scholarly journals Detecting IoT User Behavior and Sensitive Information in Encrypted IoT-App Traffic

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4777 ◽  
Author(s):  
Alanoud Subahi ◽  
George Theodorakopoulos
Keyword(s):  
Network Traffic ◽  
User Behavior ◽  
Learning Algorithm ◽  
User Interaction ◽  
Active Role ◽  
Supervised Machine Learning ◽  
Sensitive Information ◽  
Content Type ◽  
Iot Devices ◽  
Types Of Information

Many people use smart-home devices, also known as the Internet of Things (IoT), in their daily lives. Most IoT devices come with a companion mobile application that users need to install on their smartphone or tablet to control, configure, and interface with the IoT device. IoT devices send information about their users from their app directly to the IoT manufacturer’s cloud; we call this the ”app-to-cloud way”. In this research, we invent a tool called IoT-app privacy inspector that can automatically infer the following from the IoT network traffic: the packet that reveals user interaction type with the IoT device via its app (e.g., login), the packets that carry sensitive Personal Identifiable Information (PII), the content type of such sensitive information (e.g., user’s location). We use Random Forest classifier as a supervised machine learning algorithm to extract features from network traffic. To train and test the three different multi-class classifiers, we collect and label network traffic from different IoT devices via their apps. We obtain the following classification accuracy values for the three aforementioned types of information: 99.4%, 99.8%, and 99.8%. This tool can help IoT users take an active role in protecting their privacy.

scholarly journals Attack Detection in IoT using Machine Learning

2021 ◽  
Vol 11 (3) ◽  
pp. 7273-7278
Author(s):  
M. Anwer ◽  
M. U. Farooq ◽  
S. M. Khan ◽  
W. Waseemullah
Keyword(s):  
Machine Learning ◽  
Network Traffic ◽  
Learning Algorithm ◽  
Attack Detection ◽  
Supervised Machine Learning ◽  
Detection Methods ◽  
Support Vector ◽  
Web Traffic ◽  
Local Networks ◽  
Iot Devices

Many researchers have examined the risks imposed by the Internet of Things (IoT) devices on big companies and smart towns. Due to the high adoption of IoT, their character, inherent mobility, and standardization limitations, smart mechanisms, capable of automatically detecting suspicious movement on IoT devices connected to the local networks are needed. With the increase of IoT devices connected through internet, the capacity of web traffic increased. Due to this change, attack detection through common methods and old data processing techniques is now obsolete. Detection of attacks in IoT and detecting malicious traffic in the early stages is a very challenging problem due to the increase in the size of network traffic. In this paper, a framework is recommended for the detection of malicious network traffic. The framework uses three popular classification-based malicious network traffic detection methods, namely Support Vector Machine (SVM), Gradient Boosted Decision Trees (GBDT), and Random Forest (RF), with RF supervised machine learning algorithm achieving far better accuracy (85.34%). The dataset NSL KDD was used in the recommended framework and the performances in terms of training, predicting time, specificity, and accuracy were compared.

scholarly journals Data Privacy in Wearable IoT Devices: Anonymization and Deanonymization

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Semi Park ◽  
Riha Kim ◽  
Hyunsik Yoon ◽  
Kyungho Lee
Keyword(s):  
Data Privacy ◽  
Wearable Devices ◽  
Location Information ◽  
Sensitive Information ◽  
Large Numbers ◽  
Other Information ◽  
User Data ◽  
Recorded Data ◽  
Iot Devices ◽  
Types Of Information

With the development of IoT devices, wearable devices are being used to record various types of information. Wearable IoT devices are attached to the user and can collect and transmit user data at all times along with a smartphone. In particular, sensitive information such as location information has an essential value in terms of privacy, and therefore some IoT devices implement data protection by introducing methods such as masking. However, masking can only protect privacy to a certain extent in logs having large numbers of recorded data. However, the effectiveness may decrease if we are linked with other information collected from within the device. Herein, a scenario-based case study on deanonymizing anonymized location information based on logs stored in wearable devices is described. As a result, we combined contextual and direct evidence from the collected information. It was possible to obtain the result in which the user could effectively identify the actual location. Through this study, not only can a deanonymized user location be identified but we can also confirm that cross-validation is possible even when dealing with modified GPS coordinates.

scholarly journals A look into user’s privacy perceptions and data practices of IoT devices

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mahdi Nasrullah Al-Ameen ◽  
Apoorva Chauhan ◽  
M.A. Manazir Ahsan ◽  
Huzeyfe Kocabas
Keyword(s):  
Private Information ◽  
Storage Period ◽  
Security And Privacy ◽  
Personal Contact ◽  
Content Type ◽  
Privacy Preferences ◽  
Data Practices ◽  
Online Social Media ◽  
Iot Devices ◽  
Types Of Information

Purpose With the rapid deployment of internet of things (IoT) technologies, it has been essential to address the security and privacy issues through maintaining transparency in data practices. The prior research focused on identifying people's privacy preferences in different contexts of IoT usage and their mental models of security threats. However, there is a dearth in existing literature to understand the mismatch between user's perceptions and the actual data practices of IoT devices. Such mismatches could lead users unknowingly sharing their private information, exposing themselves to unanticipated privacy risks. The paper aims to identify these mismatched privacy perceptions in this work. Design/methodology/approach The authors conducted a lab study with 42 participants, where they compared participants’ perceptions with the data practices stated in the privacy policy of 28 IoT devices from different categories, including health and exercise, entertainment, smart homes, toys and games and pets. Findings The authors identified the mismatched privacy perceptions of users in terms of data collection, sharing, protection and storage period. The findings revealed the mismatches between user's perceptions and the data practices of IoT devices for various types of information, including personal, contact, financial, heath, location, media, connected device, online social media and IoT device usage. Originality/value The findings from this study lead to the recommendations on designing simplified privacy notice by highlighting the unexpected data practices, which in turn, would contribute to the secure and privacy-preserving use of IoT devices.

Completeness based classification algorithm: a novel approach for educational semantic data completeness assessment

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ouidad Akhrif ◽  
Chaymae Benfaress ◽  
Mostapha EL Jai ◽  
Youness El Bouzekri El Idrissi ◽  
Nabil Hmina
Keyword(s):  
Collaborative Learning ◽  
Learning Algorithm ◽  
Interdisciplinary Collaboration ◽  
Supervised Machine Learning ◽  
Content Type ◽  
Learning Groups ◽  
University Environment ◽  
Semantic Data ◽  
Novel Approach ◽  
Completeness Assessment

Purpose The purpose of this paper is to reveal the smart collaborative learning service. This concept aims to build teams of learners based on the complementarity of their skills, allowing flexible participation and offering interdisciplinary collaboration opportunities for all the learners. The success of this environment is related to predict efficient collaboration between the different teammates, allowing a smartly sharing knowledge in the Smart University environment. Design/methodology/approach A random forest (RF) approach is proposed, which is based on semantic modelization of the learner and the problem-solving allowing multidisciplinary collaboration, and heuristic completeness processing to build complementary teams. To achieve that, this paper established a Konstanz Information Miner workflow that integrates the main steps for building and evaluating the RF classifier, this workflow is divided into: extracting knowledge from the smart collaborative learning ontology, calculating the completeness using a novel heuristic and building the RF classifier. Findings The smart collaborative learning service enables efficient collaboration and democratized sharing of knowledge between learners, by using a semantic support decision support system. This service solves a frequent issue related to the composition of learning groups to serve pedagogical perspectives. Originality/value The present study harmonizes the integration of ontology, a new heuristic processing and supervised machine learning algorithm aiming at building an intelligent collaborative learning service that includes a qualified classifier of complementary teams of learners.

scholarly journals Comprehensive Analysis of IoT Malware Evasion Techniques

2021 ◽  
Vol 11 (4) ◽  
pp. 7495-7500
Author(s):  
A. Al-Marghilani
Keyword(s):  
Malware Detection ◽  
User Interaction ◽  
Comprehensive Analysis ◽  
Sensitive Information ◽  
Identification Code ◽  
Code Obfuscation ◽  
Malicious Software ◽  
Code Compression ◽  
Advantages And Disadvantages ◽  
Iot Devices

Malware detection in Internet of Things (IoT) devices is a great challenge, as these devices lack certain characteristics such as homogeneity and security. Malware is malicious software that affects a system as it can steal sensitive information, slow its speed, cause frequent hangs, and disrupt operations. The most common malware types are adware, computer viruses, spyware, trojans, worms, rootkits, key loggers, botnets, and ransomware. Malware detection is critical for a system's security. Many security researchers have studied the IoT malware detection domain. Many studies proposed the static or dynamic analysis on IoT malware detection. This paper presents a survey of IoT malware evasion techniques, reviewing and discussing various researches. Malware uses a few common evasion techniques such as user interaction, environmental awareness, stegosploit, domain and IP identification, code obfuscation, code encryption, timing, and code compression. A comparative analysis was conducted pointing various advantages and disadvantages. This study provides guidelines on IoT malware evasion techniques.

scholarly journals Predictive Modelling of Employee Turnover in Indian IT Industry Using Machine Learning Techniques

2019 ◽  
Vol 23 (1) ◽  
pp. 12-21 ◽  
Author(s):  
Shikha N. Khera ◽  
Divya
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Confusion Matrix ◽  
Predictive Modelling ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
It Industry ◽  
Knowledge Based ◽  
Employee Attrition

Information technology (IT) industry in India has been facing a systemic issue of high attrition in the past few years, resulting in monetary and knowledge-based loses to the companies. The aim of this research is to develop a model to predict employee attrition and provide the organizations opportunities to address any issue and improve retention. Predictive model was developed based on supervised machine learning algorithm, support vector machine (SVM). Archival employee data (consisting of 22 input features) were collected from Human Resource databases of three IT companies in India, including their employment status (response variable) at the time of collection. Accuracy results from the confusion matrix for the SVM model showed that the model has an accuracy of 85 per cent. Also, results show that the model performs better in predicting who will leave the firm as compared to predicting who will not leave the company.

scholarly journals Machine Learning Classification and Regression Approaches for Optical Network Traffic Prediction

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1578
Author(s):  
Daniel Szostak ◽  
Adam Włodarczyk ◽  
Krzysztof Walkowiak
Keyword(s):  
Machine Learning ◽  
Optical Networks ◽  
Network Traffic ◽  
Optical Network ◽  
Optimization Methods ◽  
Supervised Machine Learning ◽  
Traffic Prediction ◽  
Machine Learning Classification ◽  
Classification And Regression ◽  
Network Technologies

Rapid growth of network traffic causes the need for the development of new network technologies. Artificial intelligence provides suitable tools to improve currently used network optimization methods. In this paper, we propose a procedure for network traffic prediction. Based on optical networks’ (and other network technologies) characteristics, we focus on the prediction of fixed bitrate levels called traffic levels. We develop and evaluate two approaches based on different supervised machine learning (ML) methods—classification and regression. We examine four different ML models with various selected features. The tested datasets are based on real traffic patterns provided by the Seattle Internet Exchange Point (SIX). Obtained results are analyzed using a new quality metric, which allows researchers to find the best forecasting algorithm in terms of network resources usage and operational costs. Our research shows that regression provides better results than classification in case of all analyzed datasets. Additionally, the final choice of the most appropriate ML algorithm and model should depend on the network operator expectations.

scholarly journals How to Cope with Big Data in Functional Analysis of the Esophagus

2020 ◽  
Vol 36 (6) ◽  
pp. 439-442
Author(s):  
Alissa Jell ◽  
Christina Kuttler ◽  
Daniel Ostler ◽  
Norbert Hüser
Keyword(s):  
Esophageal Motility ◽  
Learning Algorithm ◽  
Supervised Machine Learning ◽  
Esophageal Motility Disorders ◽  
Motility Disorders ◽  
High Resolution Manometry ◽  
Evaluation Time ◽  
Core Evaluation

<b><i>Introduction:</i></b> Esophageal motility disorders have a severe impact on patients’ quality of life. While high-resolution manometry (HRM) is the gold standard in the diagnosis of esophageal motility disorders, intermittently occurring muscular deficiencies often remain undiscovered if they do not lead to an intense level of discomfort or cause suffering in patients. Ambulatory long-term HRM allows us to study the circadian (dys)function of the esophagus in a unique way. With the prolonged examination period of 24 h, however, there is an immense increase in data which requires personnel and time for evaluation not available in clinical routine. Artificial intelligence (AI) might contribute here by performing an autonomous analysis. <b><i>Methods:</i></b> On the basis of 40 previously performed and manually tagged long-term HRM in patients with suspected temporary esophageal motility disorders, we implemented a supervised machine learning algorithm for automated swallow detection and classification. <b><i>Results:</i></b> For a set of 24 h of long-term HRM by means of this algorithm, the evaluation time could be reduced from 3 days to a core evaluation time of 11 min for automated swallow detection and clustering plus an additional 10–20 min of evaluation time, depending on the complexity and diversity of motility disorders in the examined patient. In 12.5% of patients with suggested esophageal motility disorders, AI-enabled long-term HRM was able to reveal new and relevant findings for subsequent therapy. <b><i>Conclusion:</i></b> This new approach paves the way to the clinical use of long-term HRM in patients with temporary esophageal motility disorders and might serve as an ideal and clinically relevant application of AI.

scholarly journals Identification of abnormal tribological regimes using a microphone and semi-supervised machine-learning algorithm

Friction ◽  
2021 ◽  
Author(s):  
Vigneashwara Pandiyan ◽  
Josef Prost ◽  
Georg Vorlaufer ◽  
Markus Varga ◽  
Kilian Wasmer
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Industrial Applications ◽  
Supervised Machine Learning ◽  
Normal Operation ◽  
Promising Alternative ◽  
Wear And Tear ◽  
Validation Procedure ◽  
And Performance

AbstractFunctional surfaces in relative contact and motion are prone to wear and tear, resulting in loss of efficiency and performance of the workpieces/machines. Wear occurs in the form of adhesion, abrasion, scuffing, galling, and scoring between contacts. However, the rate of the wear phenomenon depends primarily on the physical properties and the surrounding environment. Monitoring the integrity of surfaces by offline inspections leads to significant wasted machine time. A potential alternate option to offline inspection currently practiced in industries is the analysis of sensors signatures capable of capturing the wear state and correlating it with the wear phenomenon, followed by in situ classification using a state-of-the-art machine learning (ML) algorithm. Though this technique is better than offline inspection, it possesses inherent disadvantages for training the ML models. Ideally, supervised training of ML models requires the datasets considered for the classification to be of equal weightage to avoid biasing. The collection of such a dataset is very cumbersome and expensive in practice, as in real industrial applications, the malfunction period is minimal compared to normal operation. Furthermore, classification models would not classify new wear phenomena from the normal regime if they are unfamiliar. As a promising alternative, in this work, we propose a methodology able to differentiate the abnormal regimes, i.e., wear phenomenon regimes, from the normal regime. This is carried out by familiarizing the ML algorithms only with the distribution of the acoustic emission (AE) signals captured using a microphone related to the normal regime. As a result, the ML algorithms would be able to detect whether some overlaps exist with the learnt distributions when a new, unseen signal arrives. To achieve this goal, a generative convolutional neural network (CNN) architecture based on variational auto encoder (VAE) is built and trained. During the validation procedure of the proposed CNN architectures, we were capable of identifying acoustics signals corresponding to the normal and abnormal wear regime with an accuracy of 97% and 80%. Hence, our approach shows very promising results for in situ and real-time condition monitoring or even wear prediction in tribological applications.

Sign in / Sign up

Export Citation Format

Share Document