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.

Study on Machine Learning Techniques for Botnet Detection

2020 ◽  
Vol 18 (05) ◽  
pp. 881-888
Author(s):  
L. Silva ◽  
L. Utimura ◽  
K. Costa ◽  
M. Silva ◽  
S. Prado
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Botnet Detection ◽  
Learning Techniques

Imaging and machine learning techniques for diagnosis of Alzheimer’s disease

2016 ◽  
Vol 27 (8) ◽  
pp. 857-870 ◽  
Author(s):  
Golrokh Mirzaei ◽  
Anahita Adeli ◽  
Hojjat Adeli
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Probabilistic Neural Network ◽  
Machine Learning Techniques ◽  
Detection Techniques ◽  
The Past ◽  
Learning Techniques ◽  
Diagnosis Accuracy ◽  
Rate Research

AbstractAlzheimer’s disease (AD) is a common health problem in elderly people. There has been considerable research toward the diagnosis and early detection of this disease in the past decade. The sensitivity of biomarkers and the accuracy of the detection techniques have been defined to be the key to an accurate diagnosis. This paper presents a state-of-the-art review of the research performed on the diagnosis of AD based on imaging and machine learning techniques. Different segmentation and machine learning techniques used for the diagnosis of AD are reviewed including thresholding, supervised and unsupervised learning, probabilistic techniques, Atlas-based approaches, and fusion of different image modalities. More recent and powerful classification techniques such as the enhanced probabilistic neural network of Ahmadlou and Adeli should be investigated with the goal of improving the diagnosis accuracy. A combination of different image modalities can help improve the diagnosis accuracy rate. Research is needed on the combination of modalities to discover multi-modal biomarkers.

A New Framework for Classification of Distributed Denial of Service (DDOS) Attack in Cloud Computing by Machine Learning Techniques

2014 ◽  
Vol 20 (1) ◽  
pp. 175-178
Author(s):  
Mostafa Behzadi ◽  
Ramlan Mahmod ◽  
Mehdi Barati ◽  
Azizol Bin Hj Abdullah ◽  
Mahda Noura
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Denial Of Service ◽  
Machine Learning Techniques ◽  
Distributed Denial Of Service ◽  
Ddos Attack ◽  
Learning Techniques ◽  
New Framework

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 An Empirical Evaluation On Comparative Machine Learning Techniques For Detection of The Distributed Denial of Service (DDoS) Attacks

2020 ◽  
Vol 2 (1) ◽  
pp. 18-27
Author(s):  
Arnold Ojugo ◽  
Andrew Okonji Eboka
Keyword(s):  
Machine Learning ◽  
Denial Of Service ◽  
Empirical Evaluation ◽  
Machine Learning Techniques ◽  
Distributed Denial Of Service ◽  
Ddos Attacks ◽  
Secure Systems ◽  
Data Encoding ◽  
Signature Detection ◽  
Learning Techniques

The advent of the Internet that aided the efficient sharing of resources. Also, it has introduced adversaries whom are today restlessly in their continued efforts at an effective, non-detectable means to invade secure systems, either for fun or personal gains. They achieve these feats via the use of malware, which is both on the rise, wreaks havoc alongside causing loads of financial losses to users. With the upsurge to counter these escapades, users and businesses today seek means to detect these evolving behavior and pattern by these adversaries. It is also to worthy of note that adversaries have also evolved, changing their own structure to make signature detection somewhat unreliable and anomaly detection tedious to network administrators. Our study investigates the detection of the distributed denial of service (DDoS) attacks using machine learning techniques. Results shows that though evolutionary models have been successfully implemented in the detection DDoS, the search for optima is an inconclusive and continuous task. That no one method yields a better optima than hybrids. That with hybrids, users must adequately resolve the issues of data conflicts arising from the dataset to be used, conflict from the adapted statistical methods arising from data encoding, and conflicts in parameter selection to avoid model overtraining, over-fitting and over-parameterization.

scholarly journals An Empirical Study of AML Approach for Credit Card Fraud Detection–Financial Transactions

2019 ◽  
Vol 14 (6) ◽  
pp. 670-690 ◽  
Author(s):  
Ajeet Singh ◽  
Anurag Jain
Keyword(s):  
Machine Learning ◽  
Credit Card ◽  
Evaluation Criteria ◽  
Fraud Detection ◽  
Machine Learning Techniques ◽  
Credit Card Fraud ◽  
Detection Techniques ◽  
Digital World ◽  
Learning Techniques ◽  
Sensitivity Specificity

Credit card fraud is one of the flip sides of the digital world, where transactions are made without the knowledge of the genuine user. Based on the study of various papers published between 1994 and 2018 on credit card fraud, the following objectives are achieved: the various types of credit card frauds has identified and to detect automatically these frauds, an adaptive machine learning techniques (AMLTs) has studied and also their pros and cons has summarized. The various dataset are used in the literature has studied and categorized into the real and synthesized datasets.The performance matrices and evaluation criteria have summarized which has used to evaluate the fraud detection system.This study has also covered the deep analysis and comparison of the performance (i.e sensitivity, specificity, and accuracy) of existing machine learning techniques in the credit card fraud detection area.The findings of this study clearly show that supervised learning, card-not-present fraud, skimming fraud, and website cloning method has been used more frequently.This Study helps to new researchers by discussing the limitation of existing fraud detection techniques and providing helpful directions of research in the credit card fraud detection field.

scholarly journals Detection, Defensive and Mitigation of DDoS Attacks through Machine learning Techniques: A Literature

2019 ◽  
Vol 8 (4) ◽  
pp. 2719-2725
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Ddos Attacks ◽  
Systematic Analysis ◽  
Detection Techniques ◽  
Learning Techniques ◽  
Different Types ◽  
Mitigation Techniques

iNowaday ithe iworld iis icompletely idepend on ithe iinternet iand the day ito iday iactivities of ihuman ilife icompletely idepends ion ithe iinternet. The idependency ion ithe iinternet iallows ithe iattackers ido damage ior iharm ito ithe ilegitimate iuser’s itransactions iand ievents iwhich iis icalled ias iSecurity iattack. Distributed iDenial iof iService iis ione itype iof ithe imost vulnerable iattacks iof itoday’s icyber iworld. iIn ithis ipaper, iwe ipresent ia isurvey iof iDistributed iDenial iof iService iattack, idetection, idefensive iand imitigation iof imachine ilearning iapproaches. This isurvey iarticle iprefer itwo ifamous isupervised imachine ilearning ialgorithms isnamely. (i) Decision itrees, (ii)isupport ivector imachine and ipresented ithe irecent iresearch iworks icarried iout. From ithis isurvey iit iis ilearnt ithat iconnecting supervised imachine ilearning ialgorithm iwith iboosting iprocess will iincrease iprediction iefficiency iand ithere iis ia iwide iscope iin ithis iresearch ielement. We provide a systematic analysis of these attacks including so many motivations and evolutions, different types of attacks analysis so far, detection techniques and mitigation techniques, possible constraints and challenges of existing approaches. iFinally isome iimportant iresearch ipoints are outlined ito iensure isuccessful idetection, idefensive iand mitigation iagainst iDistributed iDenial iof iService attacks

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