Efficient Method for Discriminating Flash Event from DoS Attack during Internet Protocol Traceback using Shark Smell Optimization Algorithm

Salami, Omoniyi Wale; Umoh, Ime J.; Adedokun, Emmanuel Adewale; Mu’azu, Muhammed Bashir; Ajao, Lukman Adewale

doi:10.1109/nigeriacomputconf45974.2019.8949671

Cited by 3 publications

(3 citation statements)

References 15 publications

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“…Shark Smell Optimization (SSO) has relied on the Shark's ability because it has superiority in catching prey by using a strong smell sense in a short time [18]- [20]. SSO algorithm consists of four basic steps as follows:…”

Section: Feature Extraction By Using Sso Algorithmmentioning

confidence: 99%

Fingerprint recognition based on shark smell optimization and genetic algorithm

Ahmed

Abdulhameed

2020

Int. J. Adv. Intell. Informatics

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Fingerprint recognition is a dominant form of biometric due to its distinctiveness. The study aims to extract and select the best features of fingerprint images, and evaluate the strength of the Shark Smell Optimization (SSO) and Genetic Algorithm (GA) in the search space with a chosen set of metrics. The proposed model consists of seven phases namely, enrollment, image preprocessing by using weighted median filter, feature extraction by using SSO, weight generation by using Chebyshev polynomial first kind (CPFK), feature selection by using GA, creation of a user’s database, and matching features by using Euclidean distance (ED). The effectiveness of the proposed model’s algorithms and performance is evaluated on 150 real fingerprint images that were collected from university students by the ZKTeco scanner at Sulaimani city, Iraq. The system’s performance was measured by three renowned error rate metrics, namely, False Acceptance Rate (FAR), False Rejection Rate (FRR), and Correct Verification Rate (CVR). The experimental outcome showed that the proposed fingerprint recognition model was exceedingly accurate recognition because of a low rate of both FAR and FRR, with a high CVR percentage gained which was 0.00, 0.00666, and 99.334%, respectively. This finding would be useful for improving biometric secure authentication based fingerprint. It is also possibly applied to other research topics such as fraud detection, e-payment, and other real-life applications authentication.

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Section: Feature Extraction By Using Sso Algorithmmentioning

confidence: 99%

Fingerprint recognition based on shark smell optimization and genetic algorithm

Ahmed

Abdulhameed

2020

Int. J. Adv. Intell. Informatics

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“…The key factor that guides the shark toward its prey is concentration. Moreover, if the concentration is strong, the shark's movement will be increased [17], [18]. The pseudo code of the SSO Algorithm is described in the algorithm (1) below.…”

Section: Sso Algorithmmentioning

confidence: 99%

Fingerprint Authentication using Shark Smell Optimization Algorithm

Ahmed

Abdulhameed

2020

UHD J SCI TECH

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Recognition of people relying on biometric characteristics is a common phenomenon in our society. It has increased in recent years in most areas of life such as government, department, companies, and banks. Fingerprint identification is one of the most common and credible personal biometric identification methods. Extracting features are one of the most important steps in the fingerprint identification; the strength of any system depends mainly on this step, where whenever the features obtained are accurate whenever the identification process is more accurate. Therefore, an effective and efficient method must be used to extract the features. This paper solved two main problems that were (1) improving security by designing and implementing an accurate, efficient, and fast authentication system for the identification and verification process using an intelligent algorithm to extract the best features from the fingerprint image and (2) evaluating the strength of the Shark Smell Optimization (SSO) in the search space with a chosen set of metrics. This paper aims to extract the best features of the fingerprint image using an algorithm that depends on nature for its movement and work; therefore, the SSO was used. In this paper, the SSO algorithm is used to extract the features. SSO is a new meta-heuristic algorithm that uses smart methods and random movements to get its prey. These methods and movements were used to extract features from the fingerprint image which will be used later for identification and verification process. The proposed method is implemented through four phases, namely, create a database to store and organize data, image pre-processing using median filter, feature extraction using SSO algorithm, and matching process using euclidean distance. The results demonstrated the strength, accurate, credible, and effectiveness of the algorithm used by applying it on (150) real fingerprint samples taken from university students, where the results of false acceptation rate, false rejection rate, and correct verification rate were 0.00, 0.00666, and 99.334%, respectively.

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“…The idea of combining a uniform random number in the range of [1, 0] with a distributed speed-updating procedure has been derived from the gravitational search method. Accordingly, we can present the speed in any dimension by the following format (Salami et al, 2019):…”

Section: Shark Movement Toward Bait: the Evolution Of Algorithmsmentioning

confidence: 99%

Load balancing in the internet of things using fuzzy logic and shark smell optimization algorithm

Rui

Zhao

et al. 2020

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Purpose Based on the positive features of the shark smell optimization (SSO) algorithm, the purpose of this paper is to propose a method based on this algorithm, dynamic voltage and frequency scaling (DVFS) model and fuzzy logic to minimize the energy consumption of integrated circuits of internet of things (IoT) nodes and maximize the load-balancing among them. Design/methodology/approach Load balancing is a key problem in any distributed environment such as cloud and IoT. It is useful when a few nodes are overloaded, a few are under-loaded and the remainders are idle without interrupting the functioning. As this problem is known as an NP-hard one and SSO is a powerful meta-hybrid method that inspires shark hunting behavior and their skill to detect and feel the smell of the bait even from far away, in this research, this study have provided a new method to solve this problem using the SSO algorithm. Also, the study have synthesized the fuzzy logic to counterbalance the load distribution. Furthermore, DVFS, as a powerful energy management method, is used to reduce the energy consumption of integrated circuits of IoT nodes such as processor and circuit bus by reducing the frequency. Findings The outcomes of the simulation have indicated that the proposed method has outperformed the hybrid ant colony optimization – particle swarm optimization and PSO regarding energy consumption. Similarly, it has enhanced the load balance better than the moth flame optimization approach and task execution node assignment algorithm. Research limitations/implications There are many aspects and features of IoT load-balancing that are beyond the scope of this paper. Also, given that the environment was considered static, future research can be in a dynamic environment. Practical implications The introduced method is useful for improving the performance of IoT-based applications. We can use these systems to jointly and collaboratively check, handle and control the networks in real-time. Also, the platform can be applied to monitor and control various IoT applications in manufacturing environments such as transportation systems, automated work cells, storage systems and logistics. Originality/value This study have proposed a novel load balancing technique for decreasing energy consumption using the SSO algorithm and fuzzy logic.

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Efficient Method for Discriminating Flash Event from DoS Attack during Internet Protocol Traceback using Shark Smell Optimization Algorithm

Cited by 3 publications

References 15 publications

Fingerprint recognition based on shark smell optimization and genetic algorithm

Fingerprint recognition based on shark smell optimization and genetic algorithm

Fingerprint Authentication using Shark Smell Optimization Algorithm

Load balancing in the internet of things using fuzzy logic and shark smell optimization algorithm

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