scholarly journals Big Data Handling Approach for Unauthorized Cloud Computing Access

Electronics ◽  
2022 ◽  
Vol 11 (1) ◽  
pp. 137
Author(s):  
Abdul Razaque ◽  
Nazerke Shaldanbayeva ◽  
Bandar Alotaibi ◽  
Munif Alotaibi ◽  
Akhmetov Murat ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Access Control ◽  
Intrusion Detection ◽  
Data Access ◽  
Data Encryption ◽  
Data Handling ◽  
Unauthorized Access ◽  
Data Access Control ◽  
Cloud Computing System

Nowadays, cloud computing is one of the important and rapidly growing services; its capabilities and applications have been extended to various areas of life. Cloud computing systems face many security issues, such as scalability, integrity, confidentiality, unauthorized access, etc. An illegitimate intruder may gain access to a sensitive cloud computing system and use the data for inappropriate purposes, which may lead to losses in business or system damage. This paper proposes a hybrid unauthorized data handling (HUDH) scheme for big data in cloud computing. The HUDH scheme aims to restrict illegitimate users from accessing the cloud and to provide data security provisions. The proposed HUDH consists of three steps: data encryption, data access, and intrusion detection. The HUDH scheme involves three algorithms: advanced encryption standards (AES) for encryption, attribute-based access control (ABAC) for data access control, and hybrid intrusion detection (HID) for unauthorized access detection. The proposed scheme is implemented using the Python and Java languages. The testing results demonstrated that the HUDH scheme can delegate computation overhead to powerful cloud servers. User confidentiality, access privilege, and user secret key accountability can be attained with more than 97% accuracy.

scholarly journals Big Data Handling Approach for Unauthorized Access of Cloud Computing

2021 ◽  
Author(s):  
Abdul Razaque ◽  
Shaldanbayeva Nazerke ◽  
Bandar Alotaibi ◽  
Munif Alotaibi ◽  
Akhmetov Murat ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Access Control ◽  
Intrusion Detection ◽  
Data Access ◽  
Computing System ◽  
Data Handling ◽  
Unauthorized Access ◽  
Data Access Control ◽  
Cloud Computing System

Nowadays, cloud computing is one of the important and rapidly growing paradigms that extend its capabilities and applications in various areas of life. The cloud computing system challenges many security issues, such as scalability, integrity, confidentiality, and unauthorized access, etc. An illegitimate intruder may gain access to the sensitive cloud computing system and use the data for inappropriate purposes that may lead to losses in business or system damage. This paper proposes a hybrid unauthorized data handling (HUDH) scheme for Big data in cloud computing. The HUDU aims to restrict illegitimate users from accessing the cloud and data security provision. The proposed HUDH consists of three steps: data encryption, data access, and intrusion detection. HUDH involves three algorithms; Advanced Encryption Standards (AES) for encryption, Attribute-Based Access Control (ABAC) for data access control, and Hybrid Intrusion Detection (HID) for unauthorized access detection. The proposed scheme is implemented using Python and Java language. Testing results demonstrate that the HUDH can delegate computation overhead to powerful cloud servers. User confidentiality, access privilege, and user secret key accountability can be attained with more than 97% high accuracy.

Efficient revocable CP-ABE for big data access control in cloud computing

2019 ◽  
Vol 14 (3) ◽  
pp. 119 ◽  
Author(s):  
Syam Kumar Pasupuleti ◽  
P.J.A. Alphonse ◽  
Praveen Kumar Premkamal
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Access Control ◽  
Data Access ◽  
Data Access Control

Efficient revocable CP-ABE for big data access control in cloud computing

2019 ◽  
Vol 14 (3) ◽  
pp. 119 ◽  
Author(s):  
Praveen Kumar Premkamal ◽  
Syam Kumar Pasupuleti ◽  
P.J.A. Alphonse
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Access Control ◽  
Data Access ◽  
Data Access Control

scholarly journals Efficient Implementation of Big Data Access Control Scheme with Privacy-Preserving Policy

2019 ◽  
Vol 8 (10) ◽  
pp. 3385-3395
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Access Control ◽  
Control Systems ◽  
Information Access ◽  
Bloom Filter ◽  
Data Access ◽  
Physical Control ◽  
Data Access Control ◽  
Cloud Server

In the time of big data, cloud computing, an immense measure of information can be created rapidly from different IT, non-IT related sources. Towards these big data, cloud computing, customary PC frameworks are not up to required skilled to store and process this information. Due to the adaptable and flexible figuring assets, distributed computing is a characteristic fit for putting away and preparing big data. With cloud computing, end-clients store their information into the cloud server and depend on the advanced cloud server to share their information to different clients. To share end-client's information to just approved clients, it is important to configuration access control systems as indicated by the prerequisites of end clients. When re-appropriating information into the cloud, end-clients free the physical control, virtual physical control of their information. In addition, cloud specialist co-ops are not completely trusted by end-clients, which make the entrance control additionally testing. on the off chance that the conventional access control systems (e.g., Access Control Lists) are connected, the cloud server turns into the judge to assess the entrance approach and settle on access choice. Subsequently, end-clients may stress that the cloud server may settle on wrong access choices purposefully or accidentally and uncover their information to some unapproved clients. To empower end-clients to control the entrance of their own information, a proficient and fine-grained huge information access control plot with protection saving strategy is proposed. In particular, the entire trait (as opposed to just its qualities) in the entrance strategies are scrambled. To help information decoding, encoding, a novel Attribute Bloom Filter is utilized [14][16] to assess whether a characteristic is in the entrance arrangement and find the accurate position in the entrance approach on the off chance that it is in the entrance strategy. Just the clients whose traits fulfill the entrance arrangement are qualified to unscramble the information.

scholarly journals A Secure and Fine-Grained Big Data Access Control Scheme for Cloud-Based Services

2020 ◽  
pp. 254-262
Author(s):  
Nisha J William ◽  
Nisha O S
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Access Control ◽  
Data Access ◽  
End Users ◽  
Access Policy ◽  
Fine Grained ◽  
Data Access Control ◽  
Control Scheme ◽  
Access Policies

Cloud computing is the delivery of computing services including servers, storage, databases, networking, software, analytics, and intelligence over the Internet. Nowadays, access control is one of the most critical problems with cloud computing. Ciphertext-Policy Attribute Based Encryption (CP-ABE) is a promising encryption technique that enables end-users to encrypt their data under the access policies defined over some attributes of data consumers and only allows data consumers whose attributes satisfy the access policies to decrypt the data. In CP-ABE, the access policy is attached to the ciphertext in plaintext form, which may also leak some private information about end-users. Existing methods only partially hide the attribute values in the access policies, while the attribute names are still unprotected. This paper proposes an efficient and fine-grained big data access control scheme with privacy-preserving policy. Specifically, it hides the whole attribute (rather than only its values) in the access policies. To assist data decryption, it designs an algorithm called Attribute Bloom Filter to evaluate whether an attribute is in the access policy and locate the exact position in the access policy if it is in the access policy. The paper also deals with offline attribute guessing attack. Security analysis and performance evaluation show that this scheme can preserve the privacy from any LSSS access policy without employing much overhead.

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