Attack Detection in IoT using Machine Learning

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.

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

Vision The Journal of Business Perspective ◽

10.1177/0972262918821221 ◽

2019 ◽

Vol 23 (1) ◽

pp. 12-21 ◽

Cited By ~ 2

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.

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Classifying relevant video tutorials for the school’s learning management system using support vector machine algorithm

10.31219/osf.io/scz4r ◽

2020 ◽

Author(s):

Castro Mayleen Dorcas Bondoc ◽

Tumibay Gilbert Malawit

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Learning Process ◽

Learning Algorithm ◽

Research Work ◽

Supervised Machine Learning ◽

Support Vector ◽

Video Tutorials ◽

Learning Management ◽

Face To Face Instruction

Today many schools, universities and institutions recognize the necessity and importance of using Learning Management Systems (LMS) as part of their educational services. This research work has applied LMS in the teaching and learning process of Bulacan State University (BulSU) Graduate School (GS) Program that enhances the face-to-face instruction with online components. The researchers uses an LMS that provides educators a platform that can motivate and engage students to new educational environment through manage online classes. The LMS allows educators to distribute information, manage learning materials, assignments, quizzes, and communications. Aside from the basic functions of the LMS, the researchers uses Machine Learning (ML) Algorithms applying Support Vector Machine (SVM) that will classify and identify the best related videos per topic. SVM is a supervised machine learning algorithm that analyzes data for classification and regression analysis by Maity [1]. The results of this study showed that integration of video tutorials in LMS can significantly contribute knowledge and skills in the learning process of the students.

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Support Vector Machine

Handbook of Research on Modern Systems Analysis and Design Technologies and Applications ◽

10.4018/978-1-59904-887-1.ch028 ◽

2009 ◽

pp. 501-522 ◽

Cited By ~ 1

Author(s):

A. B.M. Shawkat Ali

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

System Analysis ◽

Learning Algorithm ◽

Mathematical Formulation ◽

Supervised Machine Learning ◽

Future Research ◽

Support Vector ◽

Analysis And Design ◽

Simple Demonstration

From the beginning, machine learning methodology, which is the origin of artificial intelligence, has been rapidly spreading in the different research communities with successful outcomes. This chapter aims to introduce for system analysers and designers a comparatively new statistical supervised machine learning algorithm called support vector machine (SVM). We explain two useful areas of SVM, that is, classification and regression, with basic mathematical formulation and simple demonstration to make easy the understanding of SVM. Prospects and challenges of future research in this emerging area are also described. Future research of SVM will provide improved and quality access to the users. Therefore, developing an automated SVM system with state-of-the-art technologies is of paramount importance, and hence, this chapter will link up an important step in the system analysis and design perspective to this evolving research arena.

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Measuring Biomechanical Risk in Lifting Load Tasks Through Wearable System and Machine-Learning Approach

Sensors ◽

10.3390/s20061557 ◽

2020 ◽

Vol 20 (6) ◽

pp. 1557 ◽

Cited By ~ 4

Author(s):

Ilaria Conforti ◽

Ilaria Mileti ◽

Zaccaria Del Prete ◽

Eduardo Palermo

Keyword(s):

Machine Learning ◽

Material Handling ◽

Learning Algorithm ◽

Occupational Injuries ◽

Wearable Sensors ◽

High Stress ◽

Machine Learning Algorithms ◽

Supervised Machine Learning ◽

Support Vector ◽

Kinematic Parameters

Ergonomics evaluation through measurements of biomechanical parameters in real time has a great potential in reducing non-fatal occupational injuries, such as work-related musculoskeletal disorders. Assuming a correct posture guarantees the avoidance of high stress on the back and on the lower extremities, while an incorrect posture increases spinal stress. Here, we propose a solution for the recognition of postural patterns through wearable sensors and machine-learning algorithms fed with kinematic data. Twenty-six healthy subjects equipped with eight wireless inertial measurement units (IMUs) performed manual material handling tasks, such as lifting and releasing small loads, with two postural patterns: correctly and incorrectly. Measurements of kinematic parameters, such as the range of motion of lower limb and lumbosacral joints, along with the displacement of the trunk with respect to the pelvis, were estimated from IMU measurements through a biomechanical model. Statistical differences were found for all kinematic parameters between the correct and the incorrect postures (p < 0.01). Moreover, with the weight increase of load in the lifting task, changes in hip and trunk kinematics were observed (p < 0.01). To automatically identify the two postures, a supervised machine-learning algorithm, a support vector machine, was trained, and an accuracy of 99.4% (specificity of 100%) was reached by using the measurements of all kinematic parameters as features. Meanwhile, an accuracy of 76.9% (specificity of 76.9%) was reached by using the measurements of kinematic parameters related to the trunk body segment.

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MALGRA: Machine Learning and N-Gram Malware Feature Extraction and Detection System

Electronics ◽

10.3390/electronics9111777 ◽

2020 ◽

Vol 9 (11) ◽

pp. 1777

Author(s):

Muhammad Ali ◽

Stavros Shiaeles ◽

Gueltoum Bendiab ◽

Bogdan Ghita

Keyword(s):

Machine Learning ◽

Learning Algorithm ◽

Detection System ◽

Malware Detection ◽

Machine Learning Algorithms ◽

Supervised Machine Learning ◽

Detection Methods ◽

Normal Operation ◽

Analysis Technique ◽

N Gram

Detection and mitigation of modern malware are critical for the normal operation of an organisation. Traditional defence mechanisms are becoming increasingly ineffective due to the techniques used by attackers such as code obfuscation, metamorphism, and polymorphism, which strengthen the resilience of malware. In this context, the development of adaptive, more effective malware detection methods has been identified as an urgent requirement for protecting the IT infrastructure against such threats, and for ensuring security. In this paper, we investigate an alternative method for malware detection that is based on N-grams and machine learning. We use a dynamic analysis technique to extract an Indicator of Compromise (IOC) for malicious files, which are represented using N-grams. The paper also proposes TF-IDF as a novel alternative used to identify the most significant N-grams features for training a machine learning algorithm. Finally, the paper evaluates the proposed technique using various supervised machine-learning algorithms. The results show that Logistic Regression, with a score of 98.4%, provides the best classification accuracy when compared to the other classifiers used.

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An Overview of Supervised Machine Learning Methods and Data Analysis for COVID-19 Detection

Journal of Healthcare Engineering ◽

10.1155/2021/4733167 ◽

2021 ◽

Vol 2021 ◽

pp. 1-18

Author(s):

Aurelle Tchagna Kouanou ◽

Thomas Mih Attia ◽

Cyrille Feudjio ◽

Anges Fleurio Djeumo ◽

Adèle Ngo Mouelas ◽

...

Keyword(s):

Machine Learning ◽

Data Analysis ◽

Learning Algorithm ◽

Machine Learning Algorithms ◽

High Rate ◽

Supervised Machine Learning ◽

Polymerase Chain Reaction Test ◽

Support Vector ◽

Machine Learning Algorithm ◽

Test Results

Background and Objective. To mitigate the spread of the virus responsible for COVID-19, known as SARS-CoV-2, there is an urgent need for massive population testing. Due to the constant shortage of PCR (polymerase chain reaction) test reagents, which are the tests for COVID-19 by excellence, several medical centers have opted for immunological tests to look for the presence of antibodies produced against this virus. However, these tests have a high rate of false positives (positive but actually negative test results) and false negatives (negative but actually positive test results) and are therefore not always reliable. In this paper, we proposed a solution based on Data Analysis and Machine Learning to detect COVID-19 infections. Methods. Our analysis and machine learning algorithm is based on most cited two clinical datasets from the literature: one from San Raffaele Hospital Milan Italia and the other from Hospital Israelita Albert Einstein São Paulo Brasilia. The datasets were processed to select the best features that most influence the target, and it turned out that almost all of them are blood parameters. EDA (Exploratory Data Analysis) methods were applied to the datasets, and a comparative study of supervised machine learning models was done, after which the support vector machine (SVM) was selected as the one with the best performance. Results. SVM being the best performant is used as our proposed supervised machine learning algorithm. An accuracy of 99.29%, sensitivity of 92.79%, and specificity of 100% were obtained with the dataset from Kaggle (https://www.kaggle.com/einsteindata4u/covid19) after applying optimization to SVM. The same procedure and work were performed with the dataset taken from San Raffaele Hospital (https://zenodo.org/record/3886927#.YIluB5AzbMV). Once more, the SVM presented the best performance among other machine learning algorithms, and 92.86%, 93.55%, and 90.91% for accuracy, sensitivity, and specificity, respectively, were obtained. Conclusion. The obtained results, when compared with others from the literature based on these same datasets, are superior, leading us to conclude that our proposed solution is reliable for the COVID-19 diagnosis.

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Semi-supervised Machine Learning Aided Anomaly Detection Method in Cellular Networks

10.36227/techrxiv.11634720 ◽

2020 ◽

Author(s):

Yutao Lu ◽

Juan Wang ◽

Miao Liu ◽

Kaixuan Zhang ◽

Guan Gui ◽

...

Keyword(s):

Machine Learning ◽

Anomaly Detection ◽

Cellular Networks ◽

Expert Knowledge ◽

Learning Algorithm ◽

Positive Sample ◽

Supervised Machine Learning ◽

Support Vector ◽

Soft Decision ◽

Decision Methods

The ever-increasing amount of data in cellular networks poses challenges for network operators to monitor the quality of experience (QoE). Traditional key quality indicators (KQIs)-based hard decision methods are difficult to undertake the task of QoE anomaly detection in the case of big data. To solve this problem, in this paper, we propose a KQIs-based QoE anomaly detection framework using semi-supervised machine learning algorithm, i.e., iterative positive sample aided one-class support vector machine (IPS-OCSVM). There are four steps for realizing the proposed method while the key step is combining machine learning with the network operator's expert knowledge using OCSVM. Our proposed IPS-OCSVM framework realizes QoE anomaly detection through soft decision and can easily fine-tune the anomaly detection ability on demand. Moreover, we prove that the fluctuation of KQIs thresholds based on expert knowledge has a limited impact on the result of anomaly detection. Finally, experiment results are given to confirm the proposed IPS-OCSVM framework for QoE anomaly detection in cellular networks.

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Prediction of COVID-19 Patient using Supervised Machine Learning Algorithm

Sains Malaysiana ◽

10.17576/jsm-2021-5008-28 ◽

2021 ◽

Vol 50 (8) ◽

pp. 2479-2497

Author(s):

Buvana M. ◽

Muthumayil K.

Keyword(s):

Machine Learning ◽

Learning Algorithm ◽

Nasal Congestion ◽

Supervised Machine Learning ◽

Support Vector ◽

Data Set ◽

Physiological Measurement ◽

Machine Learning Classifiers ◽

Balanced Diet ◽

Learning Classifiers

One of the most symptomatic diseases is COVID-19. Early and precise physiological measurement-based prediction of breathing will minimize the risk of COVID-19 by a reasonable distance from anyone; wearing a mask, cleanliness, medication, balanced diet, and if not well stay safe at home. To evaluate the collected datasets of COVID-19 prediction, five machine learning classifiers were used: Nave Bayes, Support Vector Machine (SVM), Logistic Regression, K-Nearest Neighbour (KNN), and Decision Tree. COVID-19 datasets from the Repository were combined and re-examined to remove incomplete entries, and a total of 2500 cases were utilized in this study. Features of fever, body pain, runny nose, difficulty in breathing, shore throat, and nasal congestion, are considered to be the most important differences between patients who have COVID-19s and those who do not. We exhibit the prediction functionality of five machine learning classifiers. A publicly available data set was used to train and assess the model. With an overall accuracy of 99.88 percent, the ensemble model is performed commendably. When compared to the existing methods and studies, the proposed model is performed better. As a result, the model presented is trustworthy and can be used to screen COVID-19 patients timely, efficiently.

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Optimized machine learning algorithm for intrusion detection

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v24.i1.pp590-599 ◽

2021 ◽

Vol 24 (1) ◽

pp. 590

Author(s):

Royida A. Ibrahem Alhayali ◽

Mohammad Aljanabi ◽

Ahmed Hussein Ali ◽

Mostafa Abdulghfoor Mohammed ◽

Tole Sutikno

Keyword(s):

Machine Learning ◽

Intrusion Detection ◽

Learning Algorithm ◽

Detection System ◽

Optimization Algorithms ◽

Feature Subset Selection ◽

Supervised Machine Learning ◽

Support Vector ◽

Feature Subset ◽

Training Time

Intrusion detection is mainly achieved by using optimization algorithms. The need for optimization algorithms for intrusion detection is necessitated by the increasing number of features in audit data, as well as the performance failure of the human-based smart intrusion detection system (IDS) in terms of their prolonged training time and classification accuracy. This article presents an improved intrusion detection technique for binary classification. The proposal is a combination of different optimizers, including Rao optimization algorithm, extreme learning machine (ELM), support vector machine (SVM), and logistic regression (LR) (for feature selection & weighting), as well as a hybrid Rao-SVM algorithm with supervised machine learning (ML) techniques for feature subset selection (FSS). The process of selecting the least number of features without sacrificing the FSS accuracy was considered a multi-objective optimization problem. The algorithm-specific, parameter-less concept of the proposed Rao-SVM was also explored in this study. The KDDCup 99 and CICIDS 2017 were used as the intrusion dataset for the experiments, where significant improvements were noted with the new Rao-SVM compared to the other algorithms. Rao-SVM presented better results than many existing works by reaching 100% accuracy for KDDCup 99 dataset and 97% for CICIDS dataset.

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