A Technical Survey on Intelligent Optimization Grouping Algorithms for Finite State Automata in Deep Packet Inspection

Samuel, Prithi; Subbaiyan, Sumathi; Balamurugan, B.; Sumathi, D.; Gandomi, Amir H.

doi:10.1007/s11831-020-09419-z

Cited by 16 publications

(4 citation statements)

References 44 publications

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“…RDA balance between exploration (global search) and exploitation (local search) of the solution space. Unlike traditional optimization methods, RDA often require minimal problem-specific knowledge or parameter tuning 65 .…”

Section: Proposed Methodologymentioning

confidence: 99%

Performance enhancement in clustering cooperative spectrum sensing for cognitive radio network using metaheuristic algorithm

Srivastava,

Singh,

Mahajan

et al. 2023

Sci Rep

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Spectrum sensing describes, whether the spectrum is occupied or empty. Main objective of cognitive radio network (CRN) is to increase probability of detection (Pd) and reduce probability of error (Pe) for energy consumption. To reduce energy consumption, probability of detection should be increased. In cooperative spectrum sensing (CSS), all secondary users (SU) transmit their data to fusion center (FC) for final measurement according to the status of primary user (PU). Cluster should be used to overcome this problem and improve performance. In the clustering technique, all SUs are grouped into clusters on the basis of their similarity. In cluster technique, SU transfers their data to cluster head (CH) and CH transfers their combined data to FC. This paper proposes the detection performance optimization of CRN with a machine learning-based metaheuristic algorithm using clustering CSS technique. This article presents a hybrid support vector machine (SVM) and Red Deer Algorithm (RDA) algorithm named Hybrid SVM–RDA to identify spectrum gaps. Algorithm proposed in this work outperforms the computational complexity, an issue reported with various conventional cluster techniques. The proposed algorithm increases the probability of detection (up to 99%) and decreases the probability of error (up to 1%) at different parameters.

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Section: Proposed Methodologymentioning

confidence: 99%

Performance enhancement in clustering cooperative spectrum sensing for cognitive radio network using metaheuristic algorithm

Srivastava,

Singh,

Mahajan

et al. 2023

Sci Rep

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“…Swarm intelligence optimization algorithms have been developed by many researchers. [ 28 ] These algorithms completely overcome the previously discussed shortcomings of the traditional algorithm because they belong to global optimization and do not rely on gradient information. Among the swarm intelligent algorithms, the PSO algorithm is easy to implement owing to its simple parameter adjustment process.…”

Section: Algorithm Implementationmentioning

confidence: 99%

“…Thus, a novel practical model for the ring-down spectral signal was established. Then, we introduced the particle swarm optimization (PSO) algorithm [28,29] into FLRDS to accomplish pulse-shape fitting of the ring-down spectral signal and extract the ring-down time. To our knowledge, the development of a model based on the amplitude modulation theory and the application of the PSO algorithm to the ring-down spectral signal have not been reported in existing literature.…”

Section: Introductionmentioning

confidence: 99%

An Effective Spectral Processing Method for Low‐Cost Fiber Loop Ring‐Down Systems

Guo-zhi

Wang

et al. 2023

Advcd Theory and Sims

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Fiber loop ring-down spectroscopy (FLRDS) is a highly sensitive spectroscopic technology. Here, a spectral processing method is proposed for low-cost fiber loop ring-down systems. In the proposed method, the amplitude modulation theory is used to establish a new ring-down spectral model, and the particle swarm optimization algorithm is introduced into FLRDS. The experimental results demonstrate that the proposed method can effectively process the ring-down spectral signals acquired from the low-cost fiber loop ring-down system. Moreover, this method demonstrates higher accuracy and stronger adaptability than the current widely used method. Owing to its excellent performance, the proposed method has considerable potential to be applied in low-cost fiber loop ring-down systems.

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“…In terms of network security, the undesired traffic cannot be filtered by the header information because most threats target the application layer [5,6]. Deep packet inspection and payload checking are needed to assure protection against viruses, malware, spyware, spam and deliberate incursion efforts, making this task increasingly difficult, leading to the design of security filters with lower processing time using GPUs and FPGAs [7]. Bioinformatics applications, where a considerable number of reads must be mapped (or aligned) to a longer reference or database sequence, also make use of string similarities evaluation [8].…”

Section: String Similarities In Large Datasetsmentioning

confidence: 99%

GPU Based Similarity Metrics Computation and Machine Learning Approaches for String Similarity Evaluation in Large Datasets

Baloi

Belean

Turcu

et al. 2022

Preprint

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The digital era brings up on one hand massive amounts of available data, and on the other hand the need of parallel computing architectures for efficient data processing. String similarity evaluation is a processing task applied on large data volumes, commonly performed by various applications such as search engines, bio-medical data analysis and even software tools for defending against viruses, spyware, or spam. String similarities are also evaluated in musical industry for matching playlist records with repertory records composed of song titles, performer artists and producers names, aiming to assure copyright protection of massmedia broadcast materials. Thus, the present paper proposes a GPU based approach for parallel implementation of the Jaro-Winkler string similarity metric computation. Further on, a thresholding-based algorithm is also implemented using GPU for matching records over large datasets. The global GPU RAM memory is used to store multiple string lines as raw data. In the case of a single string, its comparisons with the raw data are performed using the maximum number of available GPU threads and the stride operations. Moreover, based on the computed similarity metrics, an adaptive neural network approach guided by a novelty detection classifier together with a naive neural network implementation are proposed to increase the accuracy of the records matching procedure. Timing considerations and the computational complexity are detailed for the proposed approaches compared with state-of-the-art CPU and GPU approaches. A speed-up factor of 21.6 was obtained for the GPU based JaroWinkler implementation compared with the general purpose processor one, whereas improved accuracy for the records matching procedure was delivered using machine learning approaches.

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A Technical Survey on Intelligent Optimization Grouping Algorithms for Finite State Automata in Deep Packet Inspection

Cited by 16 publications

References 44 publications

Performance enhancement in clustering cooperative spectrum sensing for cognitive radio network using metaheuristic algorithm

Performance enhancement in clustering cooperative spectrum sensing for cognitive radio network using metaheuristic algorithm

An Effective Spectral Processing Method for Low‐Cost Fiber Loop Ring‐Down Systems

GPU Based Similarity Metrics Computation and Machine Learning Approaches for String Similarity Evaluation in Large Datasets

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