| Title | Advanced Intrusion Detection Using Deep Learning-LSTM Network On Cloud Environment |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Jisna, P, Jarin, T, Praveen, P N |
| Conference Name | 2021 Fourth International Conference on Microelectronics, Signals Systems (ICMSS) |
| Keywords | cloud computing, Cloud Intrusion Detection System (CIDS), composability, Deep Learning, Deep learning-LSTM, IDS, IDS model, Intrusion detection, machine learning algorithms, Measurement, privacy, pubcrawl, Resiliency, SCAE+SVM IDS, Support vector machines |
| Abstract | Cloud Computing is a favored choice of any IT organization in the current context since that provides flexibility and pay-per-use service to the users. Moreover, due to its open and inclusive architecture which is accessible to attackers. Security and privacy are a big roadblock to its success. For any IT organization, intrusion detection systems are essential to the detection and endurance of effective detection system against attacker aggressive attacks. To recognize minor occurrences and become significant breaches, a fully managed intrusion detection system is required. The most prevalent approach for intrusion detection on the cloud is the Intrusion Detection System (IDS). This research introduces a cloud-based deep learning-LSTM IDS model and evaluates it to a hybrid Stacked Contractive Auto Encoder (SCAE) + Support Vector Machine (SVM) IDS model. Deep learning algorithms like basic machine learning can be built to conduct attack detection and classification simultaneously. Also examine the detection methodologies used by certain existing intrusion detection systems. On two well-known Intrusion Detection datasets (KDD Cup 99 and NSL-KDD), our strategy outperforms current methods in terms of accurate detection. |
| DOI | 10.1109/ICMSS53060.2021.9673607 |
| Citation Key | jisna_advanced_2021 |
Advanced Intrusion Detection Using Deep Learning-LSTM Network On Cloud Environment