adaptability, and resources consumption of our solution. Finally, its utility is validated through the design and implementation of an online bank application as proof of concept, which allows users to perform different actions according to their authentication level.

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Section: Methodsmentioning

confidence: 99%

Improving the Security and QoE in Mobile Devices through an Intelligent and Adaptive Continuous Authentication System

Valero

Sánchez

Maimó

et al. 2018

Sensors

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“…Software Radio/ Cognitive Radio Huang et al [250] Multi-dimensional Scaling Applied distance based subspace dimensionality reduction technique for anomaly detection in data traffic.…”

Section: Mimomentioning

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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“…PCA derives a reduced set of the most significant uncorrelated features (principal components) that are linear combinations of the original set of features [27]. The new principal components are vectors in the direction of the largest variance of the dataset.…”

Section: Host Behavior Using Profilesmentioning

confidence: 99%

A Novel Network user Behaviors and Profile Testing based on Anomaly Detection Techniques

Tahir

Xiao

et al. 2019

IJACSA

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The proliferation of smart devices and computer networks has led to a huge rise in internet traffic and network attacks that necessitate efficient network traffic monitoring. There have been many attempts to address these issues; however, agile detecting solutions are needed. This research work deals with the problem of malware infections or detection is one of the most challenging tasks in modern computer security. In recent years, anomaly detection has been the first detection approach followed by results from other classifiers. Anomaly detection methods are typically designed to new model normal user behaviors and then seek for deviations from this model. However, anomaly detection techniques may suffer from a variety of problems, including missing validations for verification and a large number of false positives. This work proposes and describes a new profile-based method for identifying anomalous changes in network user behaviors. Profiles describe user behaviors from different perspectives using different flags. Each profile is composed of information about what the user has done over a period of time. The symptoms extracted in the profile cover a wide range of user actions and try to analyze different actions. Compared to other symptom anomaly detectors, the profiles offer a higher level of user experience. It is assumed that it is possible to look for anomalies using high-level symptoms while producing less false positives while effectively finding real attacks. Also, the problem of obtaining truly tagged data for training anomaly detection algorithms has been addressed in this work. It has been designed and created datasets that contain real normal user actions while the user is infected with real malware. These datasets were used to train and evaluate anomaly detection algorithms. Among the investigated algorithms for example, local outlier factor (LOF) and one class support vector machine (SVM). The results show that the proposed anomaly-based and profile-based algorithm causes very few false positives and relatively high true positive detection. The two main contributions of this work are a new approaches based on network anomaly detection and datasets containing a combination of genuine malware and actual user traffic. Finally, the future directions will focus on applying the proposed approaches for protecting the internet of things (IOT) devices.

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A New Approach to Dimensionality Reduction for Anomaly Detection in Data Traffic

Cited by 37 publications

References 37 publications

Improving the Security and QoE in Mobile Devices through an Intelligent and Adaptive Continuous Authentication System

Improving the Security and QoE in Mobile Devices through an Intelligent and Adaptive Continuous Authentication System

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

A Novel Network user Behaviors and Profile Testing based on Anomaly Detection Techniques

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