A Review on Malicious Software Detection using Machine Learning Algorithms

2021 ◽  
Author(s):  
Ravi Kumar Tirandasu ◽  
Y. Prasanth
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Malicious Software

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 Trusted Detection of Ransom ware using Machine Learning Algorithms

2019 ◽  
Vol 8 (9S2) ◽  
pp. 653-656
Keyword(s):  
Machine Learning ◽  
Network Security ◽  
Computer Networks ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
The Internet ◽  
Malicious Software ◽  
Main Target ◽  
Different Types ◽  
Effective Network

Nowadays, the Computer Networks and the internet are increased. Lots of information is accessed and allowed to the users to share the information to the Internet. One of the major issues with internet was different types of attack. Ransomware is a one kind of attack or it is malicious software that threatens to publish the victim's data. A variety of threats is the main target for the effective network security and avoids them from spreading or entering to the networks the network security on computer essential for computer networks. Ransom ware is a critical threat in network security since each day the raising of ransomware gets abundant. The major problem by the researchers is the prediction of ransomware. This paper planned to carry out a review on the different method to detect ransomware. Ransomware detection is very much helpful on minimizing the workload of analyst and for determining the variation in hidden Ransomware samples. Using machine learning algorithms Ransomware detected efficiently and trustfully.

Forecasting US movies box office performances in Turkey using machine learning algorithms

2020 ◽  
Vol 39 (5) ◽  
pp. 6579-6590
Author(s):  
Sandy Çağlıyor ◽  
Başar Öztayşi ◽  
Selime Sezgin
Keyword(s):  
Machine Learning ◽  
Global Economy ◽  
Learning Algorithms ◽  
Forecast Model ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
High Stakes ◽  
Box Office ◽  
Industry Forecast ◽  
The Impact

The motion picture industry is one of the largest industries worldwide and has significant importance in the global economy. Considering the high stakes and high risks in the industry, forecast models and decision support systems are gaining importance. Several attempts have been made to estimate the theatrical performance of a movie before or at the early stages of its release. Nevertheless, these models are mostly used for predicting domestic performances and the industry still struggles to predict box office performances in overseas markets. In this study, the aim is to design a forecast model using different machine learning algorithms to estimate the theatrical success of US movies in Turkey. From various sources, a dataset of 1559 movies is constructed. Firstly, independent variables are grouped as pre-release, distributor type, and international distribution based on their characteristic. The number of attendances is discretized into three classes. Four popular machine learning algorithms, artificial neural networks, decision tree regression and gradient boosting tree and random forest are employed, and the impact of each group is observed by compared by the performance models. Then the number of target classes is increased into five and eight and results are compared with the previously developed models in the literature.

Intelligent system of English composition scoring model based on improved machine learning algorithm

2020 ◽  
pp. 1-11
Author(s):  
Jie Liu ◽  
Lin Lin ◽  
Xiufang Liang
Keyword(s):  
Machine Learning ◽  
Evaluation System ◽  
Intelligent System ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Assessment System ◽  
English Composition ◽  
Region Extraction ◽  
Constraint Model

The online English teaching system has certain requirements for the intelligent scoring system, and the most difficult stage of intelligent scoring in the English test is to score the English composition through the intelligent model. In order to improve the intelligence of English composition scoring, based on machine learning algorithms, this study combines intelligent image recognition technology to improve machine learning algorithms, and proposes an improved MSER-based character candidate region extraction algorithm and a convolutional neural network-based pseudo-character region filtering algorithm. In addition, in order to verify whether the algorithm model proposed in this paper meets the requirements of the group text, that is, to verify the feasibility of the algorithm, the performance of the model proposed in this study is analyzed through design experiments. Moreover, the basic conditions for composition scoring are input into the model as a constraint model. The research results show that the algorithm proposed in this paper has a certain practical effect, and it can be applied to the English assessment system and the online assessment system of the homework evaluation system algorithm system.

The Unlearnable Checkerboard Pattern

2019 ◽  
Vol 1 (2) ◽  
pp. 78-80
Author(s):  
Eric Holloway
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Checkerboard Pattern ◽  
Simple Task

Detecting some patterns is a simple task for humans, but nearly impossible for current machine learning algorithms.  Here, the "checkerboard" pattern is examined, where human prediction nears 100% and machine prediction drops significantly below 50%.

1290-P: Gut Microbiota in New-Onset Pediatric Patients with Type 1 Diabetes: Machine Learning Algorithms to Classify Microorganisms Disease-Linked

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 1290-P
Author(s):  
GIUSEPPE D’ANNUNZIO ◽  
ROBERTO BIASSONI ◽  
MARGHERITA SQUILLARIO ◽  
ELISABETTA UGOLOTTI ◽  
ANNALISA BARLA ◽  
...  
Keyword(s):  
Machine Learning ◽  
Type 1 Diabetes ◽  
Gut Microbiota ◽  
Pediatric Patients ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
New Onset
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