Hybrid RSAECC Based Secure Communication in Mobile Cloud Environment

Sridhar, S K; Smys, S.

doi:10.1007/s11277-019-06867-0

Cited by 6 publications

(5 citation statements)

References 18 publications

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“…We implemented Whirlpool because (a) the anticipated capability of producing an impact was of the mandate of 2 x/2 performances of Whirlpool; (b) Specified an n-bit rate, the predictable capability of discovering a communicated/buffered data that jumbles to that value is of the mandate of 2 x performances of Whirlpool; and (c) assumed a communication and its n-bit hash outcome, the anticipated capacity of discovering a subsequent data that hashes to the identical value is of the order of 2 x implementations of Whirlpool. Figure 3 illustrates the proposed scheme that utilizes 'hybrid encryption (RSA-AES) [14] [24]. It integrates a blend of asymmetric and symmetric encoding/decoding to profit from the vitality of each arrangement of encryption.…”

Section: Node Security Using Blockchainmentioning

confidence: 99%

Context-Aware Autonomous Security Assertion for Industrial IoT

et al. 2020

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The Industrial Internet of Things (IIoT) platform consists of purpose-driven communication controllers, enterprise-grade modems (routers and gateways), and edge computing systems that require integrated software and sensing capability in mission-critical environments. Extensible purpose-built industrial supervisory control and data acquisition networks are prone to numerous cybersecurity threats. In this paper, the historical databased qualitative threat assessment was part of the comprehensive risk breakdown (i.e., to quantify assessment and remediation) of the practicing industry (i.e., systems that rely on robotics, big data & analytics). Furthermore, a risk and operability (HAZOP & convolution neuralnetwork) evaluation was proved to be the paramount study for autonomous vulnerability assessment. Through autonomous network management, continuous software monitoring, data-driven device insights, and integrated content filtering, the proposed endpoint protection scheme shows significant improvement in preventing data breaches, denial of service (DoS), and malware detection. A distinctive computational methodology to determine the cyber risk for industrial structures with IoT-explicit control factors has been programmed and elucidated in the perspective of IIoT systems. Firmware driven emulation (integrated and optimized) outcome aided to reduce breach ratio, better incident detection, and enhanced protection of confidential data.

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Section: Node Security Using Blockchainmentioning

confidence: 99%

Context-Aware Autonomous Security Assertion for Industrial IoT

et al. 2020

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“…To evaluate the proposed method, the computation costs of RSA (Rivest-Shamir-Adleman) [1] and ECC (elliptic curves cryptography) [2] were compared in TABLE VII. In our experiments, the number of records is m. The computation costs of encryption and summation by these methods (i.e.…”

Section: Evaluation and Analysesmentioning

confidence: 99%

“…With the privacy application development of the Internet of things, more and more terminal devices access the Internet and transmit data via web services. However, massive calculations are needed by encryption and decryption for processing huge amounts of data, for instance, the time complexities of RSA (Rivest-Shamir-Adleman) [1] and ECC (elliptic curves cryptography) [2] for encryption and decryption are both O(m), where m is the number of records. Therefore, this study proposes a multiple linear regression system coupled with method based on stream homomorphic encryption computing.…”

Section: Introductionmentioning

confidence: 99%

“…This study combines the multiple linear regression and homomorphic encryption to calculate the ciphertexts of records and obtain the plaintext of results in secure environments. (2). This study designs and implements the architecture of decentralized computing system to assign tasks to several node devices for processing huge amounts of data.…”

Section: Introductionmentioning

confidence: 99%

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Multiple Linear Regression Based on Stream Homomorphic Encryption Computing

Zhang

Liu

Chung

et al. 2020

2020 International Symposium on Computer, Consumer and Control (IS3C)

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This study proposes a multiple linear regression architecture based on stream homomorphic encryption computing to analyze ciphertext for massive secure data computing. The proposed architecture contains three subsystems including terminal subsystem, data access subsystem, and data computing subsystem. The method used behind the presented architecture contains four stages which are data preprocessing stage, data access stage, data computing stage, and result processing stage. In the practical experiments, a case study of traffic information prediction was selected to evaluate the proposed system and method. The predicted traffic information was generated by the proposed method in accordance with the encrypted traffic information. Our experimental results showed that the proposed architecture can effectively and promptly obtain the predicted traffic information.

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“…The popular flexible model for IDS deployments in cloud environments is Infrastructure as Services (IaaS) according to cloud architecture. 15 For intrusion detection, the traditional network controls the traffic and detects attacks on the hypervisor and virtual machines. 16 The higher accurate IDSs are used to detect the larger volume of network data and new attack behaviors.…”

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confidence: 99%

Oppositional tunicate fuzzy C‐means algorithm and logistic regression for intrusion detection on cloud

Kanimozhi¹,

Victoire

2021

Concurrency and Computation

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Cloud computing is the most popular emerging computational model in information technology. The data communication security requirement is increased day by day with the development of cloud computing. Virtualization facilitates cloud computing architecture, which raises a few security concerns. In the previous year, many new research approaches were introduced, resulting in more sophisticated and time-consuming results, as well as less flexibility, a lower attack detection rate, and so on. In this article, we proposed a logistic regression-based oppositional tunicate fuzzy C-mean (LR-OTSFCM) methodology for the detection of cloud intrusion. The major contribution of this article is attack detection from the cloud environment. The proposed work is classified into four stages, namely, preprocessing, partitioning, cluster handling, and intrusion detection stages. In the first stage, we use the text data as the input; then, it is preprocessed. Second, the feature selections are performed via a logistic regression model. The combination of the oppositional tunicate swarm algorithm (OPTSA) and fuzzy C-mean clustering (FCM) model is to perform data clustering. Third, the cluster handling stage is handled by cluster expansion and integration. Fourth, the clustering result builds a profile of normal and abnormal behavior. The local optimum avoidance ability of OPTSA is tested using four benchmark problems, namely, composition functions, unimodal, multimodal, and fixed dimensions of multimodal functions with 20 benchmark functions. Ultimately, the proposed LR-OTSFCM method is compared with state-of-art approaches such as ANN, LR-HID, ML-IDS, and En-ABC. The proposed scheme effectiveness is carried out by using four datasets such as, namely, synthetic, NIB, CICID 2017, and CIDD datasets. The proposed method demonstrated a superior attack detection rate.

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Hybrid RSAECC Based Secure Communication in Mobile Cloud Environment

Cited by 6 publications

References 18 publications

Context-Aware Autonomous Security Assertion for Industrial IoT

Context-Aware Autonomous Security Assertion for Industrial IoT

Multiple Linear Regression Based on Stream Homomorphic Encryption Computing

Oppositional tunicate fuzzy C‐means algorithm and logistic regression for intrusion detection on cloud

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