Data Mining Practical Machine Learning Tools and Techniques

Distributed denial of service (DDoS) attacks in cloud computing environments are growing due to the essential characteristics of cloud computing. With recent advances in software-defined networking (SDN), SDN-based cloud brings us new chances to defeat DDoS attacks in cloud computing environments. Nevertheless, there is a contradictory relationship between SDN and DDoS attacks. On one hand, the capabilities of SDN, including software-based traffic analysis, centralized control, global view of the network, dynamic updating of forwarding rules, make it easier to detect and react to DDoS attacks. On the other hand, the security of SDN itself remains to be addressed, and potential DDoS vulnerabilities exist across SDN platforms. In this paper, we discuss the new trends and characteristics of DDoS attacks in cloud computing, and provide a comprehensive survey of defense mechanisms against DDoS attacks using SDN. In addition, we review the studies about launching DDoS attacks on SDN, as well as the methods against DDoS attacks in SDN. To the best of our knowledge, the contradictory relationship between SDN and DDoS attacks has not been well addressed in previous works. This work can help to understand how to make full use of SDN's advantages to defeat DDoS attacks in cloud computing environments and how to prevent SDN itself from becoming a victim of DDoS attacks, which are important for the smooth evolution of SDN-based cloud without the distraction of DDoS attacks.

Software-Defined Networking (SDN) and Distributed Denial of Service (DDoS) Attacks in Cloud Computing Environments: A Survey, Some Research Issues, and Challenges

Wireless Sensor Networks (WSNs) consist of a large number of spatially distributed sensor nodes connected through the wireless medium to monitor and record the physical information from the environment. The nodes of WSN are battery powered, so after a certain period it loose entire energy. This energy constraint affects the lifetime of the network. The objective of this study is to minimize the overall energy consumption and to maximize the network lifetime. At present, clustering and routing algorithms are widely used in WSNs to enhance the network lifetime. In this study, the Butterfly Optimization Algorithm (BOA) is employed to choose an optimal cluster head from a group of nodes. The cluster head selection is optimized by the residual energy of the nodes, distance to the neighbors, distance to the base station, node degree and node centrality. The route between the cluster head and the base station is identified by using Ant Colony Optimization (ACO), it selects the optimal route based on the distance, residual energy and node degree. The performance measures of this proposed methodology are analyzed in terms of alive nodes, dead nodes, energy consumption and data packets received by the BS. The outputs of the proposed methodology are compared with traditional approaches LEACH, DEEC and compared with some existing methods FUCHAR, CRHS, BERA, CPSO, ALOC and FLION. For example, the alive nodes of the proposed methodology are 200 at 1500 iterations which is higher compared to the CRHS and BERA methods.

Energy efficient cluster based routing protocol for WSN using butterfly optimization algorithm and ant colony optimization

Energy efficiency has been a hot research topic for many years and many routing algorithms have been proposed to improve energy efficiency and to prolong lifetime for wireless sensor networks (WSNs). Since nodes close to the sink usually need to consume more energy to forward data of its neighbours to sink, they will exhaust energy more quickly. These nodes are called hot spot nodes and we call this phenomenon hot spot problem. In this paper, an Enhanced Power Efficient Gathering in Sensor Information Systems (EPEGASIS) algorithm is proposed to alleviate the hot spots problem from four aspects. Firstly, optimal communication distance is determined to reduce the energy consumption during transmission. Then threshold value is set to protect the dying nodes and mobile sink technology is used to balance the energy consumption among nodes. Next, the node can adjust its communication range according to its distance to the sink node. Finally, extensive experiments have been performed to show that our proposed EPEGASIS performs better in terms of lifetime, energy consumption, and network latency.

/pdf/an-enhanced-pegasis-algorithm-with-mobile-sink-support-for-d7kbynacqo.pdf

An Enhanced PEGASIS Algorithm with Mobile Sink Support for Wireless Sensor Networks

In recent years, the emergence of blockchain technology (BT) has become a unique, most disruptive, and trending technology. The decentralized database in BT emphasizes data security and privacy. Also, the consensus mechanism in it makes sure that data is secured and legitimate. Still, it raises new security issues such as majority attack and double-spending. To handle the aforementioned issues, data analytics is required on blockchain based secure data. Analytics on these data raises the importance of arisen technology Machine Learning (ML). ML involves the rational amount of data to make precise decisions. Data reliability and its sharing are very crucial in ML to improve the accuracy of results. The combination of these two technologies (ML and BT) can provide highly precise results. In this paper, we present a detailed study on ML adoption for making BT-based smart applications more resilient against attacks. There are various traditional ML techniques, for instance, Support Vector Machines (SVM), clustering, bagging, and Deep Learning (DL) algorithms such as Convolutional Neural Network (CNN) and Long short-term memory (LSTM) can be used to analyse the attacks on a blockchain-based network. Further, we include how both the technologies can be applied in several smart applications such as Unmanned Aerial Vehicle (UAV), Smart Grid (SG), healthcare, and smart cities. Then, future research issues and challenges are explored. At last, a case study is presented with a conclusion.

/pdf/machine-learning-adoption-in-blockchain-based-smart-4oe5d1a6s0.pdf

Machine Learning Adoption in Blockchain-Based Smart Applications: The Challenges, and a Way Forward

Machine Learning (ML) is a kind of Artificial Intelligence (AI) technique which allows the system to obtain knowledge with no explicit programming. The main intention of ML technique is to enable the computers to learn with no human assistance. ML is mainly divided into three categories namely supervised, unsupervised and semi-supervised learning approaches. Supervised algorithms need humans to give input and required output, in addition to providing feedback about the prediction accuracy in the training process. Unsupervised learning approaches are contrast to supervised learning approaches where it does not require any training process. But, supervised learning approaches are simpler than unsupervised learning approaches. This paper reviews the supervised learning approaches which are widely used in data classification process. The techniques are reviewed on the basis of aim, methodology, advantages and disadvantages. Finally, the readers can get an overview of supervised ML approaches in terms of data classification.

A State of Art Techniques on Machine Learning Algorithms: A Perspective of Supervised Learning Approaches in Data Classification

Wireless Sensor Networks (WSN) became a key technology for a ubiquitous living and remains an active research due to the wide range of applications. The design of energy efficient WSN is still a greater research challenge. Clustering techniques have been widely used to reduce the energy consumption and prolong the network lifetime. This paper introduces an algorithm named Fuzzy logic based Unequal clustering, and Ant Colony Optimization (ACO) based Routing, Hybrid protocol for WSN to eliminate hot spot problem and extend the network lifetime. This protocol comprises of Cluster Head (CH) selection, inter-cluster routing and cluster maintenance. Fuzzy logic selects CHs efficiently and divides the network into unequal clusters based on residual energy, distance to Base Station (BS), distance to its neighbors, node degree and node centrality. It uses ACO based routing technique for efficient and reliable inter-cluster routing from CHs to BS. Moreover, this protocol transmits data in a hybrid manner, i.e. both proactive and reactive manner. A threshold concept is employed to transmit/intimate sudden changes in the environment in addition to periodic data transmission. For proper load balancing, a new routing strategy is also employed where threshold based data transmission takes place in shortest path and the periodic data transmission takes place in unused paths. Cross-layer cluster maintenance phase is also used for uniform load distribution. The proposed method is intensively experimented and compared with existing protocols namely LEACH, TEEN, DEEC and EAUCF. The simulation results show that the proposed method attains maximum lifetime, eliminates hot spot problem and balances the energy consumption among all nodes efficiently.

Lifetime maximization of wireless sensor network using fuzzy based unequal clustering and ACO based routing hybrid protocol

Optimal DG unit placement in distribution networks by multi-objective whale optimization algorithm & its techno-economic analysis

Objectives: This paper provides the performance analysis of three edge detection operators on the basis of intensity value and high gradient. Methods: Edge detection is considered to be the building blocks of image processing for object detection and it is an important technique in image segmentation. Sobel, Prewitt and Roberts are some of the edge detection operators discussed in this paper. MATLAB R2013a is used to analyze the performances of these operators using an input image. Findings: In order to classify the operators, the limitations are identified and the performance analysis is done on the basis of obtained results. Based on the intensity value, Prewitt produces better result than Sobel and Roberts. At the same time, Sobel locates the edges with high gradient. Likewise, Roberts produces quick result due to small filter. Application/Improvement: The performance of these operators has been analyzed and realized that each operator is recognized as the best under various conditions.

Performance Analysis of Different Edge Detection Techniques for Image Segmentation

Biogeography Based Optimization Algorithm and Neural Network to Optimize Place and Size of Distributed Generating System in Electrical Distribution

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