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Journal articles on the topic 'NSL –KDD Data set'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

Shukla, Alok Kumar, and Pradeep Singh. "Building an Effective Approach toward Intrusion Detection Using Ensemble Feature Selection." International Journal of Information Security and Privacy 13, no. 3 (2019): 31–47. http://dx.doi.org/10.4018/ijisp.201907010102.

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The duplicate and insignificant features present in the data set to cause a long-term problem in the classification of network or web traffic. The insignificant features not only decrease the classification performance but also prevent a classifier from making accurate decisions, exclusively when substantial volumes of data are managed. In this article, the author introduced an ensemble feature selection (EFS) technique, where multiple homogeneous feature selection (FS) methods are combined to choose the optimal subset of relevant and non-redundant features. An intrusion detection system, name

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Journal, Baghdad Science. "Developing an Immune Negative Selection Algorithm for Intrusion Detection in NSL-KDD data Set." Baghdad Science Journal 13, no. 2 (2016): 278–90. http://dx.doi.org/10.21123/bsj.13.2.278-290.

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With the development of communication technologies for mobile devices and electronic communications, and went to the world of e-government, e-commerce and e-banking. It became necessary to control these activities from exposure to intrusion or misuse and to provide protection to them, so it's important to design powerful and efficient systems-do-this-purpose. It this paper it has been used several varieties of algorithm selection passive immune algorithm selection passive with real values, algorithm selection with passive detectors with a radius fixed, algorithm selection with passive detector

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Ma, Manfu, Wei Deng, Hongtong Liu, and Xinmiao Yun. "An Intrusion Detection Model based on Hybrid Classification algorithm." MATEC Web of Conferences 246 (2018): 03027. http://dx.doi.org/10.1051/matecconf/201824603027.

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Due to using the single classification algorithm can not meet the performance requirements of intrusion detection, combined with the numerical value of KNN and the advantage of naive Bayes in the structure of data, an intrusion detection model KNN-NB based on KNN and Naive Bayes hybrid classification algorithm is proposed. The model first preprocesses the NSL-KDD intrusion detection data set. And then by exploiting the advantages of KNN algorithm in data values, the model calculates the distance between the samples according to the feature items and selects the K sample data with the smallest

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Solekha, Novia Amilatus. "Analysis of NSL-KDD Dataset for Classification of Attacks Based on Intrusion Detection System Using Binary Logistics and Multinomial Logistics." Seminar Nasional Official Statistics 2022, no. 1 (2022): 507–20. http://dx.doi.org/10.34123/semnasoffstat.v2022i1.1138.

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At present, the intrusion detection system is the most developed trend in society. The intrusion detection system acts as a defense tool to detect security attacks which has been increasing steadily in recent years. Therefore, global information security is a very serious problem. As the types of attacks that emerge are constantly changing, there is a need to develop adaptive and flexible security features. Selection feature is one of the focuses of research on data mining for datasets that have relatively many attributes. In this study, the author tries to analyze the NSL-KDD data set with th

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Mr., Shobhan Kumar* Mr. Naveen D.C. "IMPROVE THE ACCURACY OF CLASSIFIERS PERFORMANCE USING MACHINE LEARNING & DATA PREPROCESSED METHODS ON NSL-KDD DATA SETS." Global Journal of Engineering Science and Research Management 3, no. 5 (2016): 136–42. https://doi.org/10.5281/zenodo.53749.

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Classification is the method of discovering a set of models that describes data classes for the purpose of being able to utilize the model to forecast the class of objects whose class label is unknown. The derived model is based on the analysis of a set of training data set. Since the class label of each training sample provides this step is referred as supervised learning. The manuscript describes a system that uses Feature Selection [17, 18] as a data pre-processing activities. Feature selection may present us with the means to reduce the number of network parameters made while still maintai

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Hashem, Soukaena, and Hafsa Adil. "Denial of Service Intrusion Detection System (IDS) Based on Naïve Bayes Classifier using NSL KDD and KDD Cup 99 Datasets." Journal of Al-Rafidain University College For Sciences ( Print ISSN: 1681-6870 ,Online ISSN: 2790-2293 ), no. 2 (October 9, 2021): 206–31. http://dx.doi.org/10.55562/jrucs.v40i2.200.

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Intrusion Detection Systems (IDS) become necessary to protect data from intruders and reduce the damage of the information system and networks especially in cloud environment which is next generation Internet based computing system that supplies customizable services to the end user to work or access to the various cloud applications. This paper concentrates the views to be noted that; the attacks in cloud environment have high rates of Denial of service (DoS) attacks compared with the usual network environment. This paper will introduce Naïve Bayes (NB) Classifier supported by discrete the co

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Tribak, Hind, Olga Valenzuela, Fernando Rojas, and Ignacio Rojas. "Statistical Analysis of Different Artificial Intelligent Techniques applied to Intrusion Detection System." International Journal of Systems Applications, Engineering & Development 16 (March 10, 2022): 48–55. http://dx.doi.org/10.46300/91015.2022.16.10.

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Intrusion detection is the act of detecting unwanted traffic on a network or a device. Several types of Intrusion Detection Systems (IDS) technologies exist due to the variance of network configurations. Each type has advantages and disadvantage in detection, configuration, and cost. In general, the traditional IDS relies on the extensive knowledge of security experts, in particular, on their familiarity with the computer system to be protected. To reduce this dependence, various datamining and machine learning techniques have been used in the literature. The experiments and evaluations of the

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Deepa, Hindoliya, and Avinash Sharma Prof. "Performance Evaluation of Intrusion Detection using Linear Regression with K Nearest Neighbor." International Journal of Trend in Scientific Research and Development 4, no. 1 (2019): 255–59. https://doi.org/10.5281/zenodo.3604828.

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Starting late, the colossal proportions of data and its unfaltering augmentation have changed the essentialness of information security and data examination systems for Big Data. Interference acknowledgment structure IDS is a system that screens and analyzes data to perceive any break in the structure or framework. High volume, arrangement and quick of data made in the framework have made the data examination strategy to perceive ambushes by ordinary strategies problematic. Gigantic Data frameworks are used in IDS to oversee Big Data for exact and profitable data examination process. This work

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Shaik, Aseena Babu, Rajeswara Reddy, Nagagopi Raju Vullam, Gondi Konda Reddy, and Subhani Shaik. "An Effective Method for Detecting Cyber Attacks on Computer Networks from the NSL-KDD Data Set." ITM Web of Conferences 74 (2025): 02001. https://doi.org/10.1051/itmconf/20257402001.

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Cybercrime is rapidly increasing and exploits various vulnerabilities in these computing environments. Ethical hackers pay more attention to determining vulnerabilities and recommending mitigation methods. Due to the effectiveness of machine learning in solving cybersecurity problems, machine learning is of great importance to cybersecurity. Machine learning models are used to advance the techniques to detect and solve cybersecurity problems. Machine learning methods help detect more cyber attacks more efficiently than other software-oriented techniques, reducing the burden on security analyst

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Kejia, Shen, Hamid Parvin, Sultan Noman Qasem, Bui Anh Tuan, and Kim-Hung Pho. "A classification model based on svm and fuzzy rough set for network intrusion detection." Journal of Intelligent & Fuzzy Systems 39, no. 5 (2020): 6801–17. http://dx.doi.org/10.3233/jifs-191621.

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Intrusion Detection Systems (IDS) are designed to provide security into computer networks. Different classification models such as Support Vector Machine (SVM) has been successfully applied on the network data. Meanwhile, the extension or improvement of the current models using prototype selection simultaneous with their training phase is crucial due to the serious inefficacies during training (i.e. learning overhead). This paper introduces an improved model for prototype selection. Applying proposed prototype selection along with SVM classification model increases attack discovery rate. In th

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Srinivas Akkepalli. "A Novel Framework of Anomaly-Based Network Intrusion Detection using Hybrid CNN, Bi-LSTM Deep Learning Techniques." Journal of Information Systems Engineering and Management 10, no. 19s (2025): 247–54. https://doi.org/10.52783/jisem.v10i19s.3015.

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A Novel Framework of Anomaly-based Network Intrusion Detection system using hybrid CNN,Bi-LSTM Deep learning techniques with the aim of anomaly detection, In recent years, deep learning (DL) has become increasingly important in the field of cyber security. Deep learning Algorithms efficient to detect vulnerabilities in network traffic.Objective are based on literature survey provides the various anomaly based techniques Such as NIDS,SIDS, researches are presented. [proposed CNN based BLISTM model]stand out, providing a solid basis for understanding the context of the investigation and verified

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Raghuvanshi, Abhishek, Umesh Kumar Singh, Guna Sekha Sajja, et al. "Intrusion Detection Using Machine Learning for Risk Mitigation in IoT-Enabled Smart Irrigation in Smart Farming." Journal of Food Quality 2022 (February 11, 2022): 1–8. http://dx.doi.org/10.1155/2022/3955514.

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The majority of countries rely largely on agriculture for employment. Irrigation accounts for a sizable amount of water use. Crop irrigation is an important step in crop yield prediction. Field harvesting is very reliant on human supervision and experience. It is critical to safeguard the field’s water supply. The shortage of fresh water is a major challenge for the world, and the situation will deteriorate further in the next years. As a result of the aforementioned challenges, smart irrigation and precision farming are the only viable solutions. Only with the emergence of the Internet of Thi

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Ghosh, Partha, Meghna Bardhan, Nilabhra Roy Chowdhury, and Santanu Phadikar. "IDS Using Reinforcement Learning Automata for Preserving Security in Cloud Environment." International Journal of Information System Modeling and Design 8, no. 4 (2017): 21–37. http://dx.doi.org/10.4018/ijismd.2017100102.

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Cloud computing relies on sharing computing resources. With high availability and accessibility of resources, cloud computing is under the threat of major cyber-attacks. To detect attacks and preserve security in cloud environment, having an efficient intrusion detection system (IDS) is required. In this article, an effective and efficient IDS is proposed to maintain high level security of data in cloud. The authors have incorporated Reinforcement Learning Automata with their proposed IDS while detecting and classifying attacks. Using learning automata an effective rule set is generated with t

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Rzayev, B. T., I. S. Lebedev, Zh Т. Beldeubayeva, and I. M. Uvaliyeva. "IDENTIFICATION OF ROOTKITS IN NETWORK TRAFFIC WITH USING THE BAGGING OF CLASSIFIERS." Bulletin D. Serikbayev of EKTU, no. 1 (March 2024): 234–43. http://dx.doi.org/10.51885/1561-4212_2024_1_234.

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The paper proposes an approach to identify anomalies in network traffic based on the use of machine learning classifiers. The solution allows you to determine the resulting state class by averaging the votes of individual classifiers. The approach was evaluated on the NSL-KDD public dataset. A compar- ison of the performance of classifiers and their averaged evaluation using the Weka tool was performed. The NSL-KDD set has been optimized, with an emphasis on "rootkit" type attacks, as one of the most diffi- cult types of attacks to detect. Using the bagging-based approach implemented in the We

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H., Manjunath, and Saravana Kumar. "Network Intrusion Detection System using Convolution Recurrent Neural Networks and NSL-KDD Dataset." Fusion: Practice and Applications 13, no. 1 (2023): 117–25. http://dx.doi.org/10.54216/fpa.130109.

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Increase in network activity of transferring information online allows network breeches where intruders easily avail the most important information or data. The growth of online functioning and many other governmental data over the internet without security has caused data vulnerability; attackers can easily detect the data and misuse them. Network Intrusion Detection System (NIDS) has allowed this whole process of online data transfer to occur safely and secured transactions. Due to the cloud usage in network the huge amount of traffic is created as well as number of attacks are increased day

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KumarShrivas, Akhilesh, and Amit Kumar Dewangan. "An Ensemble Model for Classification of Attacks with Feature Selection based on KDD99 and NSL-KDD Data Set." International Journal of Computer Applications 99, no. 15 (2014): 8–13. http://dx.doi.org/10.5120/17447-5392.

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Dhar, Argha Chandra, Arna Roy, M. A. H. Akhand, Md Abdus Samad Kamal, and Kou Yamada. "Cascaded Machine Learning Approach with Data Augmentation for Intrusion Detection System." International Journal of Computer Network and Information Security 16, no. 4 (2024): 17–30. http://dx.doi.org/10.5815/ijcnis.2024.04.02.

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Cybersecurity has received significant attention globally, with the ever-continuing expansion of internet usage, due to growing trends and adverse impacts of cybercrimes, which include disrupting businesses, corrupting or altering sensitive data, stealing or exposing information, and illegally accessing a computer network. As a popular way, different kinds of firewalls, antivirus systems, and Intrusion Detection Systems (IDS) have been introduced to protect a network from such attacks. Recently, Machine Learning (ML), including Deep Learning (DL) based autonomous systems, have been state-of-th

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Anamika, Sharma, and Arun Jhapate Prof. "An Intrusion Detection System Using Singular Average Dependency Estimator in Data Mining." International Journal of Trend in Scientific Research and Development 2, no. 5 (2018): 1713–19. https://doi.org/10.31142/ijtsrd18166.

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Intrusion Detection System IDS is a vital component of any network in today's world of Internet. IDS are an effective way to detect different kinds of attacks in interconnected network. An effective Intrusion Detection System requires high accuracy and detection rate as well as low false alarm rate. To tackle this growing trend in computer attacks and respond threats, industry professionals and academics are joining forces in order to build Intrusion Detection Systems IDS that combine high accuracy with low complexity and time efficiency. With the tremendous growth of usage of internet and

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Syed, Zuber Hussain, and Avinash Sharma Prof. "A Novel Approach of Intrusion Detection System Through SADE in Data Mining." International Journal of Trend in Scientific Research and Development 2, no. 6 (2018): 1579–85. https://doi.org/10.31142/ijtsrd18916.

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Intrusion Detection System IDS is a vital component of any network in today's world of Internet. IDS are an effective way to detect different kinds of attacks in interconnected network. An effective Intrusion Detection System requires high accuracy and detection rate as well as low false alarm rate. To tackle this growing trend in computer attacks and respond threats, industry professionals and academics are joining forces in order to build Intrusion Detection Systems IDS that combine high accuracy with low complexity and time efficiency. With the tremendous growth of usage of internet and

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Sheikhi, Saeid, and Panos Kostakos. "A Novel Anomaly-Based Intrusion Detection Model Using PSOGWO-Optimized BP Neural Network and GA-Based Feature Selection." Sensors 22, no. 23 (2022): 9318. http://dx.doi.org/10.3390/s22239318.

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Intrusion detection systems (IDS) are crucial for network security because they enable detection of and response to malicious traffic. However, as next-generation communications networks become increasingly diversified and interconnected, intrusion detection systems are confronted with dimensionality difficulties. Prior works have shown that high-dimensional datasets that simulate real-world network data increase the complexity and processing time of IDS system training and testing, while irrelevant features waste resources and reduce the detection rate. In this paper, a new intrusion detectio

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Magdy, Mina Eshak, Ahmed M. Matter, Saleh Hussin, Doaa Hassan, and Shaimaa Ahmed Elsaid. "Anomaly-based intrusion detection system based on feature selection and majority voting." Indonesian Journal of Electrical Engineering and Computer Science 30, no. 3 (2023): 1699. http://dx.doi.org/10.11591/ijeecs.v30.i3.pp1699-1706.

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Recently, cyberattacks have been more complex than in the past, as a new cyber-attack is initiated almost every day. Therefore, researchers should develop efficient intrusion detection systems (IDS) to detect cyber-attacks. In order to improve the detection and prevention of the aforementioned cyber-attacks, several articles developed IDSs exploiting machine learning and deep learning. In this paper, a way to find network intrusions using a combination of feature selection and adoptive voting is investigated. NSL-KDD dataset, a high-dimensional dataset that has been widely used for network int

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Mina, Eshak Magdy, M. Matter Ahmed, Hussin Saleh, Hassan Doaa, and Ahmed Elsaid Shaimaa. "Anomaly-based intrusion detection system based on feature selection and majority voting." Anomaly-based intrusion detection system based on feature selection and majority voting 30, no. 3 (2023): 1699–706. https://doi.org/10.11591/ijeecs.v30.i3.pp1699-1706.

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Recently, cyberattacks have been more complex than in the past, as a new cyber-attack is initiated almost every day. Therefore, researchers should develop efficient intrusion detection systems (IDS) to detect cyber-attacks. In order to improve the detection and prevention of the aforementioned cyberattacks, several articles developed IDSs exploiting machine learning and deep learning. In this paper, a way to find network intrusions using a combination of feature selection and adoptive voting is investigated. NSL-KDD dataset, a high-dimensional dataset that has been widely used for network intr

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Girubagari, N., T. N. Ravi, and S. Panneer Arokiaraj. "Hybrid Intelligent Anomaly Detection System Using Attention based Deep Learning Approach for Cyber Attacks Prevention." Indian Journal Of Science And Technology 17, no. 38 (2024): 3947–59. http://dx.doi.org/10.17485/ijst/v17i38.2224.

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Objectives: Network Intrusion Detection System (NIDS) plays an important role in finding and preventing cyber-attacks, which helps to improve the entire security posture of an organization’s network infrastructure. The development of Deep Learning (DL) techniques possess the ability of IDS to detect attacks without delay and protects from intrusions even in real-time environment. Methods: The present study proposes an improved IDS framework called Enhanced Gated Recurrent Unit Hyper-Model combined Attention Bidirectional Long-Short Term Memory (EGHAB) approach, to effectively address the detec

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N, Girubagari, N. Ravi T, and Panneer Arokiaraj S. "Hybrid Intelligent Anomaly Detection System Using Attention based Deep Learning Approach for Cyber Attacks Prevention." Indian Journal of Science and Technology 17, no. 38 (2024): 3947–59. https://doi.org/10.17485/IJST/v17i38.2224.

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Abstract <strong>Objectives:</strong> Network Intrusion Detection System (NIDS) plays an important role in finding and preventing cyber-attacks, which helps to improve the entire security posture of an organization’s network infrastructure. The development of Deep Learning (DL) techniques possess the ability of IDS to detect attacks without delay and protects from intrusions even in real-time environment. <strong>Methods:</strong> The present study proposes an improved IDS framework called Enhanced Gated Recurrent Unit Hyper-Model combined Attention Bidirectional Long-Shor

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Bhakti, Nandurdikar, and Mahajan Rupesh. "Intelligent and Effective Intrusion Detection System using Machine Learning Algorithm." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 6 (2020): 237–40. https://doi.org/10.35940/ijeat.F1231.089620.

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Intrusion Detection System observes the network traffic and identifies the attack and also inform the admin to corrective action. Powerful Intrusion Detection system is required for detection to various modern attack. There is need of efficient Intrusion Detection system .The focus of IDS research is the application of machine Learning and Deep Learning techniques. Projected work is combination of Deep Learning Technique in which Non Symmetric Deep Auto Encoder and Machine Learning Algorithm, Support Vector Machine Classifier is used to develop the Model. Stack power of the Non symmetric Deep

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Li, Aichuan, and Shujuan Yi. "Intelligent Intrusion Detection Method of Industrial Internet of Things Based on CNN-BiLSTM." Security and Communication Networks 2022 (April 4, 2022): 1–8. http://dx.doi.org/10.1155/2022/5448647.

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Aiming at the problems of fuzzy detection characteristics, high false positive rate and low accuracy of traditional network intrusion detection technology, an improved intelligent intrusion detection method of industrial Internet of Things based on deep learning is proposed. Firstly, the data set is preprocessed and transformed into 122 dimensional intrusion data set after one-hot coding; Secondly, aiming at the problem that convolution network cannot deal with data with long-distance attributes, Bidirectional long short-term memory (BiLSTM) is used to mine the relationship between data featur

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Albulayhi, Khalid, Qasem Abu Al-Haija, Suliman A. Alsuhibany, Ananth A. Jillepalli, Mohammad Ashrafuzzaman, and Frederick T. Sheldon. "IoT Intrusion Detection Using Machine Learning with a Novel High Performing Feature Selection Method." Applied Sciences 12, no. 10 (2022): 5015. http://dx.doi.org/10.3390/app12105015.

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The Internet of Things (IoT) ecosystem has experienced significant growth in data traffic and consequently high dimensionality. Intrusion Detection Systems (IDSs) are essential self-protective tools against various cyber-attacks. However, IoT IDS systems face significant challenges due to functional and physical diversity. These IoT characteristics make exploiting all features and attributes for IDS self-protection difficult and unrealistic. This paper proposes and implements a novel feature selection and extraction approach (i.e., our method) for anomaly-based IDS. The approach begins with us

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Anitha Patil, Dr, and M. Srikanth Yadav. "Performance Analysis of Misuse Attack Data using Data Mining Classifiers." International Journal of Engineering & Technology 7, no. 4.36 (2018): 261–63. http://dx.doi.org/10.14419/ijet.v7i4.36.23782.

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Data mining can be characterized as the extraction of certain, already un-known, and conceivably valuable data from information. Various analysts have been creating security innovation and investigating new techniques to recognize digital assaults with the DARPA 1998 dataset for Intrusion Detection and adjusted renditions of this dataset KDDCup99 and NSL-KDD, yet as of not long ago nobody have inspected the execution of Top information mining calculations chose by specialists in information mining. The execution of these calculations are contrasted and precision, blunder rate and normal cost o

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Singh, Parminder, Sujatha Krishnamoorthy, Anand Nayyar, Ashish Kr Luhach, and Avinash Kaur. "Soft-computing-based false alarm reduction for hierarchical data of intrusion detection system." International Journal of Distributed Sensor Networks 15, no. 10 (2019): 155014771988313. http://dx.doi.org/10.1177/1550147719883132.

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A false alarm rate of online anomaly-based intrusion detection system is a crucial concern. It is challenging to implement in the real-world scenarios when these anomalies occur sporadically. The existing intrusion detection system has been developed to limit or decrease the false alarm rate. However, the state-of-the-art approaches are attack or algorithm specific, which is not generic. In this article, a soft-computing-based approach has been designed to reduce the false-positive rate for hierarchical data of anomaly-based intrusion detection system. The recurrent neural network model is app

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Ahmim, Ahmed, and Nacira Ghoualmi Zine. "A new hierarchical intrusion detection system based on a binary tree of classifiers." Information & Computer Security 23, no. 1 (2015): 31–57. http://dx.doi.org/10.1108/ics-04-2013-0031.

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Purpose – The purpose of this paper is to build a new hierarchical intrusion detection system (IDS) based on a binary tree of different types of classifiers. The proposed IDS model must possess the following characteristics: combine a high detection rate and a low false alarm rate, and classify any connection in a specific category of network connection. Design/methodology/approach – To build the binary tree, the authors cluster the different categories of network connections hierarchically based on the proportion of false-positives and false-negatives generated between each of the two categor

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SALEEH, HADEEL M. SALEH, Hend Marouane, and Ahmed Fakhfakh. "A Novel Deep Learning Approach for Detecting Types of Attacks in the NSL-KDD Dataset." Babylonian Journal of Networking 2024 (September 1, 2024): 171–81. http://dx.doi.org/10.58496/bjn/2024/017.

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The growing prevalence of Internet intrusions poses significant threats to the security, privacy, and reliability of systems and networks. Denial-of-service (DoS) attacks are a cause for concern as they aim to disrupt access to network resources, posing major risks. Traditional intrusion detection systems (IDS) face challenges in detecting attacks because of the evolving nature of these attacks. Therefore, advanced techniques are necessary to accomplish accurate and timely detection. This study introduces a novel approach that combines Deep learning techniques, specifically the CNN algorithm,

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Shen, Limin, Zhongkui Sun, Lei Chen, and Jiayin Feng. "Application of High-Dimensional Outlier Mining Based on the Maximum Frequent Pattern Factor in Intrusion Detection." Mathematical Problems in Engineering 2021 (June 21, 2021): 1–10. http://dx.doi.org/10.1155/2021/9234084.

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As the Internet applications are growing rapidly, the intrusion detection system is widely used to detect network intrusion effectively. Aiming at the high-dimensional characteristics of data in the intrusion detection system, but the traditional frequent-pattern-based outlier mining algorithm has the problems of difficulty in obtaining complete frequent patterns and high time complexity, the outlier set is further analysed to get the attack pattern of intrusion detection. The NSL-KDD dataset and UNSW-NB15 dataset are used for evaluating the proposed approach by conducting some experiments. Th

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Asiya Begum and B.Srinivas S.P Kumar. "NETWORK TRAFFIC DETECTION THROUGH MACHINE LEARNING." international journal of engineering technology and management sciences 6, no. 6 (2022): 404–6. http://dx.doi.org/10.46647/ijetms.2022.v06i06.072.

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In imbalanced network traffic, malicious cyber-attacks can often hide in large amounts of normal data. It exhibits a high degree of stealth and obfuscation in cyberspace, making it difficult for Network Intrusion Detection System (NIDS) to ensure the accuracy and timeliness of detection. This paper researches machine learning and deep learning for intrusion detection in imbalanced network traffic. It proposes a novel Difficult Set Sampling Technique (DSSTE) algorithm to tackle the class imbalance problem. To verify the proposed method, we conduct experiments on the classic intrusion dataset NS

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Gholi Beik, Adeleh Jafar, Mohammad Ebrahim Shiri Ahmad Abadib, and Afshin Rezakhani. "Anomalies detection in the application layer with new combined methods in IoT networks." Journal of Intelligent & Fuzzy Systems 40, no. 6 (2021): 10909–18. http://dx.doi.org/10.3233/jifs-201938.

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Today, due to increasing dependence on the internet, the tendency to make smart and the Internet of things (IoT), has risen. Also, detecting attacks, and malicious activity as well as anomalies on the internet networks, and preventing them from different layers is a necessity. In this method, a new hybrid model of IWC clustering and Random Forest methods are introduced to identify normal and abnormal conditions. It also shows unauthorized access and attacks to different layers of the Internet of Things, especially the application layer. The IWC is a clustering and improved model of the k-means

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AvinashR.Sonule, Kalla Mukesh, Jain Amit, and Chouhan D.S. "Unsw-Nb15 Dataset and Machine Learning Based Intrusion Detection Systems." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 3 (2020): 2638–48. https://doi.org/10.35940/ijeat.C5809.029320.

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The network attacks become the most important security problems in the today’s world. There is a high increase in use of computers, mobiles, sensors ,IoTs in networks, Big Data, Web Application/Server, Clouds and other computing resources. With the high increase in network traffic, hackers and malicious users are planning new ways of network intrusions. Many techniques have been developed to detect these intrusions which are based on data mining and machine learning methods. Machine learning algorithms intend to detect anomalies using supervised and unsupervised approaches .Both the dete

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Balyan, Amit Kumar, Sachin Ahuja, Umesh Kumar Lilhore, et al. "A Hybrid Intrusion Detection Model Using EGA-PSO and Improved Random Forest Method." Sensors 22, no. 16 (2022): 5986. http://dx.doi.org/10.3390/s22165986.

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Due to the rapid growth in IT technology, digital data have increased availability, creating novel security threats that need immediate attention. An intrusion detection system (IDS) is the most promising solution for preventing malicious intrusions and tracing suspicious network behavioral patterns. Machine learning (ML) methods are widely used in IDS. Due to a limited training dataset, an ML-based IDS generates a higher false detection ratio and encounters data imbalance issues. To deal with the data-imbalance issue, this research develops an efficient hybrid network-based IDS model (HNIDS),

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Pitafi, Shahneela, Toni Anwar, and Zubair Sharif. "An Improved Approach Based on Density-Based Spatial Clustering of Applications with a Noise Algorithm for Intrusion Detection." Journal of Hunan University Natural Sciences 49, no. 12 (2022): 67–77. http://dx.doi.org/10.55463/issn.1674-2974.49.12.7.

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Network Intrusion detection systems (NIDS) are extremely important for make the network secure from unauthorized access. Numerous studies have already been conducted to detect the unauthorized access to achieve security. As the NIDS are still lacking in terms of accuracy, true positive rate (TPR) and the false positive rate (FPR) of the invasive events. The main cause of high FPR in intrusion detection systems is run with a default set of signatures. Issues in the detection rate are caused by feature similarities between man-made events and environmental events. Considering this fact, in this

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Imrana, Yakubu, Yanping Xiang, Liaqat Ali та ін. "χ2-BidLSTM: A Feature Driven Intrusion Detection System Based on χ2 Statistical Model and Bidirectional LSTM". Sensors 22, № 5 (2022): 2018. http://dx.doi.org/10.3390/s22052018.

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In a network architecture, an intrusion detection system (IDS) is one of the most commonly used approaches to secure the integrity and availability of critical assets in protected systems. Many existing network intrusion detection systems (NIDS) utilize stand-alone classifier models to classify network traffic as an attack or as normal. Due to the vast data volume, these stand-alone models struggle to reach higher intrusion detection rates with low false alarm rates( FAR). Additionally, irrelevant features in datasets can also increase the running time required to develop a model. However, dat

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Ennaji, Sabrine, Nabil El Akkad, and Khalid Haddouch. "i-2NIDS Novel Intelligent Intrusion Detection Approach for a Strong Network Security." International Journal of Information Security and Privacy 17, no. 1 (2023): 1–17. http://dx.doi.org/10.4018/ijisp.317113.

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The potential of machine learning mechanisms played a key role in improving the intrusion detection task. However, other factors such as quality of data, overfitting, imbalanced problems, etc. may greatly affect the performance of an intelligent intrusion detection system (IDS). To tackle these issues, this paper proposes a novel machine learning-based IDS called i-2NIDS. The novelty of this approach lies in the application of the nested cross-validation method, which necessitates using two loops: the outer loop is for hyper-parameter selection that costs least error during the run of a small

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Eesa, Adel S. "Rule Mining Using Particle Swarm Optimization for Intrusion Detection Systems." Academic Journal of Nawroz University 9, no. 2 (2020): 222. http://dx.doi.org/10.25007/ajnu.v9n2a816.

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Traditional data mining techniques are commonly used to build the Intrusion Detection Systems IDSs. They are designed on the basis of some probabilistic methods that still do not take into account some of the important properties of each feature in the dataset. We believe that each feature in the dataset has its own crucial role for its characteristics, which should be taken into consideration. In this work, instead of using the traditional technique or applying feature selection methods we proposed max and min boundary mining approach to solve Anomaly Intrusion Detection System AIDS problem.

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Shi, Yanyan, Gaoyuan Liu, Boxiong Yang, Yong Chen, and Zhenbao Liang. "A feature selection algorithm for PNN optimized by binary PSO and applied to smart city intrusion detection system." IC-ITECHS 5, no. 1 (2024): 33–42. https://doi.org/10.32664/ic-itechs.v5i1.1510.

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In the smart city construction and development process, network security is a vital link that must be addressed. As a critical technology to ensure the security of smart cities, intrusion detection systems play multiple roles, such as real-time monitoring, threat identification, and event response. By deploying and optimizing efficient intrusion detection mechanisms, smart cities can effectively resist various network attacks and ensure the safety and stability of urban operations. Therefore, this paper proposes a PNN feature selection model based on binary particle swarm algorithm optimizatio

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Ludwig, Simone A. "Applying a Neural Network Ensemble to Intrusion Detection." Journal of Artificial Intelligence and Soft Computing Research 9, no. 3 (2019): 177–88. http://dx.doi.org/10.2478/jaiscr-2019-0002.

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Abstract An intrusion detection system (IDS) is an important feature to employ in order to protect a system against network attacks. An IDS monitors the activity within a network of connected computers as to analyze the activity of intrusive patterns. In the event of an ‘attack’, the system has to respond appropriately. Different machine learning techniques have been applied in the past. These techniques fall either into the clustering or the classification category. In this paper, the classification method is used whereby a neural network ensemble method is employed to classify the different

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Hussein, Abdul Ameer Abbas Al-Khamees, Al-A'araji Nabeel, and Salih Al-Shamery Eman. "Enhancing the stability of the deep neural network using a non-constant learning rate for data stream." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 2 (2023): 2123–30. https://doi.org/10.11591/ijece.v13i2.pp2123-2130.

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The data stream is considered the backbone of many real-world applications. These applications are most effective when using modern techniques of machine learning like deep neural networks (DNNs). DNNs are very sensitive to set parameters, the most prominent one is the learning rate. Choosing an appropriate learning rate value is critical because it is able to control the overall network performance. This paper presents a new developing DNN model using a multi-layer perceptron (MLP) structure that includes network training based on the optimal learning rate. Thereupon, this model consists of t

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Nayebi, F., and M. Noorimehr M.Noorimehr. "An instruction detection system with Support Vector Machine, Cuckoo-Genetic algorithm and principal component analysis." International Journal Artificial Intelligent and Informatics 3, no. 1 (2022): 1–12. http://dx.doi.org/10.33292/ijarlit.v3i1.43.

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Nowadays, with the abundant growth of internet users, attacks on computer systems have dramatically increased. This condition might increase the risk of security for Internet users or networks systems. Thus, Intrusion Detection Systems (IDS) is used for detection, identification and diagnosis of security issues on computer networks .As a data mining technique, Support Vector Machine (SVM) is considered in the design and implementation of IDS.SVM’s performance is influenced by its parameters and its input feature space respectively. So, in order to reach and achieve a reasonable efficiency of S

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Abdulrahman, Yadgar Sirwan. "Network Intrusion Detection using a Combination of Fuzzy Clustering and Ant Colony Algorithm." UHD Journal of Science and Technology 5, no. 2 (2021): 11–19. http://dx.doi.org/10.21928/uhdjst.v5n2y2021.pp11-19.

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As information technology grows, network security is a significant issue and challenge. The intrusion detection system (IDS) is known as the main component of a secure network. An IDS can be considered a set of tools to help identify and report abnormal activities in the network. In this study, we use data mining of a new framework using fuzzy tools and combine it with the ant colony optimization algorithm (ACOR) to overcome the shortcomings of the k-means clustering method and improve detection accuracy in IDSs. Introduced IDS. The ACOR algorithm is recognized as a fast and accurate meta-meth

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HOSSAIN, Yakub, Zannatul FERDOUS, Tanzillah WAHID, Md Torikur RAHMAN, Uttam Kumar DEY, and Mohammad Amanul ISLAM. "Enhancing intrusion detection systems: Innovative deep learning approaches using CNN, RNN, DBN and autoencoders for robust network security." Applied Computer Science 21, no. 1 (2025): 111–25. https://doi.org/10.35784/acs_6667.

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The increasing sophistication of cyber threats poses significant challenges to network security. This makes effective intrusion detection system (IDS) more important than ever before. Conventional IDS methods, which often rely on signatures or rules it will struggle to keep up with its complex attacks and evolution. This thesis evaluates and analyze the performance of DL algorithms. They include convolutional neural networks (CNN), recurrent neural networks (RNN), deep belief networks (DBN), and Auto-encoder. Using the models, these models are trained and tested only on the NSL-set. KDD data,

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Yang, Yanqing, Kangfeng Zheng, Chunhua Wu, Xinxin Niu, and Yixian Yang. "Building an Effective Intrusion Detection System Using the Modified Density Peak Clustering Algorithm and Deep Belief Networks." Applied Sciences 9, no. 2 (2019): 238. http://dx.doi.org/10.3390/app9020238.

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Machine learning plays an important role in building intrusion detection systems. However, with the increase of data capacity and data dimension, the ability of shallow machine learning is becoming more limited. In this paper, we propose a fuzzy aggregation approach using the modified density peak clustering algorithm (MDPCA) and deep belief networks (DBNs). To reduce the size of the training set and the imbalance of the samples, MDPCA is used to divide the training set into several subsets with similar sets of attributes. Each subset is used to train its own sub-DBNs classifier. These sub-DBN

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Abbas Al-Khamees, Hussein Abdul Ameer, Nabeel Al-A'araji, and Eman Salih Al-Shamery. "Enhancing the stability of the deep neural network using a non-constant learning rate for data stream." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 2 (2023): 2123. http://dx.doi.org/10.11591/ijece.v13i2.pp2123-2130.

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The data stream is considered the backbone of many real-world applications. These applications are most effective when using modern techniques of machine learning like deep neural networks (DNNs). DNNs are very sensitive to set parameters, the most prominent one is the learning rate. Choosing an appropriate learning rate value is critical because it is able to control the overall network performance. This paper presents a new developing DNN model using a multi-layer perceptron (MLP) structure that includes network training based on the optimal learning rate. Thereupon, this model consists of t

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Liu, Qun, Zhiyong Tong, Shuiqing Wang, and Ziheng Yang. "Research on intrusion detection method based on feature selection and integrated learning." Journal of Physics: Conference Series 2221, no. 1 (2022): 012054. http://dx.doi.org/10.1088/1742-6596/2221/1/012054.

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Abstract With the introduction of computer technology, network attacks have become more frequent. Some illegal elements may intrude into computers through network attacks to tamper with messages, spread viruses and other destructive behaviors, causing great damage to personal sensitive information, industrial control networks, transaction systems, etc. . For this, this design proposes an improved intrusion detection method based on feature selection and integrated model. The NSL-KDD training data set is used to evaluate the proposed model. First, balance the data categories through the SMOTE-E

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Sumathi, Sumathi, Sumathi Pawar, and Sunil Kumar B. L. "Hybrid chaotic bat artificial bee colony algorithm assisted hybrid machine learning based intrusion detection system." Fusion: Practice and Applications 19, no. 2 (2025): 45–63. https://doi.org/10.54216/fpa.190204.

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Network intrusions are becoming more common, resulting in significant privacy violations, financial losses, and the illegal transfer of sensitive information. Numerous intrusion strategies pose a threat to data, computer resources, and networks. While hackers may focus on obtaining trade secrets, private information, or confidential data that can then be disclosed for illegal purposes, each type of intrusion aims to achieve a distinct objective. False attack detection by security systems and changing threat environments create challenges such as delayed identification of true attacks and long-

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