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lion IDS: A meta-heuristics approach to detect DDoS attacks against Software-Defined Networks

NEURAL COMPUTING & APPLICATIONS 2019年第5期31卷 1491-1501页

作者： Arivudainambi, D. Kumar, Varun K. A. Chakkaravarthy, S. Sibi Anna Univ Dept Math Chennai Tamil Nadu India Anna Univ Madras Inst Technol Dept Elect Engn Chennai Tamil Nadu India

Most of the enterprises are transforming their conventional networks into Software-Defined Network (SDN) to avail the cost efficiency and network flexibility. But recent attacks and security breaches against SDNs expose the security weakness of the technology. Distributed Denial of Service (DDoS) is the most common attack launched against various SDN architecture layers. Hence, DDoS has been claimed to be the most dangerous attack and threat to SDN. The existing mitigation techniques are traffic volumetric methods, entropical methods and traffic flow analysis methods. They depend on traffic sampling to achieve truly inline against DDoS detection accuracy in real time. However, traffic sampling-based methods are expensive with chances for incomplete approximation of underlying traffic patterns being very high. Early detection of DDoS attack in the controller is critical and requires highly adaptive and accurate methods. In this paper, an effective and accurate DDoS detection method using lion optimization algorithm is proposed. The proposed detection technique is robust enough to detect DDoS attack within the least magnitude of attack traffic. Further, to evaluate the performance, the proposed method is compared with the state-of-the-art techniques. The outcome of this paper is current method limitation and scope for improvement depicted from overall study and analysis. The experimental results have proved that the proposed method outperforms the existing state-of-the-art methods with 96% accuracy.

关键词： Software-Defined Networks Distributed Denial of Service lion optimization algorithm Security

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Levenberg marquedet lion based artificial neural network for cooperative spectrum sensing in cognitive radio

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MULTIAGENT AND GRID SYSTEMS 2018年第4期14卷 321-336页

作者： Yelalwar, Rajendra Ravinder, Yerram Pune Inst Comp Technol Dept Elect & Tele Commun Engn Pune Maharashtra India Savitribai Phule Pune Univ Pune Maharashtra India

Cognitive Radio (CR) is a promising wireless communication system that allows the user in wireless environment to communicate with wide band of frequencies. Spectrum sensing is the most vital function of CR that plays significant role in identifying the available channel in CR environment. Hence, this work introduces the novel cooperative spectrum sensing approach based on the proposed optimal fusion score that enhances spectrum sensing performance. The optimal fusion score finds the required channel availability by training the proposed Levenberg Marquedet (LM), and the lion optimization algorithm (LOA) based Neural Network (LML-NN) classifier and the fuzzy classifier. The inputs for the classifier training are test statistics based on the Energy Detection (ED), and the Generalized Likelihood Ratio Test (GLRT). These test statistics are derived using the eigenvalues extracted from a received signal covariance matrix. The performance analysis of the system is done by varying the number of sensors in CR and threshold values. The system performance is quantified in terms of probability of detection, probability of false alarm and receiver operating curves. The simulation results confirm that proposed LML-NN algorithm has achieved an improved probability of detection and reduced false alarm rate as compared to the existing eigenvalue based CSS model.

关键词： Cognitive radio cooperative spectrum sensing optimal fusion score levenberg marquedet lion optimization algorithm

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The lion Cloud Optimizer 1

The Lion Cloud Optimizer

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1st International Conference on Computer Applications and Information Security (ICCAIS)

作者： Almezeini, Nora Hafez, Alaaeldin King Saud Univ Coll Comp & Informat Sci Riyadh Saudi Arabia

ISBN: (纸本)9781538644270

Resource management and efficient performance of task scheduling is one of the main research issues in cloud computing. In this paper, an enhanced task scheduling algorithm based on the concept of lion's lifestyle has been introduced for cloud computing environment. The enhancement is applied by benchmarking the performance of the task scheduling algorithm using real workload traces to evaluate high level of realism performance and effectiveness. A comparative study has been done among the enhanced algorithm, task scheduling based on Genetic algorithm (GA), and task scheduling based on Particle Swarm optimization (PSO) to evaluate the performance of the enhanced algorithm. The results demonstrate higher performance than the other algorithms.

关键词： Cloud computing task scheduling algorithm cloud scheduling lion optimization algorithm optimization algorithm

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Despise Detection through LOA Using Fs with 1D-CNN in Public Forum 5

Despise Detection through LOA Using Fs with 1D-CNN in Public...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Revathi, V. Gunapriya, B. Kavin, Balasubramanian Prabhu Gangadevi, E. Balusamy, Balamurugan Gite, Shilpa New Horizon College of Engineering Ring Road Bellandur Post Bengaluru India Srm Institute of Science and Technology Department of Data Science and Business Systems Chengalpattu Kattankulathur India Loyola College Department of Computer Science Chennai India Shiv Nadar University Delhi India Symbiosis Institute of Technology Pune India

ISBN: (纸本)9798350383522

Hate speech is any speech intended to incite hatred in others. Hate speech is escalating quickly and unpredictably in this digital age where everyone is connected via social media. Due to a lack of understanding of the distinction among hate speech numerous people do not realise they are engaging in hate speech when they criticise somewhat on social media. Victims experience a sense of social alienation as a result, and those who propagate it frequently end up in legal trouble. To combat people's ignorance, it is crucial to detect any instances of hate speech in the sentences. A machine learning technique is frequently used to help classify each sentence in order to detect such sentences. Additionally, it is a difficult duty because to the enormous volume of rising hate speech on social media from diverse sources. This work offers a lion optimisation algorithm (LOA) with Processing Based Hate Speech Detection (HSD) and Classification model in response to this objective. The main goal of the proposed LOA-based HSD model is to recognise and categorise instances of hate speech on social media platforms. The described model uses data preprocessing at numerous phases, such as tokenization, vectorization, etc., to achieve this. In addition, the organisation of social media text into three categories-neutral, offensive, and hate language-is done using a convolutional neural network (ID-CNN) model. The proposed model is experimentally validated, and the outcomes are assessed from several angles. The results of the experiment showed that the LOA-ID-CNN model achieved favourably compared to current state-of-the-art techniques © 2024 IEEE.

关键词： Convolutional Neural Network Hate Speech Finding lion optimization algorithm social media Twitter. Natural Language Processing

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Energy Efficient Data Migration Concerning Interoperability Using Optimized Deep Learning in Container-Based Heterogeneous Cloud Computing

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ADVANCES IN ENGINEERING SOFTWARE 2023年第1期183卷

作者： Hiremath, Tej. C. Rekha, K. S. Natl Inst Engn Dept Comp Sci & Engn Mysuru 570008 India

Cloud computing generates a proper computing platform and facilitates optimizing with the utilization of infrastructure resources, increases flexibility, and decreases deployment time. Interoperability is one of the major challenges to be studied for ensuring seamless access and sharing of services and resources. Containers have developed into the most dependable and lightweight platform for virtualization to deliver cloud services that offer flexible sorting, scalability, and portability. This paper presents energy-efficient data migration approach using hybrid optimized deep learning in a heterogeneous cloud. Simulation of the cloud is carried out with Physical Machines (PM), container, and Virtual Machines (VM) in the cloud. Migration application is done with proposed Taylor lion-based Poor and Rich optimization (Taylor lion-based PRO), wherein load is found by Actor Critic Neural Network (ACNN). Moreover, objective functions utilized are agility, migration time, predicted load, demand, transmission cost, resource capacity, energy consumption, as well as reputation. Here, Taylor lion-based PRO is formed by hybridization of the Taylor series along lion optimization algorithm (LOA), and Poor and Rich optimization (PRO). Furthermore, the performance of data migration concerning interoperability is carried out with three performance metrics, like load, resource capacity, and energy consumption of 0.006, 0.364, and 0.281.

关键词： Poor and Rich optimization Taylor series Actor Critic Neural Network Predicted load lion optimization algorithm

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Safe and secured routing using multi-objective fractional artificial lion algorithm in WSN

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2022年第21期34卷 e7098-e7098页

作者： Prakash, Perumalla Suman Kavitha, Dwaram Reddy, Pakanati Chenna Jawaharlal Nehru Technol Univ Dept CSE Ananthapuramu 515002 Andhra Pradesh India G Pulla Reddy Engn Coll Comp Sci & Engn Kurnool Andhra Pradesh India

The routing process in WSN needs to be more secure and safe, thus a secure routing process is carried out in this research, where the source node transmits packet simultaneously to destination. WSN nodes are simulated initially, and then the optimal cluster head (CH) from the entire nodes are selected. Here, CH selection is done with the developed Fractional Artificial lion algorithm (FAL) method. The proposed FAL approach is introduced by combining fractional Calculus, lion optimization algorithm (LOA), and the Artificial Bee Colony (ABC). Moreover, the fitness parameters, like delay, energy, distance, and link lifetime (LLT) where the minimal distance is chosen as the best path. Consequently, route maintenance is done after the secure routing. Moreover, the performance of devised FAL is compared with various performance metrics, like several alive nodes, energy, distance, and throughput. The developed FAL approach obtains a maximal alive node of 45, a minimal distance of 9.29 m, maximal energy of 0.14 J, and maximal throughput of 92.25%.

关键词： Artificial Bee colony fractional calculus link lifetime lion optimization algorithm WSN

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Charging station placement optimization based on the location significance prediction

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ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS 2024年第1期46卷 12218-12239页

作者： Matkovic, Daria Matijasevic, Terezija Capuder, Tomislav Univ Zagreb Fac Elect & Comp Engn Unska 3 Zagreb 10000 Croatia

One of the key challenges in charging infrastructure planning is ensuring optimal charging station placement. To address this issue, we introduce a novel approach to optimize the placement of electric vehicle charging stations, integrating a novel location-based charging station significance prediction model with a lion optimization algorithm (LOA) where the significance is defined as the combination of charging energy and the number of sessions. First, the data recorded on the existing charging stations are analyzed and preprocessed. Subsequently, we introduced the modified support vector regression (SVR) model for significance prediction and compared it with eight existing models showing its superiority over others. The SVR modification is related to the kernel function, where the standard Gaussian kernel is adapted to better suit location-based significance predictions. Following this, we utilize LOA to optimize the placement of additional charging stations in established charging infrastructure based on the prediction model trained on data congregated at the existing charging stations. The optimization is conducted for Zagreb and Split to evaluate the performance on small and large datasets. The results are assessed using significance prediction and pseudo-simulation. The new charging stations in Zagreb have a significance prediction of 10.53% greater than the calculated significance of existing charging stations. Furthermore, the significance prediction for new stations in Split is 0.42% greater than the calculated significance for existing stations. Pseudo-simulation proves that new charging stations have 38.15% greater significance than the existing stations in Zagreb and 31.24% in Split. Both methods confirm that infrastructure significance, in terms of charging energy and session count, is improved.

关键词： charging station charging station placement Electric vehicles lion optimization algorithm support vector regression

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Parallel architecture for cotton crop classification using WLI-Fuzzy clustering algorithm and Bs-lion neural network model

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The Journal of Photographic Science 2017年第8期65卷 438-456页

ABSTRACTABSTRACTThe application of remote sensory images in crop monitoring has been increasing in the recent years due to its high classification accuracy. In this paper, a novel parallel classification methodology is proposed using a new clustering and classification concept. A novel neural network model with the Bs-lion training algorithm is developed by integrating the Bayesian regularization training with the lion algorithm. Here, two levels of parallel processing are performed, namely parallel WLI-Fuzzy clustering and parallel BS-lion neural network classification. The experimentation of the proposed parallel methodology is carried out using satellite images obtained from the Indian remote sensing satellite IRS-P6. The performance of the proposed system is compared with the existing techniques using validation measures accuracy, sensitivity and specificity. The experimentations resulted in promising results with an accuracy of 0.8994, sensitivity of 0.8682 and specificity of 0.8739, which favour the performance of the proposed parallel architecture in the classification.

关键词： Remote sensing imagery Bayesian regularization neural network lion optimization algorithm WLI-Fuzzy clustering

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Automatic text classification using BPlion-neural network and semantic word processing

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The Journal of Photographic Science 2018年第2期66卷 69-83页

ABSTRACTABSTRACTText mining has become a major research topic in which text classification is the important task for finding the relevant information from the new document. Accordingly, this paper presents a semantic word processing technique for text categorization that utilizes semantic keywords, instead of using independent features of the keywords in the documents. Hence, the dimensionality of the search space can be reduced. Here, the Back Propagation lion algorithm (BP lion algorithm) is also proposed to overcome the problem in updating the neuron weight. The proposed text classification methodology is experimented over two data sets, namely, 20 Newsgroup and Reuter. The performance of the proposed BPlion is analysed, in terms of sensitivity, specificity, and accuracy, and compared with the performance of the existing works. The result shows that the proposed BPlion algorithm and semantic processing methodology classifies the documents with less training time and more classification accuracy of 90.9%.

关键词： Text classification back propagation algorithm lion optimization algorithm semantic words hyponymy

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Deer hunting optimization technique for clustering unsupervised data in data mining

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INTERNATIONAL JOURNAL OF MODELING SIMULATION AND SCIENTIFIC COMPUTING 2023年第1期14卷

作者： Azeez, Hayder Hussein Southern Tech Univ Basra Iraq

Unsupervised data clustering investigation is a standout among the most valuable tools and is an informative task in data mining that looks to characterize similar articles' gatherings. One of the eminent algorithms for the clustering field is K-means clustering. Scholars recommended enhancing the nature of K-means, and optimization algorithms were hybridized. In this study, a heuristic calculation, deer hunting optimization algorithm (DHOA), was adjusted for K-means data clustering by altering the fundamental parameters of DHOA calculation, which are propelled from the characteristic enlivened calculations. During this work, a new human-based descriptive DHOA has been developed following a human deer hunting strategy. In order to attack the fawn, hunters update their positions based on the movement of the leader and backward movement while also considering the angle of the deer. In this work, the DHOA was hybridized with K-means clustering and the performance of the proposed approach is tested against UCI repository data with different algorithms.

关键词： Clustering K-means deer hunting optimization lion optimization algorithm cuckoo search optimization

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