Node Verification to Join the Cloud Environment Using Third Party Verification Server

Aljammal, Ashraf Hamdan; Bani-Salameh, Hani; Alsarhan, Ayoub; Kharabsheh, Mohammad; Obiedat, Mamoon

doi:10.3991/ijim.v11i4.6501

Cited by 3 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Deep learning was adopted in this system for securing sensitive data of IoT devices. Authors in [26] proposed a new technique for ensuring that a new node that requests to join the cloud does not constitute a threat to the cloud environment. The proposed scheme method checks if a node is running malware or software that could be used to launch an attack before allowing it to join the cloud.…”

Section: Related Workmentioning

confidence: 99%

A Novel Scheme for Malicious Nodes Detection in Cloud Markets Based on Fuzzy Logic Technique

Alsarhan

Al-Ghuwairi²,

Alshdaifat³

et al. 2022

Int. J. Interact. Mob. Technol.

Self Cite

View full text Add to dashboard Cite

Cloud security vulnerabilities have recently become more prevalent around the world, posing a threat to cloud service providers' (CSPs) ability to respond to client demands. In cloud market, the requests are announced by the client nodes to their CSP. A malicious node can alter a client's request, resulting in the next cloud market collapse, decreased reliability, and data leaking.To identify malicious nodes in the cloud market, a novel fuzzy multiple criterion decision making scheme is suggested. Authentication test, trust level, traffic size, and node activity levels are all taken into consideration simultaneously as the major criteria for identifying malicious nodes. For each node, the CSP uses fuzzy Integral to generate a composite value based on these criteria. The malicious node is then removed from the cloud market using this composite value. The simulation results demonstrated the potential of the proposed method to prevent nodes in the cloud market from running malware or software that can be used to degrade quality of service by exhausting resources in the cloud market.

show abstract

Section: Related Workmentioning

confidence: 99%

A Novel Scheme for Malicious Nodes Detection in Cloud Markets Based on Fuzzy Logic Technique

Alsarhan

Al-Ghuwairi²,

Alshdaifat³

et al. 2022

Int. J. Interact. Mob. Technol.

Self Cite

View full text Add to dashboard Cite

show abstract

“…A common architecture is to employ a neutral TPV to accomplish the work [20][21][22][23] . To enhance universality, our system model is built on this TPV architecture, under which three types of entities are involved: Cloud Service Users (CSU), Cloud Service Providers (CSP), and TPVs.…”

Section: System Modelmentioning

confidence: 99%

SIV: a structural integrity verification approach of cloud components with enhanced privacy

Zhao

Fan

Zhao

et al. 2019

Tinshhua Sci. Technol.

View full text Add to dashboard Cite

Private data leakage is a threat to current integrity verification schemes of cloud components. To address this issue, this work proposes a privacy-enhancing Structural Integrity Verification (SIV) approach. It is made up of three processes: proof organization, proof transformation, and integrity judgement. By introducing a Merkle tree technique, the integrity of a constituent part of a cloud component on a node is represented by a root value. The value is then masked to cipher texts in proof transformation. With the masked proofs, a structural feature is extracted and validated in an integrity judgement by a third-party verification provider. The integrity of the cloud component is visually displayed in the output result matrix. If there are abnormities, the corrupted constituent parts can be located. Integrity is verified through the encrypted masked proofs. All raw proofs containing sensitive information stay on their original nodes, thus minimizing the attack surface of the proof data, and eliminating the risk of leaking private data at the source. Although some computations are added, the experimental results show that the time overhead is within acceptable bounds.

show abstract

“…Therefore, the success of phishing attacks rely on the weaknesses of the user [6]. Individual users as well as organizations are vulnerable to many types of attacks including phishing attacks [7,8]. According to Anti-Phishing Working Group (APWG) [9] report, the phishing attacks ratio is doubled since early 2020.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Based Phishing Attacks Detection Using Multiple Datasets

Aljammal

Taamneh

Qawasmeh

et al. 2023

Int. J. Interact. Mob. Technol.

View full text Add to dashboard Cite

Nowadays, individuals and organizations are increasingly targeted by phishing attacks, so an accurate phishing detection system is required. Therefore, many phishing detection techniques have been proposed as well as phishing datasets have been collected. In this paper, three datasets have been used to train and test machine learning classifiers. The datasets have been archived by Phish-Tank and UCI Machine Learning Repository. Furthermore, Information Gain algorithm have been used for features reduction and selection purpose. In addition, six machine learning classifiers have been evaluated, namely NaiveBayes, ANN, DecisionStump, KNN, J48 and RandomForest. However, the classifiers have been trained and tested over the three datasets in two stages. The first stage is using all features included in each dataset while the second stage using selected features by IG algorithm. At the first stage RandomForest classifier has shown the best performance over Dataset-1 and Dataset-2, while J48 has shown the best performance over Dataset-3. On the other hand, after features selection, the RandomForest classifier was the superior among the other five classifiers over Dataset-1 and Dataset-2 with accuracy of 98% and 93.66% respectively. While ANN classifier has shown the best performance with accuracy of 88.92% over Dataset-3. Because of the few number of instances as well as features in Dataset-3 comparing to the other two dataset; the performance of the classifiers has been affected.

show abstract

Node Verification to Join the Cloud Environment Using Third Party Verification Server

Cited by 3 publications

References 0 publications

A Novel Scheme for Malicious Nodes Detection in Cloud Markets Based on Fuzzy Logic Technique

A Novel Scheme for Malicious Nodes Detection in Cloud Markets Based on Fuzzy Logic Technique

SIV: a structural integrity verification approach of cloud components with enhanced privacy

Machine Learning Based Phishing Attacks Detection Using Multiple Datasets

Contact Info

Product

Resources

About