scholarly journals Radial Basis Kernel Regressive Feature Extraction and Robert Ensembled Brown Boost Classifier for Attack Detection in Cloud Environment

Webology ◽  
2021 ◽  
Vol 18 (2) ◽  
pp. 41-59
Author(s):  
K. Padmaja ◽  
K. Padmaja
Keyword(s):  
Cloud Computing ◽  
Error Rate ◽  
Data Communication ◽  
Attack Detection ◽  
Detection Accuracy ◽  
Communication Performance ◽  
Time Consumption ◽  
Radial Basis ◽  
Input Variables ◽  
Regressive Function

Cloud computing shares the resource in information technology field. The existing technique is failed to provide better results for identifying unknown attacks with higher accuracy and lesser time consumption. In order to address these problems, Radial Basis Kernel Regressive Feature Extracted Brown Boost Classification (RBKRFEBBC) method is introduced for performing the attack detection in cloud computing. The main objective of RBKRFEBBC method is to improve the attack detection performance with higher accuracy and minimal time consumption. Dichotomous radial basis kernelized regressive function is used in RBKRFEBBC method to extract the relevant features through determining the correlation between the output and one or more input variables (i.e., features of patient transaction data). After extracting relevant features, GRNBBC algorithm is used in RBKRFEBBC method to improve the secured data communication performance through classifying the patient data transaction as attack presence or attack absence. By this way, attack detection is carried out in accurate manner. Experimental evaluation is carried out by NSL-KDD dataset using different metrics like attack detection accuracy, attack detection time and error rate. The evaluation result shows RBKRFEBBC method improves the accuracy and minimizes the time consumption as well as error rate than existing works.

Design, Development and Practical Realization of a VLC Supportive Indoor Lighting System

2020 ◽  
pp. 87-97
Author(s):  
Sourish Chatterjee ◽  
Biswanath Roy
Keyword(s):  
Data Communication ◽  
Cost Effective ◽  
Ambient Light ◽  
Lighting System ◽  
Design Development ◽  
Led Driver ◽  
Communication Performance ◽  
Good Communication ◽  
Simulink Model ◽  
Light Interference

In an office space, an LED-based lighting system allows you to perform the function of a data transmitter. This article discusses the cost-effective design and development of a data-enabled LED driver that can transmit data along with its receiving part. In addition, this paper clearly outlines the application of the proposed VLC system in an office environment where ambient light interference is a severe issue of concern. The result shows satisfactory lighting characteristics in general for this area in terms of average horizontal illuminance and illuminance uniformity. At the same time, to evaluate real-time and static communication performance, Arduino interfaced MATLAB Simulink model is developed, which shows good communication performance in terms of BER (10–7) even in presence of ambient light noise with 6 dB signal to interference plus noise ratio. Our designed system is also flexible to work as a standalone lighting system, whenever data communication is not required.

An approach to ensure joint illumination & communication performance of a forward error corrected indoor visible light communication (VLC) system in presence of ambient light interference

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Sourish Chatterjee ◽  
Biswanath Roy
Keyword(s):  
Visible Light ◽  
Bit Error Rate ◽  
Error Rate ◽  
Light Emitting Diode ◽  
Visible Light Communication ◽  
Ambient Light ◽  
Ambient Illumination ◽  
Communication Performance ◽  
Light Interference ◽  
Forward Error

AbstractIn recent time of looming radio frequency (RF) spectrum crisis, visible light communication using lighting infrastructure emerged as a potential alternative at an indoor environment. This paper addresses the setback associated with ambient light interference in an indoor Visible Light Communication (VLC) system to ensure joint communication and illumination performance inside an office room. A novel VLC architecture with suitable white light emitting diode (WLED) luminaire arrangement is presented to minimize the dispersion of signal to interference plus noise ratio (SINR) across the room. Luminaires are categorized in two groups viz. data transmitting illuminants and illuminants for lighting purpose. The first group is dedicated to transmit data as well as serves the purpose of illumination. The other set creates only ambient illumination to achieve quality lighting attributes. The proposed forward error corrected receiver configuration discards the ambient light noise originated by the illuminants that serve the ambient illumination. Tail biting convolutional encoder and viterbi decoder are used at the encoding section of the transmitter and decoding section of the receiver respectively to improve bit error rate. Results obtained through MATLAB simulation shows better average bit error rate (BER) in the order of 10−8 measured at uniformly distributed 25 grid points over the working plane. At the same time achieved average horizontal illuminance with good uniformity comply with ISO recommendation.

scholarly journals SOFT COMPUTING BASED AUTONOMOUS LOW RATE DDOS ATTACK DETECTION AND SECURITY FOR CLOUD COMPUTING

2019 ◽  
Vol 2019 (2) ◽  
pp. 80-90 ◽  
Author(s):  
Mugunthan S. R.
Keyword(s):  
Cloud Computing ◽  
Soft Computing ◽  
High Speed ◽  
Denial Of Service ◽  
Attack Detection ◽  
Ddos Attacks ◽  
Counter Measures ◽  
Cloud Architecture ◽  
Ddos Attack Detection ◽  
Low Rate

The fundamental advantage of the cloud environment is its instant scalability in rendering the service according to the various demands. The recent technological growth in the cloud computing makes it accessible to people from everywhere at any time. Multitudes of user utilizes the cloud platform for their various needs and store their complete details that are personnel as well as confidential in the cloud architecture. The storage of the confidential information makes the cloud architecture attractive to its hackers, who aim in misusing the confidential/secret information’s. The misuse of the services and the resources of the cloud architecture has become a common issue in the day to day usage due to the DDOS (distributed denial of service) attacks. The DDOS attacks are highly mature and continue to grow at a high speed making the detecting and the counter measures a challenging task. So the paper uses the soft computing based autonomous detection for the Low rate-DDOS attacks in the cloud architecture. The proposed method utilizes the hidden Markov Model for observing the flow in the network and the Random forest in classifying the detected attacks from the normal flow. The proffered method is evaluated to measure the performance improvement attained in terms of the Recall, Precision, specificity, accuracy and F-measure.

scholarly journals Impact Analysis of Interference Sources on Data Communication System for CBTC Application

2012 ◽  
Vol 6-7 ◽  
pp. 356-360
Author(s):  
Shao Yin Wang ◽  
Yi Yu ◽  
Guo Xin Zheng ◽  
Qing Feng Ding
Keyword(s):  
Communication System ◽  
Impact Analysis ◽  
Data Communication ◽  
Access Point ◽  
Outdoor Environment ◽  
Test Bed ◽  
Communication Performance ◽  
Train Control ◽  
Data Throughput ◽  
Lab Test

We study the anti-interference performance of the 802.11 system when it works as Data Communication System (DCS) in Communication Based Train Control (CBTC). We first conduct extensive experiments on a 802.11b network to assess the ability on a lab test bed, then the outdoor experiments are also conducted. In the presence of jammer, we find that in each case of interference model, there exists a C/I threshold which determine the DCS-Access Point (DCS-AP) and DCS-Station Adapter (DCS-STA) communication performance. In the outdoor environment, different interference sources are adopted to investigate the data throughput value and other parameters of the DCS system under the critical state.

Attack Detection in Cloud Networks Based on Artificial Intelligence Approaches

2019 ◽  
pp. 63-84
Author(s):  
Zuleyha Yiner ◽  
Nurefsan Sertbas ◽  
Safak Durukan-Odabasi ◽  
Derya Yiltas-Kaplan
Keyword(s):  
Cloud Computing ◽  
Deep Learning ◽  
Learning Algorithms ◽  
General Information ◽  
Attack Detection ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Network Access ◽  
Cloud Infrastructure ◽  
Security Issues

Cloud computing that aims to provide convenient, on-demand, network access to shared software and hardware resources has security as the greatest challenge. Data security is the main security concern followed by intrusion detection and prevention in cloud infrastructure. In this chapter, general information about cloud computing and its security issues are discussed. In order to prevent or avoid many attacks, a number of machine learning algorithms approaches are proposed. However, these approaches do not provide efficient results for identifying unknown types of attacks. Deep learning enables to learning features that are more complex, and thanks to the collection of big data as a training data, deep learning achieves more successful results. Many deep learning algorithms are proposed for attack detection. Deep networks architecture is divided into two categories, and descriptions for each architecture and its related attack detection studies are discussed in the following section of chapter.

Cyber Security Aspects of Virtualization in Cloud Computing Environments

2021 ◽  
pp. 1658-1671
Author(s):  
Darshan Mansukhbhai Tank ◽  
Akshai Aggarwal ◽  
Nirbhay Kumar Chaubey
Keyword(s):  
Cloud Computing ◽  
Cyber Security ◽  
Attack Detection ◽  
Cyber Attacks ◽  
Point Of View ◽  
Cyber Attack ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Detection Approach ◽  
Detection Response

Cybercrime continues to emerge, with new threats surfacing every year. Every business, regardless of its size, is a potential target of cyber-attack. Cybersecurity in today's connected world is a key component of any establishment. Amidst known security threats in a virtualization environment, side-channel attacks (SCA) target most impressionable data and computations. SCA is flattering major security interests that need to be inspected from a new point of view. As a part of cybersecurity aspects, secured implementation of virtualization infrastructure is very much essential to ensure the overall security of the cloud computing environment. We require the most effective tools for threat detection, response, and reporting to safeguard business and customers from cyber-attacks. The objective of this chapter is to explore virtualization aspects of cybersecurity threats and solutions in the cloud computing environment. The authors also discuss the design of their novel ‘Flush+Flush' cache attack detection approach in a virtualized environment.

scholarly journals Semi-supervised machine learning for primary user emulation attack detection and prevention through core-based analytics for cognitive radio networks

2019 ◽  
Vol 15 (9) ◽  
pp. 155014771986036 ◽  
Author(s):  
Sundar Srinivasan ◽  
KB Shivakumar ◽  
Muazzam Mohammad
Keyword(s):  
Machine Learning ◽  
Cognitive Radio ◽  
Cognitive Radio Networks ◽  
Primary User ◽  
Radio Networks ◽  
Attack Detection ◽  
Supervised Machine Learning ◽  
Machine Language ◽  
Detection Accuracy ◽  
Primary User Emulation

Cognitive radio networks are software controlled radios with the ability to allocate and reallocate spectrum depending upon the demand. Although they promise an extremely optimal use of the spectrum, they also bring in the challenges of misuse and attacks. Selfish attacks among other attacks are the most challenging, in which a secondary user or an unauthorized user with unlicensed spectrum pretends to be a primary user by altering the signal characteristics. Proposed methods leverage advancement to efficiently detect and prevent primary user emulation future attack in cognitive radio using machine language techniques. In this paper novel method is proposed to leverage unique methodology which can efficiently handle during various dynamic changes includes varying bandwidth, signature changes etc… performing learning and classification at edge nodes followed by core nodes using deep learning convolution network. The proposed method is compared with that of two other state-of-art machine learning-based attack detection protocols and has found to significantly reduce the false alarm to secondary network, at the same time improve the overall detection accuracy at the primary network.

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