A new Deep Learning Based Intrusion Detection System for Cloud Security

Hızal, Selman; Çavuşoğlu, Ünal; Akgün, Devrim

doi:10.1109/hora52670.2021.9461285

Cited by 9 publications

(3 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hizal et al 27 presented a combination model of CNN and RNN to detect incoming malicious attacks in the cloud environment. Cloud security was highly enhanced, and a cost‐effective network could be attained and was very flexible.…”

Section: Related Workmentioning

confidence: 99%

An intelligent multiclass deep classifier‐based intrusion detection system for cloud environment

Bingu

Jothilakshmi

Srinivasu

2023

Concurrency and Computation

View full text Add to dashboard Cite

SummaryThe intrusion detection process is considered an efficient mechanism that plays a prominent role in network security. Traditional approaches for attack detection, like signature‐based detection, are easily prone to a new variant of attacks. Hence, the most prominent solution for cloud security attacks is an intrusion detection system through deep learning (DL) techniques. The behavior‐based intrusion detection system has been constructed by depicting the cloud traffic as input attributes. The deep learning technique is preferred to extract and analyze the traffic attributes rather than machine learning. Hence, an unsupervised technique named deep autoencoder (AE) is used to extract a feature in this research work. The resultant features are then classified by three classifiers named convolutional neural network (CNN), recurrent neural network (RNN), and long‐short term memory (LSTM). The designed approach is trained and assessed using a real‐time cloud attack dataset, namely, CICIDS 2018. Experimental results are analyzed using quality metrics such as receiver operating characteristics (ROC) curve, precision, accuracy, recall, and F1‐measure. Also, the performance of the proposed method is analyzed with existing literature. Higher accuracy rates like 99.55%, 94.33%, and 99.08% are attained with CNN, RNN, and LSTM networks, which are comparatively higher than the existing approaches.

show abstract

Section: Related Workmentioning

confidence: 99%

An intelligent multiclass deep classifier‐based intrusion detection system for cloud environment

Bingu

Jothilakshmi

Srinivasu

2023

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

“…The recommended method is 99.86% accurate for classification into five classes. But this framework's primary drawback is the higher connectivity cost [18].…”

Section: Abirami Et Al (2022) Demonstrated How "Deepmentioning

confidence: 99%

Ensembling of Attention-based Recurrent Units for Detection and Mitigation of Multiple Attacks in Cloud

2023

IJACSA

View full text Add to dashboard Cite

In the recent years, number of threats to network security increases exponentially as the Internet users which poses serious threat in cloud storage application. Detection and defending against the multiple threats are currently a hot topic in industry and considered as one of the challenging research in academia. Many methodologies and algorithms devised to predict the different attacks. Still, most of the methods cannot simultaneously achieve high performance of prediction with a small number of false alarm rates. In this scenario, Deep Learning (DL) algorithms are appropriate and intelligent to categorize the multiple attacks. Still, most of the existing DL techniques are computationally inefficient that may degrade the performance in predicting the both normal and attack information. To overcome this aforementioned problem, this paper proposes the hybrid combination of attention maps with deep recurrent networks to mitigate the multiple attacks with low computational overhead. Initially, the pre-processing step is proposed to the inputs in a specified range. Later on, input data are fed into the Attention Enabled Gated Recurrent Networks (AEGRN) which is used to remove the redundant features and select the optimal features that aids for the better classification. Further to enhance the faster response, deep feed forward layers are proposed to replace the traditional deep neural networks. Numerous metrics for performance, including accuracy, precision, recall, specificity, and F1-score, are examined and analyzed as part of the thorough experimentation utilizing multiple datasets, including NSL-KDD-99, UNSW -2019, and CIDC-001. Comparisons of performance between the method that is suggested and existing models developed with DL are used to demonstrate the proposed algorithm's supremacy. The suggested framework surpasses the other DL models and has the best accuracy in predicting with little computational overhead, according to an investigation.

show abstract

“…In [18], the researchers presented a method to prevent risk in the cloud to quantify level of risk based on finegrained model in co-residency. A large-scale dataset had In [19], the authors implemented the Distributed Deep Learning DDL to enhance the security of the fog-cloud computing with preserving privacy.…”

Section: Work Related To Deep Learning Fieldmentioning

confidence: 99%

A Comprehensive Study of Traditional and Deep-learning Schemes for Privacy and Data Security in the Cloud

sheet¹,

saeed²

2022

AL-Rafidain Journal of Computer Sciences and Mathematics

View full text Add to dashboard Cite

After the availability of Internet infrastructure all over the world, and connectivity is no longer an obstacle over the Internet, cloud computing has emerged as a practical and ideal solution. A huge revolution has taken place in the field of cloud computing, where it is now an industry. However, it faces great difficulties in ensuring data confidentiality and privacy. People hesitate to use it due to the risk of innumerable attacks and security breaches. This article has covered a several directions relayed to cloud computing ideas. This research would focus on traditional and deep-learning based schemes to secure user's data in the cloud. This study concluded some points about the capabilities of the traditional and deep learning-based scheme. The comparison showed that both of them increased the levels of security and privacy of the cloud. The study conclude that the Deep learning-based method had been implanted to secure clouds' data in combination with other technique performed better than others.

show abstract

A new Deep Learning Based Intrusion Detection System for Cloud Security

Cited by 9 publications

References 19 publications

An intelligent multiclass deep classifier‐based intrusion detection system for cloud environment

An intelligent multiclass deep classifier‐based intrusion detection system for cloud environment

Ensembling of Attention-based Recurrent Units for Detection and Mitigation of Multiple Attacks in Cloud

A Comprehensive Study of Traditional and Deep-learning Schemes for Privacy and Data Security in the Cloud

Contact Info

Product

Resources

About