Enhanced Honeypot cryptographic scheme and privacy preservation for an effective prediction in cloud security

Mondal, Avijit; Goswami, Radha Tamal

doi:10.1016/j.micpro.2020.103719

Cited by 42 publications

(17 citation statements)

References 11 publications

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“…A key issue is accurately describing the statistical characteristics of cyber attacks. In [20] article, we provide the rst statistical approach for thoroughly examining data from cyber attacks that honeypots have detected. A new class of mathematical objects for characterizing cyber attacks, the stochastic cyber attack process, serves as the foundation for the framework, while emphasizing that the system may also be used to analyze high-interaction honeypot data, which provides deeper information on the assaults [21].…”

Section: Related Workmentioning

confidence: 99%

Multi-Cloud Integration Security Framework Using Honeypots

Alyas

Alissa

Alqahtani

et al. 2022

Mobile Information Systems

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This rapidly changing digital world is always sensitive to improving security and resilience to protect the inhabitants of this ecosystem in terms of data, processes, repositories, communication, and functions. The transformation of this digital ecosystem is heavily dependent on cloud computing, as it is becoming the global platform for individuals, corporates, and even governments. Therefore, the concerns related to security are now linked closely with cloud computing. In this paper, a multi-cloud security framework takes a view on the development of security mechanisms to provide a diversion to the attacker. The purpose is to gain more time to analyze the attack and mitigate the intrusion without compromises. This mechanism is designed using the honeypot technology that has been around for some time but has not been used in cloud computing and other technologies. The proposed framework provides modules related to managing the multi-cloud platform, the intrusion detection and prevention system, and honeypots. The results show significant improvement in the accuracy of detecting attacks. These results are generated in a two-phase scenario, and the first phase has been analyzed without the engagement of the honeypot module presented in the framework. The second phase has been executed with same parameters and conditions by engaging the honeypot module. It includes a comparison taxonomy of both results and an in-depth study of existing honeypots, as well as critical design elements for current honeypot research and outstanding concerns for future honeypots in IoT, multi-cloud contexts.

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Section: Related Workmentioning

confidence: 99%

Multi-Cloud Integration Security Framework Using Honeypots

Alyas

Alissa

Alqahtani

et al. 2022

Mobile Information Systems

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“…Mondal et al [7] identify two issues with the security architecture and then offer two strategies to address them. The data's fundamental importance in safeguarding our CPS infrastructure from attack is addressed in the first recommendation, SCCAF (Smart Cloud Computing Adoption Framework).…”

Section: Literature Reviewmentioning

confidence: 99%

An enhanced encryption-based security framework in the CPS Cloud

et al. 2022

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The rapid advancement of computation techniques and cloud computing has led to substantial advancements in Cyber-Physical Systems (CPS), particularly in the field of health care. There are a variety of ways in which CPS is used in healthcare today, including delivering intelligent feedback systems, automatically updating patient data digitally, monitoring patients passively with biosensors, etc. In recent years, cyber-physical systems have become capable of making lifesaving decisions as they are becoming more connected to the cloud. However, healthcare has become one of the most critical issues for many. A CPS network uses the Internet of Medical Things (IoMT) to continuously monitor patients’ health metrics such as body temperature, heart rate, etc. Due to physical connectivity restrictions, networks are more susceptible to security threats. In spite of the fact that the data is stored in the cloud, it is necessary to provide security regardless of device security and network security. Several cyber-security vulnerabilities have been identified in cloud-based healthcare systems in particular. To give patients a reliable healthcare experience, security concerns with CPSs need to be addressed carefully. In this context, this paper proposes a Cross-Breed Blowfish and MD5 (CBM) approach to improve the security of health data in the CPS cloud. The proposed model uses the wireless sensor network, in which data acquired by the network is transmitted via the transmitting node. Using the fuzzified effective trust-based routing protocol (FET-RP), the most efficient path for data travel is selected. The best route is determined using Butter-Ant Optimization (BAO) algorithm. The proposed method conveys data throughput encryption and decryption in a decoded format. The encrypted data is then stored in the cloud database for security reasons. The route finding algorithm is the one which is sending the data from one end to other end. The data is encrypted based on the source and destination. We compare the performance metrics of our recommended technique to those of other existing techniques, such as RSA, Two fish, ICC, and FHEA, in order to ensure that it performs optimally. The values of Cross Breed Blowfish and MD5 and FET-RP with regard to the performance metrics in terms of encryption (60 ms), decryption (55 ms), latency (60 s), throughput (97 mbps), security level (98%), and execution time (57 ms) which outperforms the conventional methods by 10–15%. Also the proposed encryption shows the considerable improvement in the level of security making our model a real world solution.

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“…Avijit Mondal [29] presented an ID approach for detecting malicious attacks when performing data communication or data storage in the cloud. For identifying the malicious behavior, the Convolutional Neural Network (CNN) was used.…”

Section: Intrusion Detection With Encryptionmentioning

confidence: 99%

“…So it is necessary to develop a highly secure encryption model with an efficient authentication scheme. The models such as [29][30][31][32] are developed to achieve both ID and prevention for cloud data. However, they failed to achieve cloud user authentication when performing data transmission and data retrieval in the cloud.…”

Section: Problem Statementmentioning

confidence: 99%

An Improved Ecc Algorithm for Secure Cloud Storage System With the Help of Sha-256 Based User Authentication and Deep Learning Based Intrusion Detection System

Samy

Mary

2022

Preprint

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A cloud environment is a collection of resources used to provide on-demand services to cloud clients. Access to the cloud environment is supplied through internet services, and data kept in the cloud environment is more accessible to both internal and external invaders. At the moment, cloud security experts are unable to provide a more dependable, safe, and effective intrusion detection system (IDS) for identifying intruders in data transfer. For fulfilling this requirement, in this paper a novel deep learning (DL) model namely Wavelet Kernel including Multi-Layer Perceptron Neural Network (WKMLPNN) based IDS is proposed for identifying the attacks while performing cloud data transmission. Additionally, to offer cloud secure storage, Improved Elliptical Curve Cryptography (IECC) is proposed with a novel key generation mechanism. Initially, the cloud user or data owner (DO) is registered with the Cloud Service Provider (CSP) for uploading their data to the cloud. Then at the CSP end, the authenticity of the DO is checked by implementing the Secure Hash Algorithm 256 (SHA-256) technique. After successful authentication, the DO is allowed to upload their data to the cloud. The CSP receives the data from the DO and then the intrusion present in the received data is identified using novel IDS (WKMLPNN). The presented IDS follows three operations such as preprocessing, optimal feature selection using Time factor incorporated Gravitational Search Algorithm (TGSA) and WKMLPNN applied classification for identifying the intrusions present in the data. If the received data contain the intrusion, the data will be avoided and not stored in the cloud, otherwise, the data will be stored in the cloud securely using the IECC algorithm. Finally, if the cloud users request CSP for data retrieval, the authenticity of the user is again checked using the SHA-256 algorithm. If the user is an authenticated one, the retrieval permission is granted for him, otherwise, the permission is denied for the user. The simulation results reveal that the presented approach outperforms existing strategies for intrusion categorization and encryption.

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Enhanced Honeypot cryptographic scheme and privacy preservation for an effective prediction in cloud security

Cited by 42 publications

References 11 publications

Multi-Cloud Integration Security Framework Using Honeypots

Multi-Cloud Integration Security Framework Using Honeypots

An enhanced encryption-based security framework in the CPS Cloud

An Improved Ecc Algorithm for Secure Cloud Storage System With the Help of Sha-256 Based User Authentication and Deep Learning Based Intrusion Detection System

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