Detecting malicious software using machine learning

2019 ◽  
pp. 42-45
Author(s):  
A. V. Chevychelov ◽  
A. V. Burmistrov ◽  
K. Yu. Voyshhev
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Decision Tree ◽  
Learning Algorithms ◽  
Malware Detection ◽  
Malicious Code ◽  
Machine Learning Algorithms ◽  
Fisher Criterion ◽  
Malicious Software ◽  
Informative Parameters

Today, most malware detection tools (Trojans): trojans, spyware, adware, worms, viruses, and ransomware are based on a signature approach that is ineffective for detecting polymorphs and malware whose signatures have not been recorded in antivirus database. This article explores methods for detecting opcodes in malware using machine learning algorithms. The study is carried on a Microsoft dataset containing 21653 examples of malicious code. The 20 most informative parameters based on the Fisher criterion are distinguished, methods for selecting parameters and various classifiers (logistic decision tree, random forest, naive Bayesian classifier, random tree) are compared, as a result of which an accuracy close to 100% is achieved.

scholarly journals Techniques for Detecting Malware Traffic: A Comprehensive Approach to Feature Selection and Classification

2021 ◽  
Vol 9 (12) ◽  
pp. 1-10
Author(s):  
Harsha A K
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Random Forest ◽  
Learning Algorithms ◽  
Malware Detection ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Support Vector ◽  
Steady Increase ◽  
Extreme Gradient Boosting

Abstract: Since the advent of encryption, there has been a steady increase in malware being transmitted over encrypted networks. Traditional approaches to detect malware like packet content analysis are inefficient in dealing with encrypted data. In the absence of actual packet contents, we can make use of other features like packet size, arrival time, source and destination addresses and other such metadata to detect malware. Such information can be used to train machine learning classifiers in order to classify malicious and benign packets. In this paper, we offer an efficient malware detection approach using classification algorithms in machine learning such as support vector machine, random forest and extreme gradient boosting. We employ an extensive feature selection process to reduce the dimensionality of the chosen dataset. The dataset is then split into training and testing sets. Machine learning algorithms are trained using the training set. These models are then evaluated against the testing set in order to assess their respective performances. We further attempt to tune the hyper parameters of the algorithms, in order to achieve better results. Random forest and extreme gradient boosting algorithms performed exceptionally well in our experiments, resulting in area under the curve values of 0.9928 and 0.9998 respectively. Our work demonstrates that malware traffic can be effectively classified using conventional machine learning algorithms and also shows the importance of dimensionality reduction in such classification problems. Keywords: Malware Detection, Extreme Gradient Boosting, Random Forest, Feature Selection.

scholarly journals Prediction of COVID-19 Risk in Public Areas Using IoT and Machine Learning

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1677
Author(s):  
Ersin Elbasi ◽  
Ahmet E. Topcu ◽  
Shinu Mathew
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Decision Tree ◽  
Naive Bayes ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Bayes Classifier ◽  
Social Distancing ◽  
Public Areas ◽  
Iot Devices

COVID-19 is a community-acquired infection with symptoms that resemble those of influenza and bacterial pneumonia. Creating an infection control policy involving isolation, disinfection of surfaces, and identification of contagions is crucial in eradicating such pandemics. Incorporating social distancing could also help stop the spread of community-acquired infections like COVID-19. Social distancing entails maintaining certain distances between people and reducing the frequency of contact between people. Meanwhile, a significant increase in the development of different Internet of Things (IoT) devices has been seen together with cyber-physical systems that connect with physical environments. Machine learning is strengthening current technologies by adding new approaches to quickly and correctly solve problems utilizing this surge of available IoT devices. We propose a new approach using machine learning algorithms for monitoring the risk of COVID-19 in public areas. Extracted features from IoT sensors are used as input for several machine learning algorithms such as decision tree, neural network, naïve Bayes classifier, support vector machine, and random forest to predict the risks of the COVID-19 pandemic and calculate the risk probability of public places. This research aims to find vulnerable populations and reduce the impact of the disease on certain groups using machine learning models. We build a model to calculate and predict the risk factors of populated areas. This model generates automated alerts for security authorities in the case of any abnormal detection. Experimental results show that we have high accuracy with random forest of 97.32%, with decision tree of 94.50%, and with the naïve Bayes classifier of 99.37%. These algorithms indicate great potential for crowd risk prediction in public areas.

scholarly journals Machine Learning Algorithms for Visualization and Prediction Modeling of Boston Crime Data

2020 ◽  
Author(s):  
Jiarui Yin ◽  
Inikuro Afa Michael ◽  
Iduabo John Afa
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Decision Tree ◽  
Data Science ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Crime Data ◽  
Detection Analysis ◽  
Supervised Learning Algorithms ◽  
Supervised Methods

Machine learning plays a key role in present day crime detection, analysis and prediction. The goal of this work is to propose methods for predicting crimes classified into different categories of severity. We implemented visualization and analysis of crime data statistics in recent years in the city of Boston. We then carried out a comparative study between two supervised learning algorithms, which are decision tree and random forest based on the accuracy and processing time of the models to make predictions using geographical and temporal information provided by splitting the data into training and test sets. The result shows that random forest as expected gives a better result by 1.54% more accuracy in comparison to decision tree, although this comes at a cost of at least 4.37 times the time consumed in processing. The study opens doors to application of similar supervised methods in crime data analytics and other fields of data science

scholarly journals Data Driven Approach for Eye Disease Classification with Machine Learning

2019 ◽  
Vol 9 (14) ◽  
pp. 2789 ◽  
Author(s):  
Sadaf Malik ◽  
Nadia Kanwal ◽  
Mamoona Naveed Asghar ◽  
Mohammad Ali A. Sadiq ◽  
Irfan Karamat ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Decision Tree ◽  
Naive Bayes ◽  
Learning Algorithms ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Multiple Features ◽  
Standard Format ◽  
Free Data

Medical health systems have been concentrating on artificial intelligence techniques for speedy diagnosis. However, the recording of health data in a standard form still requires attention so that machine learning can be more accurate and reliable by considering multiple features. The aim of this study is to develop a general framework for recording diagnostic data in an international standard format to facilitate prediction of disease diagnosis based on symptoms using machine learning algorithms. Efforts were made to ensure error-free data entry by developing a user-friendly interface. Furthermore, multiple machine learning algorithms including Decision Tree, Random Forest, Naive Bayes and Neural Network algorithms were used to analyze patient data based on multiple features, including age, illness history and clinical observations. This data was formatted according to structured hierarchies designed by medical experts, whereas diagnosis was made as per the ICD-10 coding developed by the American Academy of Ophthalmology. Furthermore, the system is designed to evolve through self-learning by adding new classifications for both diagnosis and symptoms. The classification results from tree-based methods demonstrated that the proposed framework performs satisfactorily, given a sufficient amount of data. Owing to a structured data arrangement, the random forest and decision tree algorithms’ prediction rate is more than 90% as compared to more complex methods such as neural networks and the naïve Bayes algorithm.

scholarly journals Statistical Analysis for Selective Identifications of VOCs by Using Surface Functionalized MoS2 Based Sensor Array

2021 ◽  
Vol 5 (1) ◽  
pp. 35
Author(s):  
Uttam Narendra Thakur ◽  
Radha Bhardwaj ◽  
Arnab Hazra
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Random Forest ◽  
Decision Tree ◽  
Sensor Array ◽  
Multinomial Logistic Regression ◽  
Learning Algorithms ◽  
Disease Diagnosis ◽  
Machine Learning Algorithms ◽  
Human Breath

Disease diagnosis through breath analysis has attracted significant attention in recent years due to its noninvasive nature, rapid testing ability, and applicability for patients of all ages. More than 1000 volatile organic components (VOCs) exist in human breath, but only selected VOCs are associated with specific diseases. Selective identification of those disease marker VOCs using an array of multiple sensors are highly desirable in the current scenario. The use of efficient sensors and the use of suitable classification algorithms is essential for the selective and reliable detection of those disease markers in complex breath. In the current study, we fabricated a noble metal (Au, Pd and Pt) nanoparticle-functionalized MoS2 (Chalcogenides, Sigma Aldrich, St. Louis, MO, USA)-based sensor array for the selective identification of different VOCs. Four sensors, i.e., pure MoS2, Au/MoS2, Pd/MoS2, and Pt/MoS2 were tested under exposure to different VOCs, such as acetone, benzene, ethanol, xylene, 2-propenol, methanol and toluene, at 50 °C. Initially, principal component analysis (PCA) and linear discriminant analysis (LDA) were used to discriminate those seven VOCs. As compared to the PCA, LDA was able to discriminate well between the seven VOCs. Four different machine learning algorithms such as k-nearest neighbors (kNN), decision tree, random forest, and multinomial logistic regression were used to further identify those VOCs. The classification accuracy of those seven VOCs using KNN, decision tree, random forest, and multinomial logistic regression was 97.14%, 92.43%, 84.1%, and 98.97%, respectively. These results authenticated that multinomial logistic regression performed best between the four machine learning algorithms to discriminate and differentiate the multiple VOCs that generally exist in human breath.

scholarly journals Application of Data Mining Algorithms for Dementia in People with HIV/AIDS

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Luana Ibiapina Cordeiro Calíope Pinheiro ◽  
Maria Lúcia Duarte Pereira ◽  
Marcial Porto Fernandez ◽  
Francisco Mardônio Vieira Filho ◽  
Wilson Jorge Correia Pinto de Abreu ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Data Mining ◽  
Logistic Regression ◽  
Random Forest ◽  
Decision Tree ◽  
Learning Algorithms ◽  
Principal Component ◽  
Machine Learning Algorithms ◽  
Hiv Aids

Dementia interferes with the individual’s motor, behavioural, and intellectual functions, causing him to be unable to perform instrumental activities of daily living. This study is aimed at identifying the best performing algorithm and the most relevant characteristics to categorise individuals with HIV/AIDS at high risk of dementia from the application of data mining. Principal component analysis (PCA) algorithm was used and tested comparatively between the following machine learning algorithms: logistic regression, decision tree, neural network, KNN, and random forest. The database used for this study was built from the data collection of 270 individuals infected with HIV/AIDS and followed up at the outpatient clinic of a reference hospital for infectious and parasitic diseases in the State of Ceará, Brazil, from January to April 2019. Also, the performance of the algorithms was analysed for the 104 characteristics available in the database; then, with the reduction of dimensionality, there was an improvement in the quality of the machine learning algorithms and identified that during the tests, even losing about 30% of the variation. Besides, when considering only 23 characteristics, the precision of the algorithms was 86% in random forest, 56% logistic regression, 68% decision tree, 60% KNN, and 59% neural network. The random forest algorithm proved to be more effective than the others, obtaining 84% precision and 86% accuracy.

APPLICATION OF MACHINE LEARNING ALGORITHMS FOR CLASSIFICATION OF WEED VARIETIES

2021 ◽  
Vol 75 (3) ◽  
pp. 83-93
Author(s):  
Zh. A. Buribayev ◽  
◽  
Zh. E. Amirgaliyeva ◽  
A.S. Ataniyazova ◽  
Z. M. Melis ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Decision Tree ◽  
Agricultural Land ◽  
Learning Algorithms ◽  
Nearest Neighbors ◽  
Machine Learning Algorithms ◽  
Weed Detection ◽  
K Nearest Neighbors ◽  
Data Set

The article considers the relevance of the introduction of intelligent weed detection systems, in order to save herbicides and pesticides, as well as to obtain environmentally friendly products. A brief review of the researchers' scientific works is carried out, which describes the methods of identification, classification and discrimination of weeds developed by them based on machine learning algorithms, convolutional neural networks and deep learning algorithms. This research paper presents a program for detecting pests of agricultural land using the algorithms K-Nearest Neighbors, Random Forest and Decision Tree. The data set is collected from 4 types of weeds, such as amaranthus, ambrosia, bindweed and bromus. According to the results of the assessment, the accuracy of weed detection by the classifiers K-Nearest Neighbors, Random Forest and Decision Tree was 83.3%, 87.5%, and 80%. Quantitative results obtained on real data demonstrate that the proposed approach can provide good results in classifying low-resolution images of weeds.

scholarly journals Comparative analysis of machine learning algorithms in water extraction

2021 ◽  
Vol 2076 (1) ◽  
pp. 012045
Author(s):  
Aimin Li ◽  
Meng Fan ◽  
Guangduo Qin
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Logistic Regression ◽  
Comparative Analysis ◽  
Random Forest ◽  
Decision Tree ◽  
Water Body ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector

Abstract There are many traditional methods available for water body extraction based on remote sensing images, such as normalised difference water index (NDWI), modified NDWI (MNDWI), and the multi-band spectrum method, but the accuracy of these methods is limited. In recent years, machine learning algorithms have developed rapidly and been applied widely. Using Landsat-8 images, models such as decision tree, logistic regression, a random forest, neural network, support vector method (SVM), and Xgboost were adopted in the present research within machine learning algorithms. Based on this, through cross validation and a grid search method, parameters were determined for each model.Moreover, the merits and demerits of several models in water body extraction were discussed and a comparative analysis was performed with three methods for determining thresholds in the traditional NDWI. The results show that the neural network has excellent performances and is a stable model, followed by the SVM and the logistic regression algorithm. Furthermore, the ensemble algorithms including the random forest and Xgboost were affected by sample distribution and the model of the decision tree returned the poorest performance.

scholarly journals Crop Disease Recognition using Machine Learning Algorithms

2020 ◽  
Vol 9 (11) ◽  
pp. 164-166
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Decision Tree ◽  
Multilayer Perceptron ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Disease Classification ◽  
Accurate Identification ◽  
Machine Learning Classification ◽  
Simple Logistic

Classification is a method of observing the features of a new object and assigning it to a known class. Machine learning classification problem consists of known classes and a vivid training set of pre-categorized examples. The work diagnoses groundnut diseases using outstanding machine learning algorithms namely simple logistic, decision tree, random forest and multilayer perceptron for accurate identification of groundnut diseases. Experiments are conducted with the help of 10-fold cross validation strategy. The results advocate that above mentioned classification algorithms diagnose the groundnut diseases with excellent accuracy level. Simple logistic and multilayer perceptron show outstanding performance than other algorithms and result in 96.37% and 95.80% disease classification accuracy. Random forest and decision tree algorithms provide fair accuracies in less time. These machine learning algorithms can be used in diagnosing other crop diseases also.

Sign in / Sign up

Export Citation Format

Share Document