A Bibliometric Analysis of Botnet Detection Techniques

2019 ◽  
pp. 345-365 ◽  
Author(s):  
Shehu Amina ◽  
Raul Vera ◽  
Tooska Dargahi ◽  
Ali Dehghantanha
Keyword(s):  
Bibliometric Analysis ◽  
Botnet Detection ◽  
Detection Techniques

Botnet and Internet of Things (IoTs)

2020 ◽  
pp. 304-316
Author(s):  
Kamal Alieyan ◽  
Ammar Almomani ◽  
Rosni Abdullah ◽  
Badr Almutairi ◽  
Mohammad Alauthman
Keyword(s):  
Internet Of Things ◽  
Cyber Attacks ◽  
The Internet ◽  
Ddos Attacks ◽  
Botnet Detection ◽  
The Public ◽  
Detection Techniques ◽  
Channel Information ◽  
Iot Devices ◽  
The Internet Of Things

In today's internet world the internet of things (IoT) is becoming the most significant and developing technology. The primary goal behind the IoT is enabling more secure existence along with the improvement of risks at various life levels. With the arrival of IoT botnets, the perspective towards IoT products has transformed from enhanced living enabler into the internet of vulnerabilities for cybercriminals. Of all the several types of malware, botnet is considered as really a serious risk that often happens in cybercrimes and cyber-attacks. Botnet performs some predefined jobs and that too in some automated fashion. These attacks mostly occur in situations like phishing against any critical targets. Files sharing channel information are moved to DDoS attacks. IoT botnets have subjected two distinct problems, firstly, on the public internet. Most of the IoT devices are easily accessible. Secondly, in the architecture of most of the IoT units, security is usually a reconsideration. This particular chapter discusses IoT, botnet in IoT, and various botnet detection techniques available in IoT.

Botnet and Internet of Things (IoTs)

2021 ◽  
pp. 138-150
Author(s):  
Kamal Alieyan ◽  
Ammar Almomani ◽  
Rosni Abdullah ◽  
Badr Almutairi ◽  
Mohammad Alauthman
Keyword(s):  
Internet Of Things ◽  
Cyber Attacks ◽  
The Internet ◽  
Ddos Attacks ◽  
Botnet Detection ◽  
The Public ◽  
Detection Techniques ◽  
Channel Information ◽  
Iot Devices ◽  
The Internet Of Things

In today's internet world the internet of things (IoT) is becoming the most significant and developing technology. The primary goal behind the IoT is enabling more secure existence along with the improvement of risks at various life levels. With the arrival of IoT botnets, the perspective towards IoT products has transformed from enhanced living enabler into the internet of vulnerabilities for cybercriminals. Of all the several types of malware, botnet is considered as really a serious risk that often happens in cybercrimes and cyber-attacks. Botnet performs some predefined jobs and that too in some automated fashion. These attacks mostly occur in situations like phishing against any critical targets. Files sharing channel information are moved to DDoS attacks. IoT botnets have subjected two distinct problems, firstly, on the public internet. Most of the IoT devices are easily accessible. Secondly, in the architecture of most of the IoT units, security is usually a reconsideration. This particular chapter discusses IoT, botnet in IoT, and various botnet detection techniques available in IoT.

scholarly journals A STUDY ON ADVANCED BOTNETS DETECTION IN VARIOUS COMPUTING SYSTEMS USING MACHINE LEARNING TECHNIQUES

2020 ◽  
pp. 167-173
Author(s):  
K. Vamshi Krishna
Keyword(s):  
Machine Learning ◽  
Network Traffic ◽  
Emerging Technologies ◽  
Denial Of Service ◽  
Machine Learning Techniques ◽  
Botnet Detection ◽  
Detection Techniques ◽  
Learning Techniques ◽  
Network Equipment ◽  
Internet Network

Due to the rapid growth and use of Emerging technologies such as Artificial Intelligence, Machine Learning and Internet of Things, Information industry became so popular, meanwhile these Emerging technologies have brought lot of impact on human lives and internet network equipment has increased. This increment of internet network equipment may bring some serious security issues. A botnet is a number of Internet-connected devices, each of which is running one or more bots.The main aim of botnet is to infect connected devices and use their resource for automated tasks and generally they remain hidden. Botnets can be used to perform Distributed Denial-of-Service (DDoS) attacks, steal data, send spam, and allow the attacker to access the device and its connection. In this paper we are going to address the advanced Botnet detection techniques using Machine Learning. Traditional botnet detection uses manual analysis and blacklist, and the efficiency is very low. Applying machine learning to batch automatic detection of botnets can greatly improve the efficiency of detection. Using machine learning to detect botnets, we need to collect network traffic and extract traffic characteristics, and then use X-Means, SVM algorithm to detect botnets. According to the difference of detection features, botnet detection based on machine learning technology is divided into network traffic analysis and correlation analysis-based detection technology. KEYWORDS: Botnet, Study, Security, Internet-network, Machine Learning, Techniques.

scholarly journals A Systematic Review Analysis for Mobile Botnet Detection using GPS Exploitation

2018 ◽  
Vol 2 ◽  
Author(s):  
Muhammad Yusof ◽  
Madihah Mohd Saudi ◽  
Farida Hazwani Mohd Ridzuan
Keyword(s):  
Systematic Review ◽  
Research Paper ◽  
Confidential Information ◽  
Botnet Detection ◽  
Detection Techniques ◽  
Review Analysis ◽  
Mobile Botnet ◽  
Cyber Threat ◽  
Android Platform

At present, mobile botnet has become a cyber threat for smartphone users especially on the Android platform. It has the capabilities to exploit the vulnerabilities and steal confidential information in the victim’s smartphone. Zeus, DroidDream and MisoSMS are examples of mobile botnets that have affected thousands of users worldwide. Therefore, this research paper presents a systematic review analysis on the existing techniques for mobile botnet detection techniques. It discusses the strengths and the weaknesses of the existing mobile botnet detection techniques and related works of mobile botnet that exploit GPS. This research paper can be used as a reference and guidance for those with the same interest.

Exploring the knowledge domain of earthquake prediction based on bibliometric analysis and text mining

2021 ◽  
pp. 1-17
Author(s):  
Xiaorong He
Keyword(s):  
Text Mining ◽  
Bibliometric Analysis ◽  
Earthquake Prediction ◽  
Citation Index ◽  
Knowledge Domain ◽  
Scientific Publications ◽  
Main Research ◽  
Detection Techniques ◽  
Subject Categories ◽  
Earthquake Research

Earthquake prediction is one of the important themes of earthquake research, and it is also a very difficult scientific problem in the world. In this study, a bibliometric analysis is conducted on the scientific publications about earthquake prediction indexed in SCIE (Science Citation Index Expanded) and SSCI (Social Sciences Citation Index) databases during the past two decades (1998–2017). The subject categories, annual and journal distributions, leading countries/regions and institutions are investigated in this field. The main research topics are identified through text mining method. The research trends are explored by keyword co-occurrence analysis and bursting keywords detection techniques. The results of this study are helpful for scholars in this field to find the knowledge structure and important participants. It is also helpful for scholars to seize the current research hotspots and future development trends in this field.

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