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Creditability-based weighted voting for reducing false positives and negatives in intrusion detection

Ying-Dar Lin, Yuan-Cheng Lai, Cheng-Yuan Ho, Wei-Hsuan Tai
2013 Computers & security  
False positives (FPs) and false negatives (FNs) happen in every Intrusion Detection System (IDS). How often they occur is regarded as a measurement of the accuracy of the system.  ...  In this paper, we propose a method to reduce FPs and FNs by applying a creditability-based weighted voting (CWV) scheme to the outcomes of multiple IDSs.  ...  Acknowledgments This work was supported in part by National Science Council and Institute of Information Industry in Taiwan.  ... 
doi:10.1016/j.cose.2013.09.010 fatcat:svpoynova5cevarib3ycf4bttu

Detailed Analysis of Intrusion Detection using Machine Learning Algorithms

2020 International journal of recent technology and engineering  
A significant number of techniques have been developed which are based on machine learning approaches to detect these intrusion attacks.  ...  To detect an intrusion attack in a system connected over a network is one of the most challenging tasks in today's world.  ...  To improve the intrusion detection system and reduce the false negative and false positive, which can be tested by the application of different algorithms.  ... 
doi:10.35940/ijrte.a2127.059120 fatcat:zvebsquninfgxmqp3djtxgcvta

A New Ensemble-Based Intrusion Detection System for Internet of Things

Adeel Abbas, Muazzam A. Khan, Shahid Latif, Maria Ajaz, Awais Aziz Shah, Jawad Ahmad
2021 Arabian Journal for Science and Engineering  
In this study, an ensemble-based intrusion detection model has been proposed.  ...  In order to secure data from misuse and unusual attempts, several intrusion detection systems (IDSs) have been proposed to detect the malicious activities on the basis of predefined attack patterns.  ...  that have been accurately classified as attack. • FP stands for false positive, normal data points that have been incorrectly categorized as attack. • FN stands for false negative, attack data points  ... 
doi:10.1007/s13369-021-06086-5 fatcat:pswgacyhbjhf3hwrwyyfddyuum

A Neural Network Ensemble With Feature Engineering for Improved Credit Card Fraud Detection

Ebenezer Esenogho, Ibomoiye Domor Mienye, Theo G. Swart, Kehinde Aruleba, George Obaido
2022 IEEE Access  
Recent advancements in electronic commerce and communication systems have significantly increased the use of credit cards for both online and regular transactions.  ...  Also, using conventional machine learning algorithms for credit card fraud detection is inefficient due to their design, which involves a static mapping of the input vector to output vectors.  ...  It has achieved excellent performance in several applications, including credit card fraud detection [1] and intrusion detection systems [23] .  ... 
doi:10.1109/access.2022.3148298 fatcat:or4qwpuunvglxfy54ci7szzzli

A Comparative Study of Hidden Markov Model and Support Vector Machine in Anomaly Intrusion Detection

Ruchi Jain, Nasser S. Abouzakhar
2013 Journal of Internet Technology and Secured Transaction  
This paper aims to analyse the performance of Hidden Markov Model (HMM) and Support Vector Machine (SVM) for anomaly intrusion detection.  ...  The publicly available KDD Cup 1999 dataset has been used in training and evaluation of such techniques.  ...  Peter lane of University of Hertfordshire with help of Weka Tools, Romil Jain of Santa Clara University with background of HMM and SVM.  ... 
doi:10.20533/jitst.2046.3723.2013.0023 fatcat:zmyeqvmgundqnpxz67wqfcqiry

Experimental Study of Machine Learning Methods in Anomaly Detection
Anomaliyaların aşkarlanmasında maşın təlimi metodlarının eksperimental tədqiqi

Makrufa Hajirahimova, Institute of Information Technology, Azerbaijan National Academy of Sciences, Leyla Yusifova, Institute of Information Technology, Azerbaijan National Academy of Sciences
2022 Problems of Information Technology  
Existing security systems and devices are insufficient in the detection of intruders' attacks on network infrastructure, and they considered to be outdated for storing and analyzing large network traffic  ...  data in terms of size, speed, and diversity.  ...  ROC curve created on the basis of test data Table 1 . 1 Confusion matrix Actual Positive Negative Positive True Positive False Positive TP FP Negative False Negative True Negative FN TN  ... 
doi:10.25045/jpit.v13.i1.02 fatcat:4ojvehkk2bdrbcbinnffyglnty

Effective detection of sophisticated online banking fraud on extremely imbalanced data

Wei Wei, Jinjiu Li, Longbing Cao, Yuming Ou, Jiahang Chen
2012 World wide web (Bussum)  
Its detection is a typical use case of the broad-based Wisdom Web of Things (W2T) methodology.  ...  effective detection become more and more important and challenging.  ...  Acknowledgements This work is partially sponsored by the Australian Research Council Discovery grant (DP1096218) and Linkage grant (LP100200774).  ... 
doi:10.1007/s11280-012-0178-0 fatcat:n5mvzx25tjbvdfhmcx4p2yvo4i

Credit Card Fraud Detection Using AdaBoost and Majority Voting

Kuldeep Randhawa, Chu Kiong Loo, Manjeevan Seera, Chee Peng Lim, Asoke K. Nandi
2018 IEEE Access  
The experimental results positively indicate that the majority voting method achieves good accuracy rates in detecting fraud cases in credit cards.  ...  Then, hybrid methods which use AdaBoost and majority voting methods are applied. To evaluate the model efficacy, a publicly available credit card data set is used.  ...  The MCC metric has been adopted as a performance measure, as it takes into account the true and false positive and negative predicted outcomes.  ... 
doi:10.1109/access.2018.2806420 fatcat:htfwvk7egjazvlyhcxqgzshqva

Random-Forests-Based Network Intrusion Detection Systems

Jiong Zhang, M. Zulkernine, A. Haque
2008 IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews)  
, our anomaly detection approach achieves higher detection rate when the false positive rate is low; and the presented hybrid system can improve the overall performance of the aforementioned IDSs.  ...  Therefore, we propose new systematic frameworks that apply a data mining algorithm called random forests in misuse, anomaly, and hybrid-network-based IDSs.  ...  Actually, outlier detection has been used in a number of practical applications such as credit card fraud detection, voting irregularity analysis, and severe weather prediction [22] .  ... 
doi:10.1109/tsmcc.2008.923876 fatcat:wkimolx7orghzpioe5tgquvidq

Data Fusion for Network Intrusion Detection: A Review

Guoquan Li, Zheng Yan, Yulong Fu, Hanlu Chen
2018 Security and Communication Networks  
In this article, we focus on DF techniques for network intrusion detection and propose a specific definition to describe it.  ...  In order to solve these problems, data fusion (DF) has been applied into network intrusion detection and has achieved good results.  ...  Besides, the inherent weakness of NIDSs, such as high false positives (FP) and high false negatives (FN), raises urgent requests on effective solutions.  ... 
doi:10.1155/2018/8210614 fatcat:v3s5acnt65gevp34k5hj4at2ra

An Improved Method to Detect Intrusion Using Machine Learning Algorithms

Urvashi Modi, Anurag Jain
2016 Informatics Engineering an International Journal  
In our paper we use KDDCUP 99 dataset to analyze efficiency of intrusion detection with different machine learning algorithms like Bayes, NaiveBayes, J48, J48Graft and Random forest.  ...  An intrusion detection system detects various malicious behaviors and abnormal activities that might harm security and trust of computer system.  ...  Here, TP is True Positive, FP is False Positive, and FN is False Negative. A TP is a case, which is truly an attack and is acknowledged as attack by the proposed technique.  ... 
doi:10.5121/ieij.2016.4203 fatcat:6yn7a6hcjffqhmyaijg2cq67zi

Survey on Incremental Approaches for Network Anomaly Detection [article]

Monowar H. Bhuyan and D. K. Bhattacharyya and J. K. Kalita
2012 arXiv   pre-print
Anomaly detection systems face many problems including high rate of false alarm, ability to work in online mode, and scalability.  ...  System administrators can attempt to prevent such attacks using intrusion detection tools and systems. There are many commercially available signature-based Intrusion Detection Systems (IDSs).  ...  This work is also partially supported by NSF grants CNS-0851783 and CNS-0958576. The authors are thankful to the funding agencies.  ... 
arXiv:1211.4493v2 fatcat:vqmysyr2fnfy3bjismgtt4pjku

Towards Optimization of Malware Detection using Extra-Tree and Random Forest Feature Selections on Ensemble Classifiers

Fadare Oluwaseun Gbenga, Adetunmbi Adebayo Olusola, Oyinloye Oghenerukevwe Elohor
2021 International journal of recent technology and engineering  
The study results uncover the tree-based ensemble model is proficient and successful for malware classification.  ...  There have been several attempts in curbing the menace using a signature-based approach and in recent times, machine learning techniques have been extensively explored.  ...  (OA), False Positive, False Negative, True Positive, True Negative, Recall, Precision, F1-Score, False Positive Rate, ROC, Cohen Kappa, and AUC.  ... 
doi:10.35940/ijrte.f5545.039621 fatcat:qp3xmw6pmvb67hwrcmhc2wvtey

Development of Data Mining Models Based on Features Ranks Voting (FRV)

Mofreh A. Hogo
2022 Computers Materials & Continua  
It merges the benefits of the different features selection algorithms to specify the features ranks in the dataset correctly and robustly; based on the feature ranks and voting algorithm.  ...  It successes to develop data mining models for the Hungarian CAD dataset with Acc. of 96.8%, and with Acc. of 96% for the Z-Alizadeh Sani CAD dataset compared with 83.94% and 92.56% respectively in [48  ...  An efficient feature selection-based Bayesian and Rough set approach for intrusion detection presented in [43] aimed at improving the performance of the intrusion detection system.  ... 
doi:10.32604/cmc.2022.027300 fatcat:gnvtp7fbazb4fb543wzgxqcog4

Generalized Insider Attack Detection Implementation using NetFlow Data [article]

Yash Samtani, Jesse Elwell
2020 arXiv   pre-print
We combine these techniques to limit the number of false positives to an acceptable level required for real-world deployments by using One-Class SVM to check for anomalies detected by the proposed Bi-clustering  ...  We show that our approach is a promising tool for insider attack detection in realistic settings.  ...  Previous Work Anomaly detection is a general process applied in many different areas, such as for cyber intrusion detection, credit card fraud detection, medical diagnosis and fault detection.  ... 
arXiv:2010.15697v1 fatcat:d5ro4ibffvbjtbv5m3aex3f3ku
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