Usage Analysis of Mobile Devices

• URL: http://arxiv.org/abs/2005.12140v1
• Date: Wed, 13 May 2020 12:15:24 GMT
• Title: Usage Analysis of Mobile Devices
• Authors: Aman Singh, Ashish Prajapatia, Vikash Kumar, Subhankar Mishra
• Abstract summary: A novel approach of user behaviour detection is proposed with Deep Learning Network (DNN) Initial approach was to use recurrent neural network (RNN) along with Long Short Term Memory (LSTM) for completely unsupervised analysis of mobile devices. Next approach is to extract features by using Long Short Term Memory (LSTM) to understand the user behaviour, which are then fed into the Convolution Neural Network (CNN)
• Score: 9.686156285265069
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Mobile devices have evolved from just communication devices into an indispensable part of people's lives in form of smartphones, tablets and smart watches. Devices are now more personal than ever and carry more information about a person than any other. Extracting user behaviour is rather difficult and time-consuming as most of the work previously has been manual or requires feature extraction. In this paper, a novel approach of user behavior detection is proposed with Deep Learning Network (DNN). Initial approach was to use recurrent neural network (RNN) along with LSTM for completely unsupervised analysis of mobile devices. Next approach is to extract features by using Long Short Term Memory (LSTM) to understand the user behaviour, which are then fed into the Convolution Neural Network (CNN). This work mainly concentrates on detection of user behaviour and anomaly detection for usage analysis of mobile devices. Both the approaches are compared against some baseline methods. Experiments are conducted on the publicly available dataset to show that these methods can successfully capture the user behaviors.

Related papers

• Approaches to human activity recognition via passive radar [4.2261749429617534]
  The thesis explores novel methods for Human Activity Recognition (HAR) using passive radar with a focus on non-intrusive Wi-Fi Channel State Information (CSI) data. This study leverages the non-intrusive nature of CSI, using Spiking Neural Networks (SNN) to interpret signal variations caused by human movements.
  arXiv  Detail & Related papers  (2024-10-31T17:28:41Z)
• LGB: Language Model and Graph Neural Network-Driven Social Bot Detection [43.92522451274129]
  Malicious social bots achieve their malicious purposes by spreading misinformation and inciting social public opinion. We propose a novel social bot detection framework LGB, which consists of two main components: language model (LM) and graph neural network (GNN) Experiments on two real-world datasets demonstrate that LGB consistently outperforms state-of-the-art baseline models by up to 10.95%.
  arXiv  Detail & Related papers  (2024-06-13T02:47:38Z)
• Modeling User Preferences via Brain-Computer Interfacing [54.3727087164445]
  We use Brain-Computer Interfacing technology to infer users' preferences, their attentional correlates towards visual content, and their associations with affective experience. We link these to relevant applications, such as information retrieval, personalized steering of generative models, and crowdsourcing population estimates of affective experiences.
  arXiv  Detail & Related papers  (2024-05-15T20:41:46Z)
• Intrusion Detection in Internet of Things using Convolutional Neural Networks [4.718295605140562]
  We propose a novel solution to the intrusion attacks against IoT devices using CNNs. The data is encoded as the convolutional operations to capture the patterns from the sensors data along time. The experimental results show significant improvement in both true positive rate and false positive rate compared to the baseline using LSTM.
  arXiv  Detail & Related papers  (2022-11-18T07:27:07Z)
• Big data analysis and distributed deep learning for next-generation intrusion detection system optimization [0.0]
  This paper proposes a solution to detect new threats with higher detection rate and lower false positive than already used IDS. We achieve those results by using Networking, a deep recurrent neural network: Long Short Term Memory (LSTM) on top of Apache Spark Framework. We propose a model that describes the network abstract normal behavior from a sequence of millions of packets within their context and analyzes them in near real-time to detect point, collective and contextual anomalies.
  arXiv  Detail & Related papers  (2022-09-28T09:46:16Z)
• CubeLearn: End-to-end Learning for Human Motion Recognition from Raw mmWave Radar Signals [40.53874877651099]
  mmWave FMCW radar has attracted huge amount of research interest for human-centered applications in recent years. Most existing pipelines are built upon conventional DFT pre-processing and deep neural network hybrid methods. We propose a learnable pre-processing module, named CubeLearn, to directly extract features from raw radar signal.
  arXiv  Detail & Related papers  (2021-11-07T00:45:51Z)
• Finding Facial Forgery Artifacts with Parts-Based Detectors [73.08584805913813]
  We design a series of forgery detection systems that each focus on one individual part of the face. We use these detectors to perform detailed empirical analysis on the FaceForensics++, Celeb-DF, and Facebook Deepfake Detection Challenge datasets.
  arXiv  Detail & Related papers  (2021-09-21T16:18:45Z)
• Smartphone Impostor Detection with Behavioral Data Privacy and Minimalist Hardware Support [7.374079197112307]
  Impostors are attackers who take over a smartphone and gain access to the legitimate user's confidential and private information. This paper proposes a defense-in-depth mechanism to detect impostors quickly with simple Deep Learning algorithms. We also show how a minimalist hardware module, dubbed SID for Smartphone Impostor Detector, can be designed and integrated into smartphones for self-contained impostor detection.
  arXiv  Detail & Related papers  (2021-03-11T04:39:53Z)
• Spiking Neural Networks -- Part III: Neuromorphic Communications [38.518936229794214]
  The presence of more and more wirelessly connected devices is driving efforts to export advances in machine learning. Implementing machine learning models for learning and inference on battery-powered devices that are connected via bandwidth-constrained channels remains challenging. This paper explores two ways in which Spiking Neural Networks (SNNs) can help address these open problems.
  arXiv  Detail & Related papers  (2020-10-27T11:52:35Z)
• Enabling Incremental Knowledge Transfer for Object Detection at the Edge [25.22732861751805]
  Object detection using deep neural networks (DNNs) involves a huge amount of computation. shallow neural network (SHNN) is deployed on user-end device to detect objects in observing environment. SHNN knowledge can be obtained from a powerful edge device connected to the user-end device through LAN or Wi-Fi.
  arXiv  Detail & Related papers  (2020-04-13T02:19:18Z)
• Any-Shot Sequential Anomaly Detection in Surveillance Videos [36.24563211765782]
  We propose an online anomaly detection method for surveillance videos using transfer learning and any-shot learning. Our proposed algorithm leverages the feature extraction power of neural network-based models for transfer learning and the any-shot learning capability of statistical detection methods.
  arXiv  Detail & Related papers  (2020-04-05T02:15:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.