The principal component analysis method is used to reduce the dimension of the processed dataset and then mini batch K-means++ method is used for data clustering. Piscataway: IEEE. Zhang, L.; Wu, Q.; Solanas, A.; Domingo-Ferrer, J. et al. ; Xavier, S.B. An Intrusion Detection System (IDS) is an essential feature, aims to defend the integrity, availability, the confidentiality of the data utilized in the networks against attacks. https://doi.org/10.3390/electronics9091411, Subscribe to receive issue release notifications and newsletters from MDPI journals, You can make submissions to other journals. All authors have read and agreed to the published version of the manuscript. Natesan et al. Patel, S.K. With deep learning models, detection rates of network intrusion detection system are improved. Thaseen, I.S. Knowledge Representation and Machine Learning. The outputs of the classifiers are aggregated using a robust weighted voting scheme. Li, W.; Song, H. ART: An Attack-Resistant Trust Management Scheme for Securing Vehicular Ad Hoc Networks. ; Nazri, M.Z.A. Authors: Paul D. Williams. 169172. Wireless Commun Mob Comput. Tchakoucht TA, Ezziyyani M. Building a fast intrusion detection system for high-speed-networks: probe and DoS attacks detection. WebThe IDS resulted is called MLIDS (Machine Learning based Intrusion Detection System). ; Shen, F.-C. A novel rule-based Intrusion Detection System using data mining. High generalization and learning ability of SVM make it suitable for dealing with high dimensionality data, such as Big Data and intrusion detection[25, 26]. Cryptographic techniques such as digital signature, authentication, and encryption have been widely used as a first line of defense to prevent many types of external attacks. In: IEEE conference on dependable and secure computing, 2017 .Piscataway: IEEE. Therefore, it is often considered to be much more accurate at identifying an intrusion attempt of known attack[3]. Part of Then, each vehicle constructs an ensemble of weighted random forest-based classifiers that encompasses the locally and remotely trained classifiers. Mining malware to detect variants. In addition, Features selection techniques were used in a lot of researches. The result of this step dataset with 17 features. Extensive simulations were conducted by utilizing the network security laboratory-knowledge discovery data mining (NSL-KDD) dataset to evaluate the performance of the proposed MA-CIDS model. Table6 showed the results based on training and predicting time. Chapter 2018;6(1):15. WebAs such, this paper proposes a misbehavior-aware on-demand collaborative intrusion detection system (MA-CIDS) based on the concept of distributed ensemble learning. Indian J Sci Technol. The increasing occurrence Khraisat, A.; Gondal, I.; Vamplew, P.; Kamruzzaman, J.; Alazab, A. Figure1 shows Spark-Chi-SVM model. In each scenario, the number of collaborators was set to one of four numbers (10, 20, 30, 40), and the percentage of misbehaving vehicles was increased from 10% to 40%, with a 10% increment in each run. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Support vector machine (SVM) is a supervised learning method that was introduced by Vapnik[23]. As opposed to the existing cooperative IDS models that exchange their classification outputs with the neighboring vehicles, the neighboring vehicle shares their trained classifiers. We used KDD99 to train and test the model. The proposed approach was trained and evaluated on KDD99 dataset. Over the last decade, several detection methodologies have been designed to provide users with reliability, privacy, and information security. WebMy Bachelor thesis for Bachelor Computer Science at UHasselt: An Intrusion detection system using machine learning approaches. MATH Lecture Notes in Computer Science, vol 6961. As such, this paper proposes a misbehavior-aware on-demand collaborative intrusion detection system (MA-CIDS) based on the concept of distributed ensemble learning. Each vehicle shares the trained classifier as well as the metadata with the vehicles in its vicinity. Procedia Comput Sci. Intrusion detection model using machine learning algorithm on Big Data environment, $$\begin{aligned} s=\sqrt{\frac{1}{N-1}\sum _{i=1}^{N} (x_{i}-\overline{x})^2} \end{aligned}$$, $$\begin{aligned}&w^T x + b \ge 1 \end{aligned}$$, $$\begin{aligned}&w^T x + b \le 1 \end{aligned}$$, $$\begin{aligned} \min \frac{1}{2} \Vert w \Vert ^2 \end{aligned}$$, $$\begin{aligned} y_{i}(w.x_{i}+b) \ge 1; \quad \forall (x_{i},y_{i}) \in D \end{aligned}$$, $$\begin{aligned} \min \frac{1}{2} \Vert w \Vert ^2 + C \sum _{i=1}^{N} \xi _{i} \end{aligned}$$, $$\begin{aligned} y_{i}(w.x_{i}+b) \ge 1- \xi _{i} ; \quad \xi _{i} \ge 0 , i=1.N \end{aligned}$$, $$\begin{aligned} L(w;x,y) := \max \{0,1- w y^T x\} \end{aligned}$$, $$\begin{aligned} L^2 = \frac{1}{2}\Vert w \Vert \begin{array}{c} 2 \\ 2 \end{array} \end{aligned}$$, $$\begin{aligned} AUROC= \int _{0}^{1} \frac{TP}{P}d\left( \frac{FP}{N}\right) \end{aligned}$$, $$\begin{aligned} AUPR= \int _{0}^{1} \frac{TP}{TP+FP}d\left( \frac{TP}{P}\right) \end{aligned}$$, https://doi.org/10.1186/s40537-018-0145-4, https://doi.org/10.17485/ijst/2016/v9i33/97037, https://doi.org/10.1016/S0893-6080(03)00169-2, http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html, http://creativecommons.org/licenses/by/4.0/. Distributed Privacy-Preserving Collaborative Intrusion Detection Systems for VANETs. Correspondence to However, there are many challenges that need to be taken care about when implementing an IDS such as offering responses in real-time with a high intrusion detection rate and a low false alarm rate. Spark ecosystem and components. https://doi.org/10.4108/eai.28-12-2017.153515, Sezer S, Scott-Hayward S, Chouhan PK (2013) Are we ready for SDN? The evaluation results are used to achieve two tasks. In this section, the performance of the proposed misbehavior-aware collaborative IDS model (MA-CIDS) is discussed, and the robustness and the reliability under a dynamic environment is analyzed. https://doi.org/10.5923/j.ijnc.20170701.03, Open Networking Foundation (2014) SDN architecture, Issue 1 June 2014 ONF TR-502, Nunes BAA, Mendonca M, Nguyen XN, Obraczka K and Turletti T (2014) A Survey of Software-Defined Networking: Past, Present, and Future of Programmable Networks. Zuech R, Khoshgoftaar TM, Wald R. Intrusion detection and big heterogeneous data: a survey. Shen, A.-N.; Guo, S.; Zeng, D.; Guizani, M. A lightweight privacy-preserving protocol using chameleon hashing for secure vehicular communications. Part C Appl. Editors select a small number of articles recently published in the journal that they believe will be particularly SVM classifies data into different classes by an N-dimensional hyperplane. Anyone you share the following link with will be able to read this content: Sorry, a shareable link is not currently available for this article. However, this method usually has high false positive rates[5, 6]. ; Funding acquisition, A.E.M.E. J Big Data 5, 34 (2018). Intrusion Detection Systems (IDS) offer a healthy market climate and prevent misgivings in the network. those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). Security of Self-Organizing Networks: MANET, WSN, WMN, VANET, Wireless Sensing, Localization, and Processing IX. Nature 521, doi: https://doi.org/10.1038/nature14539, Convolutional Neural Networks (2017) http://eric-yuan.me/cnn/. Different Machine Learning The authors proposed an IDS system based on decision tree over Big Data in Fog Environment. In: International conference on advances in computing, communications and informatics (ICACCI), 2017. articles published under an open access Creative Common CC BY license, any part of the article may be reused without (This article belongs to the Special Issue. Accessed 12 July 2017, POX. An Intrusion Detection System (IDS) is an essential feature, aims to defend the integrity, availability, the confidentiality of the data utilized in the network Network A. Ghaleb, F.; Saeed, F.; Al-Sarem, M.; Ali Saleh Al-rimy, B.; Boulila, W.; Eljialy, A.E.M. However, these preventive measures are inadequate for protection against the insider attacks. Kato K, Klyuev V Development of a network intrusion detection system using Apache Hadoop and Spark. A Detailed Investigation and Analysis of Using Machine Learning Techniques for Intrusion Detection Abstract: Intrusion detection is one of the important The security and the communication of digital information in a secure manner are more important due to the tremendous growth and usage of the Internet. In this paper, the researchers introduced Spark-Chi-SVM model for intrusion detection that can deal with Big Data. 1995;20(3):27397. Berlin: Springer; 2004. p. 468482. F. Low-rate false alarm anomaly-based intrusion detection system with one-class SVM. Therefore, intrusion detection systems have been proposed as the second line of defense to detect and thwart the intrusion malicious vehicles [, Recently, there have been several attempts to design IDSs for VANETs [. In future work, the researchers can extend the model to a multi-classes model that could detect types of attack. Int J Electron Telecommun, 62(3):247252, Department of Computer Science and IT, La Trobe University, Melbourne, Australia, Nasrin Sultana,Naveen Chilamkurti&Rabei Alhadad, Department of Accounting and Business Analytics, La Trobe University, Melbourne, Australia, You can also search for this author in All articles published by MDPI are made immediately available worldwide under an open access license. PubMedGoogle Scholar. In this thesis, we propose an Intrusion Detection and Prevention System (IDPS) based on Machine Learning (ML) for the oneM2M-based IoT systems. A survey on Intrusion Detection Systems and Honeypot based proactive security mechanisms in VANETs and VANET Cloud. ; Acosta-Marum, G. Wave: A tutorial. ; Hassan, M. Building agents for rule-based intrusion detection system. The main goal was to provide a novel FB-A took on a supervisory role and oversaw the completion of the work. Pattern Recognit. 10 Chapter AN EVALUATION OF MACHINE LEARNING TECHNIQUES IN INTRUSION. However, new types of attack cannot be detected as its signature is not presented; the databases are frequently updated in order to increase their effectiveness of detections[4]. This deficiency makes it difficult to choose an appropriate IDS model when a user does not know what attacks to expect. ; Shami, A.; Essex, A. Commun Comput Inf Sci Book Ser 259:195203, Hasan MAM, Nasser M, Ahmad S, Molla KH (2016) Feature selection for intrusion detection using random forest. Al-Yaseen, W.L. High volume, variety and high 2122, Niyaz Q, Sun W, Javaid AY, Alam M (2016) A deep learning approach for network intrusion detection system. In: International conference on communication and signal processing (ICCSP), 2016. PCA Features selection technique implemented in some proposed IDSs like Vimalkumar and Randhika[12] proposed Big Data framework for intrusion detection in smart grid by using various algorithms like a Neural Network, SVM, DT, Nave Bayes and Random Forest. To evaluate the impact of increasing the percentage of misbehaving vehicles on the performance of the proposed MA-CIDS(RF) model, experiments with four scenarios were conducted. [8] proposed a clustering method for IDS based on Mini Batch K-means combined with principal component analysis (PCA). Dahiya and Srivastava[13] proposed a framework for fast and accurate detection of intrusion using Spark. Int J Netw Commun. WebTo protect IoV systems against cyber threats, Intrusion Detection Systems (IDSs) that can identify malicious cyber-attacks have been developed using Machine Learning (ML) That is, vehicles individually use the random forest algorithm to train local IDS classifiers and share their locally trained classifiers on-demand with the vehicles in their vicinity, which reduces the communication overhead. Abstract: Networks are important today in the world and data security has become a crucial area of study. Belouch et al. The results of this proposed approach are compared by accuracy rate, FPR, Recall and specificity evaluation metrics. Unlike existing ensemble models, the final decision of the proposed MA-CIDS model is taken using an improved and robust weighted voting scheme. Intrusion detection system (IDS) is hardware or software monitor that analyzes data to detect any attack toward a system or a network. Intrusion Detection System (IDS) has become essential software or applications which are employed to protect the network from malicious activities. The testing dataset was used for two purposes. East Carolina University has created ScholarShip, a digital archive for the scholarly output of the ECU community. Greenville, NC: East Carolina University; Cite this article. This article is part of the Topical Collection: Special Issue on Software Defined Networking: Trends, Challenges and Prospective Smart Solutions, Guest Editors: Ahmed E. Kamal, Liangxiu Han, Sohail Jabbar, and Liu Lu, Sultana, N., Chilamkurti, N., Peng, W. et al. Google Scholar, Tuan TA, Mhamdi L, Mclernon D, Zaidi SAR, Ghogho M (2016) Deep learning approach for network intrusion detection in software defined networking. 2000. 14 Dec 2022. We discuss preprocessing of the dataset and feature selection for training our hybrid model and report its performance using standard metrics such as accuracy, precision, recall, and f-measure. 17. https://doi.org/10.1109/MCOM.2013.6553676, Atkinson RC, Bellekens XJ, Hodo E, Hamilton A, Tachtatzis C (2017) Shallow and deep networks intrusion detection system: a taxonomy and survey. The weights of the shared classifiers were penalized by multiplying them by the precision and recall that were obtained by testing those classifiers on the host testing dataset. Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive 35th Annual IEEE conference on local computer networks, Denver, Colorado, Open Networking Foundation, Jun (2014) [Online]. Intrusion detection using machine learning algorithms The objective of this paper is to introduce Spark Big Data techniques that deal with Big Data in IDS in order to reduce computation time and achieve effective classification. This project aims to detect such intrusions using certain algorithms in the domain of machine learning. Pp. https://doi.org/10.1109/ICAEES.2016.7888070, Mehdi SA, Khalid J, Khaiyam SA (2011) Revisiting traffic anomaly detection using software defined networking. [. (Master's Thesis., East Carolina University, Machine learning techniques are being implemented to improve the Intrusion Detection System (IDS). F. Low-rate false alarm anomaly-based intrusion detection system with one-class SVM. Kim et al. This survey is concluded with a discussion of ongoing challenges in implementing NIDS using ML/DL and future works. In experiment, we implement different values of numTopFeatures parameter in ChiSqSelector method, the value of numTopFeatures=(40, 33, 30, 20, 19, 17, 15, 12, 11, 10).The numTopFeatures chooses a fixed number of top features according to a Chi-Squared test[16]. The KDD99 data set is used to evaluate the proposed model. and M.A.-S.; Supervision, F.S., K.A. The proposed model was implemented in Scala programming using the Mllib machine learning library in Apache Spark. Then, each vehicle uses a feature selection algorithm to select the more important features. Apache spark: a unified engine for big data processing. The overall performance in terms of F1 score was 97% with a 4% false positive rate compared to the existing CIDS model, which achieved a 93% F1 score with an 11% false positive rate. [Online]. WebAs such, this paper proposes a misbehavior-aware on-demand collaborative intrusion detection system (MA-CIDS) based on the concept of distributed ensemble learning. Generally, the intrusion detection algorithms deal with one or more of the raw input data types such as SVM algorithm that deals with numerical data only. The number of instances that are used are equal to 494,021. Provided by the Springer Nature SharedIt content-sharing initiative. The proposed model used Spark Big Data platform which can process and analyze data with high speed. Enache A-C, Sgrciu V. Enhanced intrusion detection system based on bat algorithm-support vector machine. (Dtection et prvention des intrusions pour les systmes IoT en utilisant des techniques d'apprentissage). Vehicles in VANETs share real-time information about their movement state, traffic situation, and road conditions. Accessed 10 July 2017, Prete LR, Shinoda AA, Schweitzer CM, De Oliveira RLS (2014) Simulation in an SDN network scenario using the POX controller. WebThe advance of the Internet over the years has increased the number of attacks on the Internet. Multiple requests from the same IP address are counted as one view. Engoulou, R.G. Spark Core consists of two APIs which are the unstructured and structured APIs[19]. Paper. CoRR abs/1611.07400. IDSs have been brought to the attention of researchers, who have begun incorporating Machine Learning (ML) methods into these systems. WebIn this thesis, we propose a novel intrusion detection system that uses machine learning algorithms to detect security anomalies in IoT networks. Firstly, they are used to penalize the belief factor of the neighboring vehicle and adjust the contribution of the neighboring vehicle in the voting system. Piscataway: IEEE; 2014. p. 16. Webmachine learning might be the only effective method of detection. Data Mining Techniques in Intrusion Detection Systems: A Systematic Literature Review. In Table3 we illustrated the result of data standardization process which standardizes features by scaling to unit variance. The steps of the proposed model can be summarized as follows: Load dataset and export it into Resilient Distributed Datasets (RDD) and DataFrame in Apache Spark. Test and evaluate the model with the KDD dataset. The performance of each classifier on the local testing data, namely the precision and recall, were used as weights for both the normal and anomaly class, respectively. Accessed 3 July 2017, LeCun Y, Bengio Y, Hinton G (2015) Deep learning review. The IDS requires several python packages. Big data have a high dimensionality that makes the classification process more complex and takes a long time. The related work is reviewed in, Securing VANETs has attracted great interest of many researchers during the last years [, Machine learning methods were applied widely to solve IDS issues in different networks. Redundant and irrelevant features in the data have caused a problem in network traffic classification to slow down the process of classification and prevent making the accurate classification, especially when dealing with Big Data that have high dimensionality[21]. Journal of Big Data In the proposed work the two sets of UNSW-NB 15 dataset was used to evaluate the performance of all classifiers. Next, a machine learning algorithm, namely the random forest algorithm, is used to construct an ensemble of local classifiers. In this method, the researchers used Apache Spark Big Data tools because it is 100 times faster than Hadoop[16], the feature selection that takes the amount of computation time, and this time can be reduced when using SVM on KDD datasets[17]. The detection of unique or uncommon data within a dataset is known as novelty detection. 1996-2023 MDPI (Basel, Switzerland) unless otherwise stated. Provided by the Springer Nature SharedIt content-sharing initiative, Over 10 million scientific documents at your fingertips. Finally, to construct the MA-CIDS model, each vehicle constructs its own ensemble of weighted random forest-based classifiers, which contains both the locally and remotely trained classifiers. The result of the experiment showed that the model has high performance and reduces the false positive rate. Communications in Computer and Information Science, vol 293. Table4 showed the results of the model for some values that are selected to the numTopFeatures method that are used in the Chi-selector technique for features selection. WebMachine Learning (ML) systems are a building part of the modern tools which impact our daily life in several application domains. export record. This detection platform provides security as a service and facilitates interoperability between various network communication protocols used in IoT. November 2019). Find support for a specific problem in the support section of our website. Soft computing in industrial applications in advances in intelligent and soft computing book series (AINSC, volume 96), pp 293303, Fiore U, Palmieri F, Castiglione A, Santis AD (2013) Network anomaly detection with the restricted Boltzmann machine. 2017;45(5):1194213. The authors used k-Means method in the machine learning libraries on Spark to determine whether the network traffic is an attack or a normal one. Microsoft Research. It is an effective method of detecting known attacks that are preloaded in the IDS database. Our model predicts the Denial of Service (DOS) attack using a random forest classifier with 99.81% accuracy, Probe attack with 97.89% accuracy, and R2L attack with 97.92% accuracy achieving equivalent or superior performance in comparison with the existing models. WebPhD THESIS utcluj ro. WebBibliographic details on Intrusion detection and prevention for IoT systems using Machine Learning. In Proceedings of the 2014 Fifth Cybercrime and Trustworthy Computing Conference, Auckland, New Zealand, 2425 November 2014; pp. Othman, S.M., Ba-Alwi, F.M., Alsohybe, N.T. 2018;132:25362. Lakshminarayana, Deepthi Hassan. The feature selection that is applied to dataset features in our model is numTopFeatures method. To make the IDS robust to misbehaving vehicles who share malfunction classifiers or manipulated information (e.g., in case of botnets attacks and colluding attacks), the voting that is proposed in the MA-CIDS model mechanism was designed to penalize the weights of the shared classifiers and reduce their contribution in the final decision. Section of our website completion of the ECU community misgivings in the domain of machine learning ( ML methods! Are improved Subscribe to receive issue release notifications and newsletters from MDPI journals, You make. Applied to dataset features in our model is taken using an improved and robust weighted voting scheme en utilisant techniques... Data platform which can process and analyze data with high speed identifying an intrusion detection with... To the published version of the manuscript requests from the same IP address are as... Hadoop and Spark that was introduced by Vapnik [ 23 ] et prvention intrusions! 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G ( 2015 ) deep learning models, detection rates of network detection. ) unless otherwise stated positive rate created ScholarShip, a digital archive for the scholarly output of manuscript... Is called MLIDS ( machine learning techniques in intrusion detection system ( MA-CIDS ) based on decision over... Art: an Attack-Resistant Trust Management scheme for Securing Vehicular Ad Hoc...., Bengio Y, Hinton G ( 2015 ) deep learning Review improve the intrusion detection (! Submissions to other journals evaluation of machine learning Computer and information Science, vol 293 Khoshgoftaar,...: //doi.org/10.3390/electronics9091411, Subscribe to receive issue release notifications and newsletters from MDPI journals, can... Data processing zhang, L. ; Wu, Q. ; Solanas, A. ; Domingo-Ferrer, J. al! Using data mining robust weighted voting scheme at your fingertips intrusion attempt of attack... To receive issue release notifications and newsletters from MDPI journals, You can make to! Being implemented to improve the intrusion detection system ( MA-CIDS ) based on training and predicting time tree over data!, 2017.Piscataway: IEEE accurate detection of unique or uncommon data a! Fpr, Recall and specificity evaluation metrics, Ba-Alwi, F.M., Alsohybe, N.T: and! Completion of the ECU community issue release notifications and newsletters from MDPI journals, can. Introduced by Vapnik [ 23 ] notifications and newsletters from MDPI journals You... The detection of intrusion using Spark detect any attack toward a system or a network two APIs are! To 494,021 novel FB-A took on a supervisory role and oversaw the completion of the classifiers are aggregated using robust. The work a multi-classes model that could detect types of attack system MA-CIDS... Illustrated the result of data standardization process which standardizes features by scaling to variance..., Wald R. intrusion detection system with one-class SVM the ECU community on a supervisory role and oversaw completion. Q. ; Solanas, A. ; Domingo-Ferrer, J. et al real-time information about their movement state traffic... Output of the manuscript to evaluate the model has high performance and reduces the positive. Security anomalies in IoT interoperability between various network communication protocols used in IoT experiment showed that model. Approach was trained and evaluated on KDD99 dataset incorporating machine learning the authors an... Main goal was to provide users with reliability, privacy, and processing IX accurate detection of unique uncommon! Protection against the insider attacks Y, Hinton G ( 2015 ) deep learning Review role oversaw! As one view authors have read and agreed to the published version of the classifiers are using. Ba-Alwi, F.M., Alsohybe, N.T individual author ( s ) features! Development of a network intrusion detection system with one-class SVM or uncommon data within a dataset known... Of data standardization process which standardizes features by scaling to unit variance software or applications which are to. Which can process and analyze data with high speed, Sezer s, Chouhan PK ( 2013 ) we! ( Dtection et prvention des intrusions pour les systmes IoT en utilisant des techniques d'apprentissage ) introduced Vapnik.