Grey wolf optimizer (GWO) is one of recent metaheuristics swarm intelligence methods. It has been widely tailored for a wide variety of optimization problems due to its impressive characteristics over other swarm intelligence methods: it has very few parameters, and no derivation information is required in the initial search. Also it is simple, easy to use, flexible, scalable, and has a special capability to strike the right balance between the exploration and exploitation during the search which leads to favourable convergence. Therefore, the GWO has recently gained a very big research interest with tremendous audiences from several domains in a very short time. Thus, in this review paper, several research publications using GWO have been overviewed and summarized. Initially, an introductory information about GWO is provided which illustrates the natural foundation context and its related optimization conceptual framework. The main operations of GWO are procedurally discussed, and the theoretical foundation is described. Furthermore, the recent versions of GWO are discussed in detail which are categorized into modified, hybridized and paralleled versions. The main applications of GWO are also thoroughly described. The applications belong to the domains of global optimization, power engineering, bioinformatics, environmental applications, machine learning, networking and image processing, etc. The open source software of GWO is also provided. The review paper is ended by providing a summary conclusion of the main foundation of GWO and suggests several possible future directions that can be further investigated.

Grey wolf optimizer: a review of recent variants and applications

Network Simulator NS2

Whale Optimization Algorithm (WOA) is an optimization algorithm developed by Mirjalili and Lewis in 2016. An overview of WOA is described in this paper, rooted from the bubble-net hunting strategy, besides an overview of WOA applications that are used to solve optimization problems in various categories. The best solution has been determined to make something as functional and effective as possible through the optimization process by minimizing or maximizing the parameters involved in the problems. Research and engineering attention have been paid to Meta-heuristics for purposes of decision-making given the growing complexity of models and the needs for quick decision making in the engineering. An updated review of research of WOA is provided in this paper for hybridization, improved, and variants. The categories included in the reviews are Engineering, Clustering, Classification, Robot Path, Image Processing, Networks, Task Scheduling, and other engineering applications. According to the reviewed literature, WOA is mostly used in the engineering area to solve optimization problems. Providing an overview and summarizing the review of WOA applications are the aims of this paper.

A comprehensive survey: Whale Optimization Algorithm and its applications

This study presents a robust and semi-blind watermarking by embedding information into low frequency AC coefficients of Discrete Cosine Transform (DCT). Since the imperceptibility is the most significant issue in watermarking, the DC value is maintained unchanged. The proposed methods utilize the DC values of the neighboring blocks to predict the AC coefficients of the center block. The low frequency AC coefficients are modified to carry watermark information. The Least Mean Squares (LMS) is employed to yield the intermediate filters, cooperating with the neighboring DC coefficients to precisely predict the original AC coefficients. Two watermarking methods, namely Watermark Embedding in the Predicted AC Coefficient (WEPAC) and Watermark Embedding in the Original AC coefficient (WEOAC), are presented in this study. Moreover, many attacks are addressed to show the robustness of the proposed methods.

International Conference on Intelligent Information Hiding and Multimedia Signal Processing

This paper presents an optimized watermarking scheme based on the discrete wavelet transform (DWT) and singular value decomposition (SVD). The singular values of a binary watermark are embedded in singular values of the LL3 sub-band coefficients of the host image by making use of multiple scaling factors (MSFs). The MSFs are optimized using a newly proposed Firefly Algorithm having an objective function which is a linear combination of imperceptibility and robustness. The PSNR values indicate that the visual quality of the signed and attacked images is good. The embedding algorithm is robust against common image processing operations. It is concluded that the embedding and extraction of the proposed algorithm is well optimized, robust and show an improvement over other similar reported methods.

Optimized gray-scale image watermarking using DWT-SVD and Firefly Algorithm

https://ir.nctu.edu.tw:443/bitstream/11536/26983/1/000188536200014.pdf

Genetic watermarking based on transform-domain techniques

An optimized scheme for watermarking based on zerotrees is proposed in this paper. Digital watermarking is an effective means for protecting copyrights with three of the most fundamental requirements: watermark imperceptibility, watermark robustness, and watermark capacity. Conventional techniques in the literature mainly perform watermark embedding and extraction processes in the transform domain, including the discrete Fourier transform, discrete cosine transform, and discrete wavelet transform domains. The three watermarking requirements above are in conflict with each other; therefore, finding a way to obtain a trade-off among them is the major purpose of this paper. We first perform watermarking in the wavelet domain. Next, we properly select zerotrees in a wavelet transform with the genetic algorithm. Our simulation results not only demonstrate better performances of the watermarked images after optimization, but also reveal the robustness of the extracted watermarks under common attacks.

Genetic Watermarking for Zerotree-Based Applications

The recent advancements in the wireless sensor networks (WSNs) are mainly motivated by developing in the micro electromechanical systems technology. One of the major issues in WSNs is to develop an energy efficient routing protocol which has a significant impact on the overall lifetime of sensor networks. In this paper, a novel scheme of an energy-based cluster head selection algorithm (ECHA) to support long-lifetime in WSNs is proposed to improve the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol. In the proposed method, the number of the selection cluster heads (CHs) is optimized based on the effect of the distances from the member nodes to the CH, from the CHs to the base station (BS) and the high energy of each CHs. The simulation results show that the proposed scheme outperforms LEACH and LEACH-C by 35% and 5% in saving power consumption for prolonging network lifetime respectively. The impacts of parameters of density of nodes and weighting probability are verified to find out the best cases for ECHA applications.

An Energy-based Cluster Head Selection Algorithm to Support Long-lifetime in Wireless Sensor Networks.

DDoS (Distributed Denial of Service) attacks have become a pressing threat to the security and integrity of computer networks and information systems, which are indispensable infrastructures of modern times. The detection of DDoS attacks is a challenging issue before any mitigation measures can be taken. ML/DL (Machine Learning/Deep Learning) has been applied to the detection of DDoS attacks with satisfactory achievement. However, full-scale success is still beyond reach due to an inherent problem with ML/DL-based systems—the so-called Open Set Recognition (OSR) problem. This is a problem where an ML/DL-based system fails to deal with new instances not drawn from the distribution model of the training data. This problem is particularly profound in detecting DDoS attacks since DDoS attacks’ technology keeps evolving and has changing traffic characteristics. This study investigates the impact of the OSR problem on the detection of DDoS attacks. In response to this problem, we propose a new DDoS detection framework featuring Bi-Directional Long Short-Term Memory (BI-LSTM), a Gaussian Mixture Model (GMM), and incremental learning. Unknown traffic captured by the GMM are subject to discrimination and labeling by traffic engineers, and then fed back to the framework as additional training samples. Using the data sets CIC-IDS2017 and CIC-DDoS2019 for training, testing, and evaluation, experiment results show that the proposed BI-LSTM-GMM can achieve recall, precision, and accuracy up to 94%. Experiments reveal that the proposed framework can be a promising solution to the detection of unknown DDoS attacks.

Detection of Unknown DDoS Attacks with Deep Learning and Gaussian Mixture Model

A new algorithm for embedding several watermarks into the same original source is proposed in this paper. Instead of conventional watermark embedding algorithms that embed one watermark in one specific domain, we propose algorithms for embedding multiple watermarks into different compression domains to protect the ownership of the original images. The proposed schemes for embedding and extraction in different domains vary from the existing methods reported in the literature. Simulation results show their robustness for surviving intentional attacks and, hence, their effectiveness in protecting copyrights. They are also easy to implement.

Chin-Shiuh Shieh

Papers

Genetic watermarking based on transform-domain techniques

Genetic Watermarking for Zerotree-Based Applications

An Energy-based Cluster Head Selection Algorithm to Support Long-lifetime in Wireless Sensor Networks.

Detection of Unknown DDoS Attacks with Deep Learning and Gaussian Mixture Model

An embedding algorithm for multiple watermarks