{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T16:10:42Z","timestamp":1784823042612,"version":"3.55.0"},"reference-count":28,"publisher":"SAGE Publications","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2022,4,28]]},"abstract":"<jats:p>Anomaly-based detection is coupled with recognizing the uncommon, to catch the unusual activity, and to find the strange action behind that activity. Anomaly-based detection has a wide scope of critical applications, from bank application security to regular sciences to medical systems to marketing apps. Anomaly-based detection adopted by various Machine Learning techniques is really a type of system that consists of artificial intelligence. With the ever-expanding volume and new sorts of information, for example, sensor information from an incontestably enormous amount of IoT devices and from network flow data from cloud computing, it is implicitly understood without surprise that there is a developing enthusiasm for having the option to deal with more conclusions automatically by means of AI and ML applications. But with respect to anomaly detection, many applications of the scheme are simply the passion for detection. In this paper, Machine Learning (ML) techniques, namely the SVM, Isolation forest classifiers experimented and with reference to Deep Learning (DL) techniques, the proposed DA-LSTM (Deep Auto-Encoder LSTM) model are adopted for preprocessing of log data and anomaly-based detection to get better performance measures of detection. An enhanced LSTM (long-short-term memory) model, optimizing for the suitable parameter using a genetic algorithm (GA), is utilized to recognize better the anomaly from the log data that is filtered, adopting a Deep Auto-Encoder (DA). The Deep Neural network models are utilized to change over unstructured log information to training ready features, which are reasonable for log classification in detecting anomalies. These models are assessed, utilizing two benchmark datasets, the Openstack logs, and CIDDS-001 intrusion detection OpenStack server dataset. The outcomes acquired show that the DA-LSTM model performs better than other notable ML techniques. We further investigated the performance metrics of the ML and DL models through the well-known indicator measurements, specifically, the F-measure, Accuracy, Recall, and Precision. The exploratory conclusion shows that the Isolation Forest, and Support vector machine classifiers perform roughly 81%and 79%accuracy with respect to the performance metrics measurement on the CIDDS-001 OpenStack server dataset while the proposed DA-LSTM classifier performs around 99.1%of improved accuracy than the familiar ML algorithms. Further, the DA-LSTM outcomes on the OpenStack log data-sets show better anomaly detection compared with other notable machine learning models.<\/jats:p>","DOI":"10.3233\/jifs-201707","type":"journal-article","created":{"date-parts":[[2021,12,7]],"date-time":"2021-12-07T14:09:14Z","timestamp":1638886154000},"page":"6257-6271","source":"Crossref","is-referenced-by-count":4,"title":["Hybrid approach with Deep Auto-Encoder and optimized LSTM based Deep Learning approach to detect anomaly in cloud logs"],"prefix":"10.1177","volume":"42","author":[{"given":"Savaridassan","family":"Pankajashan","sequence":"first","affiliation":[{"name":"Department of Information Technology, SRM Institute of Science and Technology, Kattankualathur, Chennai, Tami Nadu, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"G.","family":"Maragatham","sequence":"additional","affiliation":[{"name":"Department of Information Technology, SRM Institute of Science and Technology, Kattankualathur, Chennai, Tami Nadu, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"T.","family":"Kirthiga Devi","sequence":"additional","affiliation":[{"name":"Department of Information Technology, SRM Institute of Science and Technology, Kattankualathur, Chennai, Tami Nadu, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","reference":[{"issue":"2","key":"10.3233\/JIFS-201707_ref1","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.icte.2018.04.003","article-title":"Intelligent intrusion detection systems using artificial neural networks","volume":"4","author":"Shenfield","year":"2018","journal-title":"ICT Express"},{"key":"10.3233\/JIFS-201707_ref2","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1007\/s10586-019-02998-y","article-title":"Artificial neural networks based techniques for anomaly detection in Apache Spark","volume":"3","author":"Alnafessah","year":"2020","journal-title":"Cluster Comput"},{"key":"10.3233\/JIFS-201707_ref3","doi-asserted-by":"publisher","DOI":"10.1109\/ICCISci.2019.8716389"},{"key":"10.3233\/JIFS-201707_ref6","doi-asserted-by":"publisher","first-page":"21954","DOI":"10.1109\/ACCESS.2017.2762418","article-title":"A Deep Learning Approach for Intrusion Detection Using Recurrent Neural Networks","volume":"5","author":"Yin","year":"2017","journal-title":"in IEEE Access"},{"key":"10.3233\/JIFS-201707_ref7","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1016\/j.patcog.2016.03.028","article-title":"High-dimensional and large-scale anomaly detection using a linear one-class SVM with Deep Learning","volume":"58","author":"Erfani","year":"2016","journal-title":"Pattern Recognition"},{"key":"10.3233\/JIFS-201707_ref8","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2008.17"},{"key":"10.3233\/JIFS-201707_ref9","doi-asserted-by":"publisher","first-page":"052023","DOI":"10.1088\/1742-6596\/1237\/5\/052023","article-title":"Research and Improvement of Isolation Forest in Detection of Local Anomaly Points","volume":"1237","author":"Gao","year":"2019","journal-title":"Journal of Physics: Conference Series"},{"key":"10.3233\/JIFS-201707_ref10","doi-asserted-by":"publisher","first-page":"64366","DOI":"10.1109\/ACCESS.2019.2917299","article-title":"Wireless Network Intrusion Detection Based on Improved Convolutional Neural Network","volume":"7","author":"Yang","year":"2019","journal-title":"in IEEE Access"},{"key":"10.3233\/JIFS-201707_ref11","doi-asserted-by":"publisher","DOI":"10.1109\/PlatCon.2016.7456805"},{"key":"10.3233\/JIFS-201707_ref12","doi-asserted-by":"crossref","unstructured":"Zhu J. , He S. , Liu J. , He P. , Xie Q. , Zheng Z. and Lyu M.R. , Tools and Benchmarks for Automated Log Parsing, International Conference on Software Engineering (ICSE) (2019).","DOI":"10.1109\/ICSE-SEIP.2019.00021"},{"key":"10.3233\/JIFS-201707_ref15","unstructured":"Lee B. , Amaresh S. , Green C. and Engels D. , Comparative Study of Deep Learning Models for Network Intrusion Detection, SMU DataScience Review 1(1) (2018), Article 8."},{"key":"10.3233\/JIFS-201707_ref16","doi-asserted-by":"publisher","first-page":"4396","DOI":"10.3390\/app9204396","article-title":"Machine Learning and Deep Learning Methods for Intrusion Detection Systems: A Survey","volume":"9","author":"Liu","year":"2019","journal-title":"Applied Sciences"},{"key":"10.3233\/JIFS-201707_ref17","doi-asserted-by":"publisher","first-page":"177","DOI":"10.2478\/jaiscr-2019-0002","article-title":"Applying a Neural Network Ensemble to Intrusion Detection","volume":"9","author":"Ludwig","year":"2019","journal-title":"Journal of Artificial Intelligence and Soft Computing Research"},{"key":"10.3233\/JIFS-201707_ref18","doi-asserted-by":"publisher","DOI":"10.1109\/KBEI.2019.8735024"},{"key":"10.3233\/JIFS-201707_ref19","doi-asserted-by":"publisher","first-page":"13624","DOI":"10.1109\/ACCESS.2018.2810198","article-title":"An Improved Intrusion Detection Algorithm Based on GA and SVM","volume":"6","author":"Tao","year":"2018","journal-title":"in IEEE Access"},{"key":"10.3233\/JIFS-201707_ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CCWC.2018.8301663"},{"key":"10.3233\/JIFS-201707_ref21","doi-asserted-by":"publisher","first-page":"8631","DOI":"10.29322\/IJSRP.9.02.2019.p8631","volume":"9","author":"Pokhrel","year":"2019","journal-title":"Intrusion Detection System using User Profile Generated from System Logs"},{"key":"10.3233\/JIFS-201707_ref22","doi-asserted-by":"publisher","DOI":"10.1109\/ICCECE.2017.8526232"},{"key":"10.3233\/JIFS-201707_ref23","unstructured":"Ring M. , Wunderlich S. , Grudl D. , Landes D. and Hotho A. , Flow-based benchmark data sets for intrusion detection. In Proceedings of the 16th European Conference on Cyber Warfare and Security (ECCWS), in press. ACPI. (2017)."},{"key":"10.3233\/JIFS-201707_ref24","doi-asserted-by":"publisher","DOI":"10.1109\/SSCI.2018.8628676"},{"key":"10.3233\/JIFS-201707_ref25","doi-asserted-by":"publisher","first-page":"41525","DOI":"10.1109\/ACCESS.2019.2895334","article-title":"Deep Learning Approach for Intelligent Intrusion Detection System","volume":"7","author":"Vinayakumar","year":"2019","journal-title":"in IEEE Access"},{"key":"10.3233\/JIFS-201707_ref26","doi-asserted-by":"publisher","DOI":"10.1109\/ISPA48434.2019.8966795"},{"key":"10.3233\/JIFS-201707_ref27","doi-asserted-by":"crossref","unstructured":"Savaridassan P. and Maragatham Dr. G. , Forensics in Private Cloud leveraging the techniques in Machine Learning, International Journal of Advanced Trends in Computer Science and EngineeringISSN: 2278\u20133091, 9(4) (2020).","DOI":"10.30534\/ijatcse\/2020\/63942020"},{"key":"10.3233\/JIFS-201707_ref28","doi-asserted-by":"publisher","first-page":"1185","DOI":"10.1007\/s00521-010-0487-0","article-title":"Intrusion detection using reduced-size RNN based on feature grouping","volume":"21","author":"Sheikhan","year":"2012","journal-title":"Neural Comput & Applic"},{"key":"10.3233\/JIFS-201707_ref29","doi-asserted-by":"publisher","DOI":"10.1109\/DASC\/PiCom\/DataCom\/CyberSciTec.2018.00037"},{"key":"10.3233\/JIFS-201707_ref30","doi-asserted-by":"publisher","first-page":"48231","DOI":"10.1109\/ACCESS.2018.2863036","article-title":"Enhanced Network Anomaly Detection Based on Deep Neural Networks","volume":"6","author":"Naseer","year":"2018","journal-title":"in IEEE Access"},{"key":"10.3233\/JIFS-201707_ref31","doi-asserted-by":"publisher","first-page":"35365","DOI":"10.1109\/ACCESS.2018.2836950","article-title":"Machine Learning and Deep Learning Methods for Cybersecurity","volume":"6","author":"Xin","year":"2018","journal-title":"in IEEE Access"},{"key":"10.3233\/JIFS-201707_ref32","doi-asserted-by":"publisher","DOI":"10.1109\/ISCAS.2019.8702583"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/JIFS-201707","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:45:21Z","timestamp":1777455921000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/JIFS-201707"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,28]]},"references-count":28,"journal-issue":{"issue":"6"},"URL":"https:\/\/doi.org\/10.3233\/jifs-201707","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,28]]}}}