scholarly journals Machine Learning-Based Botnet Detection in Software-Defined Network: A Systematic Review

Symmetry ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 866
Author(s):  
Khlood Shinan ◽  
Khalid Alsubhi ◽  
Ahmed Alzahrani ◽  
Muhammad Usman Ashraf
Keyword(s):  
Machine Learning ◽  
Network Management ◽  
Implementation Strategies ◽  
Impact Factors ◽  
Future Research ◽  
Botnet Detection ◽  
Detection Techniques ◽  
Research Areas ◽  
Network Equipment ◽  
Meta Analyses

In recent decades, the internet has grown and changed the world tremendously, and this, in turn, has brought about many cyberattacks. Cybersecurity represents one of the most serious threats to society, and it costs millions of dollars each year. The most significant question remains: Where do these attacks come from? The answer is that botnets provide platforms for cyberattacks. For many organizations, a botnet-assisted attack is a terrifying threat that can cause financial losses and leave global victims in its wake. It is therefore imperative to defend organizations against botnet-assisted attacks. Software defined networking (SDN) has emerged as one of the most promising paradigms for this because it allows exponential increases in the complexity of network management and configuration. SDN has a substantial advantage over traditional approaches with regard to network management because it separates the control plane from network equipment. However, security challenges continue to arise, which raises the need for different types of implementation strategies to spread attack vectors, despite the significant benefits. The main objective of this survey is to assess botnet detection techniques by using systematic reviews and meta-analyses (PRISMA) guidelines. We evaluated various articles published since 2006 in the field of botnet detection, based on machine learning, and from 2015 in the field of SDN. Specifically, we used top-rated journals that featured the highest impact factors. In this paper, we aim to elaborate on several research areas regarding botnet attacks, detection techniques, machine learning, and SDN. We also address current research challenges and propose directions for future research.

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.

Android Malware Detection Techniques: A Literature Review

2020 ◽  
Vol 14 ◽  
Author(s):  
Meghna Dhalaria ◽  
Ekta Gandotra
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Malware Detection ◽  
Future Research ◽  
Android Malware ◽  
Detection Techniques ◽  
Android Malware Detection ◽  
Future Research Directions ◽  
To Come ◽  
Tools And Techniques

Purpose: This paper provides the basics of Android malware, its evolution and tools and techniques for malware analysis. Its main aim is to present a review of the literature on Android malware detection using machine learning and deep learning and identify the research gaps. It provides the insights obtained through literature and future research directions which could help researchers to come up with robust and accurate techniques for classification of Android malware. Design/Methodology/Approach: This paper provides a review of the basics of Android malware, its evolution timeline and detection techniques. It includes the tools and techniques for analyzing the Android malware statically and dynamically for extracting features and finally classifying these using machine learning and deep learning algorithms. Findings: The number of Android users is expanding very fast due to the popularity of Android devices. As a result, there are more risks to Android users due to the exponential growth of Android malware. On-going research aims to overcome the constraints of earlier approaches for malware detection. As the evolving malware are complex and sophisticated, earlier approaches like signature based and machine learning based are not able to identify these timely and accurately. The findings from the review shows various limitations of earlier techniques i.e. requires more detection time, high false positive and false negative rate, low accuracy in detecting sophisticated malware and less flexible. Originality/value: This paper provides a systematic and comprehensive review on the tools and techniques being employed for analysis, classification and identification of Android malicious applications. It includes the timeline of Android malware evolution, tools and techniques for analyzing these statically and dynamically for the purpose of extracting features and finally using these features for their detection and classification using machine learning and deep learning algorithms. On the basis of the detailed literature review, various research gaps are listed. The paper also provides future research directions and insights which could help researchers to come up with innovative and robust techniques for detecting and classifying the Android malware.

Alignment for Advanced Machine Learning Systems

2020 ◽  
pp. 342-382
Author(s):  
Jessica Taylor ◽  
Eliezer Yudkowsky ◽  
Patrick LaVictoire ◽  
Andrew Critch
Keyword(s):  
Machine Learning ◽  
Side Effects ◽  
Objective Function ◽  
Unintended Consequences ◽  
Learning Systems ◽  
Large Impact ◽  
Future Research ◽  
Objective Functions ◽  
Research Areas ◽  
The Right

This chapter surveys eight research areas organized around one question: As learning systems become increasingly intelligent and autonomous, what design principles can best ensure that their behavior is aligned with the interests of the operators? The chapter focuses on two major technical obstacles to AI alignment: the challenge of specifying the right kind of objective functions and the challenge of designing AI systems that avoid unintended consequences and undesirable behavior even in cases where the objective function does not line up perfectly with the intentions of the designers. The questions surveyed include the following: How can we train reinforcement learners to take actions that are more amenable to meaningful assessment by intelligent overseers? What kinds of objective functions incentivize a system to “not have an overly large impact” or “not have many side effects”? The chapter discusses these questions, related work, and potential directions for future research, with the goal of highlighting relevant research topics in machine learning that appear tractable today.

scholarly journals Automated Reasoning for Explainable Artificial Intelligence

10.29007/4b7h ◽  
2018 ◽  
Author(s):  
Maria Paola Bonacina
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Automated Reasoning ◽  
Position Statement ◽  
Short Paper ◽  
Future Research ◽  
Grand Challenge ◽  
New Paradigm ◽  
Research Areas ◽  
Explainable Artificial Intelligence

Reasoning and learning have been considered fundamental features of intelligence ever since the dawn of the field of artificial intelligence, leading to the development of the research areas of automated reasoning and machine learning. This short paper is a non-technical position statement that aims at prompting a discussion of the relationship between automated reasoning and machine learning, and more generally between automated reasoning and artificial intelligence. We suggest that the emergence of the new paradigm of XAI, that stands for eXplainable Artificial Intelligence, is an opportunity for rethinking these relationships, and that XAI may offer a grand challenge for future research on automated reasoning.

Machine Learning Applications for Anomaly Detection

2019 ◽  
pp. 49-83
Author(s):  
Teguh Wahyono ◽  
Yaya Heryadi
Keyword(s):  
Climate Change ◽  
Machine Learning ◽  
Anomaly Detection ◽  
Research Trend ◽  
Application Development ◽  
Detection Techniques ◽  
Airline Safety ◽  
Research Areas ◽  
Machine Learning Applications ◽  
Meta Approach

The aim of this chapter is to describe and analyze the application of machine learning for anomaly detection. The study regarding the anomaly detection is a very important thing. The various phenomena often occur related to the anomaly study, such as the occurrence of an extreme climate change, the intrusion detection for the network security, the fraud detection for e-banking, the diagnosis for engines fault, the spacecraft anomaly detection, the vessel track, and the airline safety. This chapter is an attempt to provide a structured and a broad overview of extensive research on anomaly detection techniques spanning multiple research areas and application domains. Quantitative analysis meta-approach is used to see the development of the research concerned with those matters. The learning is done on the method side, the techniques utilized, the application development, the technology utilized, and the research trend, which is developed.

scholarly journals Cervical Cancer: Machine Learning Techniques for Detection, Risk Factors and Prevention Measures

2020 ◽  
Vol 9 (3) ◽  
pp. 158-163
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Cervical Cancer ◽  
Preventive Measures ◽  
Hpv Infection ◽  
Machine Learning Techniques ◽  
Future Research ◽  
Prevention Measures ◽  
Detection Techniques ◽  
Learning Techniques

Cervical Cancer is considered the fourth most common female malignancy worldwide and represents a major global health challenge. As a result, in recent years, various proposals and researches have been conducted. This study aims to analyze the data presented in current researches regarding cervical cancer and contribute to future research, all through the framework of literature review, based on 3 research questions: Q1: What are the risk factors that cause cervical cancer? Q2: What preventive measures are currently established for cervical cancer? and, Q3: What are the techniques to detect cervical cancer? Findings show that detection techniques are complementary since they are categorized under machine learning. Therefore, we recommend that further study be promoted in these techniques as they are helpful in the detection process. In addition, risk factors can be considered for a greater scope in detection, such as HPV infection, since it is the most relevant factor for the development of cervical cancer. Finally, we suggest to conduct further research on preventive measures for cervical cancer.

scholarly journals Reducing Injuries in Soccer (Football): an Umbrella Review of Best Evidence Across the Epidemiological Framework for Prevention

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Oluwatoyosi B. A. Owoeye ◽  
Mitchell J. VanderWey ◽  
Ian Pike
Keyword(s):  
Injury Prevention ◽  
Scientific Evidence ◽  
Implementation Strategies ◽  
Future Research ◽  
Dissemination And Implementation ◽  
Umbrella Review ◽  
Significant Burden ◽  
Dissemination And Implementation Research ◽  
Financial Burdens ◽  
Meta Analyses

Abstract Soccer is the most popular sport in the world. Expectedly, the incidence of soccer-related injuries is high and these injuries exert a significant burden on individuals and families, including health and financial burdens, and on the socioeconomic and healthcare systems. Using established injury prevention frameworks, we present a concise synthesis of the most recent scientific evidence regarding injury rates, characteristics, mechanisms, risk and protective factors, interventions for prevention, and implementation of interventions in soccer. In this umbrella review, we elucidate the most recent available evidence gleaned primarily from systematic reviews and meta-analyses. Further, we express the exigent need to move current soccer injury prevention research evidence into action for improved player outcomes and widespread impact through increased attention to dissemination and implementation research. Additionally, we highlight the importance of an enabling context and effective implementation strategies for the successful integration of evidence-based injury prevention programs into real-world soccer settings. This narrative umbrella review provides guidance to inform future research, practice, and policy towards reducing injuries among soccer players.

scholarly journals Intelligent COVID-19 Forecasting, Diagnoses and Monitoring Systems: A Survey

2021 ◽  
Author(s):  
Nasreen Anjum ◽  
Amna Asif, ◽  
Mehreen Kiran ◽  
Fouzia Jabeen ◽  
Zhaohui Yang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Herd Immunity ◽  
Machine Learning Algorithms ◽  
Future Research ◽  
Monitoring Systems ◽  
Extensive Literature ◽  
Awareness Campaigns ◽  
Research Areas ◽  
Wide Range

<div>To date, the novel Corona virus (SARS-CoV-2) has infected millions and has caused the deaths of thousands of people around the world. At the moment, five antibodies, two from China, two from the U.S., and one from the UK, have already been widely utilized and numerous vaccines are under the trail process. In order to reach herd immunity, around 70% of the population would need to be inoculated. It may take several years to hinder the spread of SARS-CoV-2. Governments and concerned authorities have taken stringent measurements such as enforcing partial, complete, or smart lockdowns, building temporary medical facilities, advocating social distancing, and mandating masks in public as well as setting up awareness campaigns. Furthermore, there have been massive efforts in various research areas and a wide variety of tools, technologies and techniques have been explored and developed to combat the war against this pandemic. Interestingly, machine learning algorithms and internet of Things (IoTs) technology are the pioneers in this race. Up till now, several real-time and intelligent COVID-19 forecasting, diagnosing, and monitoring systems have been proposed to tackle the COVID-19 pandemic. In this article based on our extensive literature review, we provide a taxonomy based on the intelligent COVID-19 forecasting, diagnosing, and monitoring systems. We review the available literature extensively under the proposed taxonomy and have analyzed a significantly wide range of machine learning algorithms and IoTs which can be used in predicting the spread of COVID-19 and in diagnosing and monitoring the infected individuals. Furthermore, we identify the challenges and also provide our vision about the future research on COVID-19.</div>

scholarly journals Effect of Social Media Botnets and their Detection Techniques

2020 ◽  
Vol 9 (6) ◽  
pp. 530-533
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Online Social Network ◽  
Communication Channels ◽  
Social Intervention ◽  
Botnet Detection ◽  
Learning Mechanism ◽  
Detection Techniques ◽  
Illegal Activities ◽  
Favorite Place

The Online Social Network (ONS) or Social Media have become most popular platform for millions of users for their activities and at the same time it has become favorite place for cyber criminals for their illegal activities, generally known as social botnets, which uses different techniques to spread their information on social media like facebook, twitter, renren, linkedin etc. Several researchers have tried to detect several social botnets with different detection techniques. To avoid the detection, social botnets are now using advanced command & control (C&C) communication channels like hash tags, fraud click, friend requests, images, videos etc. Image Steganography techniques are now widely being used to carry out attacks. In this paper, the primary discussion is related to effects of social media botnets along with the different techniques for botnet detection. It also, explores the use of machine learning mechanism, thereby detecting the intrusions in stegano images. Thus, an effort has been made to localize the factors that have a major role in social intervention as a whole.

scholarly journals Towards a Universal Features Set for IoT Botnet Attacks Detection

2020 ◽  
Author(s):  
Faisal Hussain ◽  
Syed Ghazanfar Abbas ◽  
Ubaid U. Fayyaz ◽  
Ghalib A. Shah ◽  
Abdullah Toqeer ◽  
...  
Keyword(s):  
Machine Learning ◽  
Denial Of Service ◽  
Distributed Denial Of Service ◽  
Learning Models ◽  
Botnet Detection ◽  
Detection Techniques ◽  
Machine Learning Model ◽  
Attack Patterns ◽  
Iot Devices ◽  
Machine Learning Models

Abstract The security pitfalls of IoT devices make it easy for the attackers to exploit the IoT devices and make them a part of a botnet. Once hundreds of thousands of IoT devices are compromised and become the part of a botnet, the attackers use this botnet to launch the large and complex distributed denial of service (DDoS) attacks which take down the target websites or services and make them unable to respond the legitimate users. So far, many botnet detection techniques have been proposed but their performance is limited to a specific dataset on which they are trained. This is because the features used to train a machine learning model on one botnet dataset, do not perform well on other datasets due to the diversity of attack patterns. Therefore, in this paper, we propose a universal features set to better detect the botnet attacks regardless of the underlying dataset. The proposed features set manifest preeminent results for detecting the botnet attacks when tested the trained machine learning models over three different botnet attack datasets.

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