RETRACTED ARTICLE: Multi-parameter optimization for load balancing with effective task scheduling and resource sharing

Malarvizhi, N.; Aswini, J.; Sasikala, S.; Chakravarthy, M.; Neeba, E. A.

doi:10.1007/s12652-021-03005-2

Cited by 14 publications

(16 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Here, these two software are integrated and provides a bridge using API. On the other hand, it needs effective accessing control as well as various authorization strategies containing policies and rules involved in the delivery of services and software development 7–23 …”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, it needs effective accessing control as well as various authorization strategies containing policies and rules involved in the delivery of services and software development. [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23] The public API is established in such a way that the software can be accessed by anyone. In addition to this, the public API comprises limited capabilities that provide a security threat to the rear end-system.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Electro search optimization based long short‐term memory network for mobile malware detection

Shanmugam

Venkateswaralu²,

Rajalakshmi

et al. 2022

Concurrency and Computation

View full text Add to dashboard Cite

Mobile malware is malicious software designed specifically for targeting various mobile gadgets like tablets, smartphones, and so forth, in which any type of malicious code affecting the mobile devices without the knowledge of the user. The increasing number of users encourages the hacker for generating various malware applications. Therefore, in this paper, we utilized three vital phases namely the pre‐processing process, Feature extraction process as well as classification process in which the malicious data are detected. In the pre‐processing phase, an Androguard tool is used for decompiling and disassembling the android applications. The API call features are extracted in the feature extraction phase and in the classification phase, long short term memory based electro search optimization (LSTM‐ESO) is employed to detect the unknown mobile applications as benign or malicious. The malicious mobile detecting accuracy deals in requesting permission and exhibiting malicious code applications. In order to enhance the identification of various malware applications, this paper utilized frequency analysis and permissions of API calls. Finally, the experimental analysis is performed by evaluating the performance measures like accuracy, precision, recall, and F‐measure. From the evaluation outcome, it is observed that the classification accuracy obtained is 97.69%.

show abstract

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

Electro search optimization based long short‐term memory network for mobile malware detection

Shanmugam

Venkateswaralu²,

Rajalakshmi

et al. 2022

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

“…This step also minimizes the monetary cost and maximizes availability. When hosting the data in the cloud, providing high data availability and minimizing the expense is the main problem that is not focused on by the state‐of‐art techniques 1,10–15 . The existing techniques also fail to incorporate different important metrics such as data availability, storage cost, network cost and so forth.…”

Section: Introductionmentioning

confidence: 99%

“…When hosting the data in the cloud, providing high data availability and minimizing the expense is the main problem that is not focused on by the state-of-art techniques. 1,[10][11][12][13][14][15] The existing techniques also fail to incorporate different important metrics such as data availability, storage cost, network cost and so forth. To overcome the vendor lock-in problem various optimization techniques are used and the major contributions proposed in this article are presented as follows:…”

Section: Introductionmentioning

confidence: 99%

H2RUN: An efficient vendor lock‐in solution for multi‐cloud environment using horse herd Runge Kutta based data placement optimization

Kumar

Mala

2022

Trans Emerging Tel Tech

View full text Add to dashboard Cite

Vendor lock‐in is a scenario where the cost of switching to another vendor is mainly high and for this reason, the consumer is mainly struck with a single cloud vendor. The vendor lock‐in mainly implies the situation of an organization where it is locked into a service or product of a single Cloud Service Providers (CSP) to prevent the disruption of the business operation or due to an insufficient workforce. Due to its high availability, portability, and other benefits, various researchers frequently adopt the multi‐cloud strategy to prevent vendor lock‐in. However, the multi‐cloud architecture also comes with major drawbacks such as cost, multiple CSP management, and the tradeoff between cost and availability. The organizations are mainly considerate about achieving the tradeoff between the cost and availability. To overcome this problem, we have proposed an H2RUN (Horse Herd Runge Kutta) optimization algorithm to achieve a tradeoff between the cost and availability using erasure coding. The proposed H2RUN optimization algorithm selects the optimal CSP and erasure coding parameters that enhance the availability of the CSP with minimal cost. The efficiency of the proposed methodology can be evaluated using the experiments conducted on different real‐time CSPs using various metrics. Based on the results achieved, the H2RUN optimizer minimizes the cost up to 48% and availability up to 58%.

show abstract

“…4 The traditional machine learning methods had higher dimensionality; higher execution time for data classification, most of the authors failed to describe different kinds of attacks, higher cost, speed, and time omitting important features in data and so forth. 2,[6][7][8][9][10][11][12] The conventional deep learning techniques like deep feed-forward neural network (DNN), long short term memory (LSTM), and convolutional neural network (CNN), and so on met fewer performances in terms of feature selection, poor hardware infrastructure, higher computational complexities and so forth. 13 To tackle these challenges, we proposed convolutional neural network-based Hybrid Whale Tabu Optimization (CNN-HWTO) algorithm for intrusion detection.…”

mentioning

confidence: 99%

Hybrid Whale Tabu algorithm optimized convolutional neural network architecture for intrusion detection in big data

Ponmalar

Dhanakoti²

2022

Concurrency and Computation

View full text Add to dashboard Cite

The rate of internet traffic in the digital world has expanded fast due to technological improvement. Because of the large number of internet users, there is a large volume of network traffic, and it is the most common and challenging task to evaluate, process, and store the data. The intrusion detection systems enhance the performance of early attacks prediction and malicious attack detection. The exiting techniques met a few challenges in terms of feature selection, computational cost, accuracy, higher dimensionality, computational time and so forth. To solve these issues, we proposed a novel deep learning model for intrusion detection in big data. The data was collected from NSL‐KDD, KDD‐Cup99, and UNSW‐NB15 datasets. Initially, the big data framework with a testbed is set up for big data analytics and processing in which the required information from a large volume is extracted. The datasets are stored in the Hadoop Distributed File System, which reduces the number of latencies in the distributed process. Finally, the intrusion detection from big data is performed using convolutional neural network‐based Hybrid Whale Tabu Optimization algorithm and it effectively classifies whether the data is intrusion is non‐intrusion. The proposed method provided appropriate and superior experimental results using the state‐of‐art method with various performance measures such as accuracy, Matthews correlation coefficient, balanced accuracy, specificity, sensitivity, precision, and F‐measures.

show abstract

RETRACTED ARTICLE: Multi-parameter optimization for load balancing with effective task scheduling and resource sharing

Cited by 14 publications

References 29 publications

Electro search optimization based long short‐term memory network for mobile malware detection

Electro search optimization based long short‐term memory network for mobile malware detection

H2RUN: An efficient vendor lock‐in solution for multi‐cloud environment using horse herd Runge Kutta based data placement optimization

Hybrid Whale Tabu algorithm optimized convolutional neural network architecture for intrusion detection in big data

Contact Info

Product

Resources

About