PMID- 35808184
OWN - NLM
STAT- MEDLINE
DCOM- 20220712
LR  - 20220716
IS  - 1424-8220 (Electronic)
IS  - 1424-8220 (Linking)
VI  - 22
IP  - 13
DP  - 2022 Jun 21
TI  - Artificial Intelligence Algorithm-Based Economic Denial of Sustainability Attack 
      Detection Systems: Cloud Computing Environments.
LID - 10.3390/s22134685 [doi]
LID - 4685
AB  - Cloud computing is currently the most cost-effective means of providing 
      commercial and consumer IT services online. However, it is prone to new flaws. An 
      economic denial of sustainability attack (EDoS) specifically leverages the 
      pay-per-use paradigm in building up resource demands over time, culminating in 
      unanticipated usage charges to the cloud customer. We present an effective 
      approach to mitigating EDoS attacks in cloud computing. To mitigate such 
      distributed attacks, methods for detecting them on different cloud computing 
      smart grids have been suggested. These include hard-threshold, machine, and deep 
      learning, support vector machine (SVM), K-nearest neighbors (KNN), random forest 
      (RF) tree algorithms, namely convolutional neural network (CNN), and long 
      short-term memory (LSTM). These algorithms have greater accuracies and lower 
      false alarm rates and are essential for improving the cloud computing service 
      provider security system. The dataset of nine injection attacks for testing 
      machine and deep learning algorithms was obtained from the Cyber Range Lab at the 
      University of New South Wales (UNSW), Canberra. The experiments were conducted in 
      two categories: binary classification, which included normal and attack datasets, 
      and multi-classification, which included nine classes of attack data. The results 
      of the proposed algorithms showed that the RF approach achieved accuracy of 98% 
      with binary classification, whereas the SVM model achieved accuracy of 97.54% 
      with multi-classification. Moreover, statistical analyses, such as mean square 
      error (MSE), Pearson correlation coefficient (R), and the root mean square error 
      (RMSE), were applied in evaluating the prediction errors between the input data 
      and the prediction values from different machine and deep learning algorithms. 
      The RF tree algorithm achieved a very low prediction level (MSE = 0.01465) and a 
      correlation R(2) (R squared) level of 92.02% with the binary classification 
      dataset, whereas the algorithm attained an R(2) level of 89.35% with a 
      multi-classification dataset. The findings of the proposed system were compared 
      with different existing EDoS attack detection systems. The proposed attack 
      mitigation algorithms, which were developed based on artificial intelligence, 
      outperformed the few existing systems. The goal of this research is to enable the 
      detection and effective mitigation of EDoS attacks.
FAU - Aldhyani, Theyazn H H
AU  - Aldhyani THH
AUID- ORCID: 0000-0003-1822-1357
AD  - Applied College in Abqaiq, King Faisal University, P.O. Box 400, Al-Ahsa 31982, 
      Saudi Arabia.
FAU - Alkahtani, Hasan
AU  - Alkahtani H
AD  - College of Computer Science and Information Technology, King Faisal University, 
      P.O. Box 400, Al-Ahsa 31982, Saudi Arabia.
LA  - eng
GR  - NA000106/'This work was supported through the Annual Funding track by the 
      Deanship of Scientific Re-search, Vice Presidency for Graduate Studies and 
      Scientific Research, King Faisal University, Sau-di Arabia [NA000106]/
PT  - Journal Article
DEP - 20220621
PL  - Switzerland
TA  - Sensors (Basel)
JT  - Sensors (Basel, Switzerland)
JID - 101204366
SB  - IM
MH  - Algorithms
MH  - *Artificial Intelligence
MH  - *Cloud Computing
MH  - Neural Networks, Computer
MH  - Support Vector Machine
PMC - PMC9269131
OTO - NOTNLM
OT  - cloud computing
OT  - deep learning approaches
OT  - economic denial of sustainability attack
OT  - intrusion detection system
OT  - machine learning approaches
COIS- The authors declare no conflict of interest.
EDAT- 2022/07/10 06:00
MHDA- 2022/07/14 06:00
PMCR- 2022/06/21
CRDT- 2022/07/09 01:23
PHST- 2022/05/10 00:00 [received]
PHST- 2022/06/16 00:00 [revised]
PHST- 2022/06/17 00:00 [accepted]
PHST- 2022/07/09 01:23 [entrez]
PHST- 2022/07/10 06:00 [pubmed]
PHST- 2022/07/14 06:00 [medline]
PHST- 2022/06/21 00:00 [pmc-release]
AID - s22134685 [pii]
AID - sensors-22-04685 [pii]
AID - 10.3390/s22134685 [doi]
PST - epublish
SO  - Sensors (Basel). 2022 Jun 21;22(13):4685. doi: 10.3390/s22134685.