Performance Analysis of Machine Learning based Botnet Detection and Classification Models for Information Security

2019 ◽  
pp. 44-53
Author(s):  
Ahmed A. Elngar ◽  
◽  
Salah-ddine KRIT ◽  
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Performance Analysis ◽  
Comprehensive Evaluation ◽  
Classification Models ◽  
Botnet Detection ◽  
Detection Model ◽  
C4.5 Decision Tree ◽  
Stage Process ◽  
Detection Schemes

Botnet detection becomes a challenging issue in several domains like cybersecurity, finance, healthcare, law, order, etc. The botnet represents a set of cooperated Internet-linked devices managed by cyber criminals to start coordinated attacks and carry out different malicious events. As the botnets are seamlessly dynamic with the developing countermeasures presented by network and host-based detection schemes, conventional methods have failed to achieve enough safety for botnet threats. Therefore, machine learning (ML) models have been developed to detect and classify botnets for cybersecurity. In this view, this paper performs a comprehensive evaluation of different ML-based botnet detection and classification models. The botnet detection model involves a three-stage process, namely preprocessing, feature extraction, and classification. In this study, four ML models such as C4.5 Decision Tree, bagging, boosting, and Adaboost are employed for classification purposes. To highlight the performance of the four ML models, an extensive set of simulations was performed. The obtained results pointed out that the ML models can attain enhanced botnet detection performance.

scholarly journals Honeypot Coupled Machine Learning Model for Botnet Detection and Classification in IoT Smart Factory – An Investigation

2021 ◽  
Vol 335 ◽  
pp. 04003
Author(s):  
Seungjin Lee ◽  
Azween Abdullah ◽  
N.Z. Jhanjhi ◽  
S.H. Kok
Keyword(s):  
Machine Learning ◽  
Process Management ◽  
Denial Of Service ◽  
The United States ◽  
Smart Factory ◽  
High Detection Rate ◽  
Botnet Detection ◽  
Detection Model ◽  
Log File ◽  
Smart Factories

In the United States, the manufacturing ecosystem is rebuilt and developed through innovation with the promotion of AMP 2.0. For this reason, the industry has spurred the development of 5G, Artificial Intelligence (AI), and Machine Learning (ML) technologies which is being applied on the smart factories to integrate production process management, product service and distribution, collaboration, and customized production requirements. These smart factories need to effectively solve security problems with a high detection rate for a smooth operation. However, number of security related cases occurring in the smart factories has been increasing due to botnet Distributed Denial of Service (DDoS) attacks that threaten the network security operated on the Internet of Things (IoT) platform. Against botnet attacks, security network of the smart factory must improve its defensive capability. Among many security solutions, botnet detection using honeypot has been shown to be effective in early studies. In order to solve the problem of closely monitoring and acquiring botnet attack behaviour, honeypot is a method to detect botnet attackers by intentionally creating resources within the network. As a result, the traced content is recorded in a log file. In addition, these log files are classified quickly with high accuracy with a support of machine learning operation. Hence, productivity is increase, while stability of the smart factory is reinforced. In this study, a botnet detection model was proposed by combining honeypot with machine learning, specifically designed for smart factories. The investigation was carried out in a hardware configuration virtually mimicking a smart factory environment.

scholarly journals Exploring the impact of similarity index on the accuracy of a phishing site detection model using machine learning

2021 ◽  
Vol 2131 (2) ◽  
pp. 022076
Author(s):  
O S Danko ◽  
T A Medvedeva
Keyword(s):  
Machine Learning ◽  
Binary Classification ◽  
Similarity Index ◽  
Classification Models ◽  
Detection Model ◽  
Advantages And Disadvantages ◽  
The Impact

Abstract In this paper, the problem of phishing site detection using machine learning is discussed. The main goal is to study the effectiveness of various binary classification models when extracting only lexical features from a URL. Special attention has been given to the analysis of features obtained from the domain by calculating the similarity index using the whitelist. After training and testing the models, accuracy metrics were calculated and the results were compared. The lexical features that have the greatest weight for the classification of URLs are highlighted, and the advantages and disadvantages of this approach are described.

scholarly journals Computer aid screening of COVID-19 using X-ray and CT scan images: An inner comparison

2021 ◽  
pp. 1-14
Author(s):  
Prabira Kumar Sethy ◽  
Santi Kumari Behera ◽  
Komma Anitha ◽  
Chanki Pandey ◽  
M.R. Khan
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Transfer Learning ◽  
Classification Models ◽  
X Ray ◽  
Computer Aid ◽  
Deep Feature ◽  
Chest X Ray ◽  
Image Set ◽  
Deep Feature Extraction

The objective of this study is to conduct a critical analysis to investigate and compare a group of computer aid screening methods of COVID-19 using chest X-ray images and computed tomography (CT) images. The computer aid screening method includes deep feature extraction, transfer learning, and machine learning image classification approach. The deep feature extraction and transfer learning method considered 13 pre-trained CNN models. The machine learning approach includes three sets of handcrafted features and three classifiers. The pre-trained CNN models include AlexNet, GoogleNet, VGG16, VGG19, Densenet201, Resnet18, Resnet50, Resnet101, Inceptionv3, Inceptionresnetv2, Xception, MobileNetv2 and ShuffleNet. The handcrafted features are GLCM, LBP & HOG, and machine learning based classifiers are KNN, SVM & Naive Bayes. In addition, the different paradigms of classifiers are also analyzed. Overall, the comparative analysis is carried out in 65 classification models, i.e., 13 in deep feature extraction, 13 in transfer learning, and 39 in the machine learning approaches. Finally, all classification models perform better when applying to the chest X-ray image set as comparing to the use of CT scan image set. Among 65 classification models, the VGG19 with SVM achieved the highest accuracy of 99.81%when applying to the chest X-ray images. In conclusion, the findings of this analysis study are beneficial for the researchers who are working towards designing computer aid tools for screening COVID-19 infection diseases.

Feature extraction and prediction of Dengue Outbreaks

2020 ◽  
pp. 216-222
Author(s):  
Kunal Parikh ◽  
Tanvi Makadia ◽  
Harshil Patel
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Developing Countries ◽  
Feature Extraction ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Health Concerns ◽  
The World ◽  
Dengue Outbreaks

Dengue is unquestionably one of the biggest health concerns in India and for many other developing countries. Unfortunately, many people have lost their lives because of it. Every year, approximately 390 million dengue infections occur around the world among which 500,000 people are seriously infected and 25,000 people have died annually. Many factors could cause dengue such as temperature, humidity, precipitation, inadequate public health, and many others. In this paper, we are proposing a method to perform predictive analytics on dengue’s dataset using KNN: a machine-learning algorithm. This analysis would help in the prediction of future cases and we could save the lives of many.

Document Preprocessing with TF-IDF to Improve the Polarity Classification Performance of Unstructured Sentiment Analysis

2020 ◽  
pp. 235-242
Author(s):  
Farrikh Alzami ◽  
Erika Devi Udayanti ◽  
Dwi Puji Prabowo ◽  
Rama Aria Megantara
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Random Forest ◽  
Sentiment Analysis ◽  
Classification Performance ◽  
Document Preparation ◽  
Learning Models ◽  
Polarity Classification ◽  
Negative Sentiment ◽  
Machine Learning Models

Sentiment analysis in terms of polarity classification is very important in everyday life, with the existence of polarity, many people can find out whether the respected document has positive or negative sentiment so that it can help in choosing and making decisions. Sentiment analysis usually done manually. Therefore, an automatic sentiment analysis classification process is needed. However, it is rare to find studies that discuss extraction features and which learning models are suitable for unstructured sentiment analysis types with the Amazon food review case. This research explores some extraction features such as Word Bags, TF-IDF, Word2Vector, as well as a combination of TF-IDF and Word2Vector with several machine learning models such as Random Forest, SVM, KNN and Naïve Bayes to find out a combination of feature extraction and learning models that can help add variety to the analysis of polarity sentiments. By assisting with document preparation such as html tags and punctuation and special characters, using snowball stemming, TF-IDF results obtained with SVM are suitable for obtaining a polarity classification in unstructured sentiment analysis for the case of Amazon food review with a performance result of 87,3 percent.

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