scholarly journals Intrusion Detection on Apache Spark Platform in Big data and Machine Learning Techniques

2021 ◽  
Vol 23 (06) ◽  
pp. 1257-1266
Author(s):  
Mrs.J. Yamuna Bee ◽  
◽  
E. Naveena ◽  
Reshma Elizabeth Thomas ◽  
Arathi Chandran ◽  
...  
Keyword(s):  
Power Systems ◽  
Cyber Attacks ◽  
Machine Learning Techniques ◽  
Frequency Regulation ◽  
Cyber Attack ◽  
Learning Techniques ◽  
Control Scheme ◽  
Risk Review ◽  
The Impact

With the rising cyber-physical power systems and emerging danger of cyber-attacks, the traditional power services are faced with higher risks of being compromised, as vulnerabilities in cyber communications can be broken to cause material damage. Therefore, adjustment needs to be made in the present control scheme plan methods to moderate the impact of possible attacks on service quality. This paper, focuses on the service of synchronized source-load contribution in main frequency regulation, a weakness study is performed with model the attack intrusion process, and the risk review of the service is made by further model the attack impacts on the service’s bodily things. On that basis, the customary synchronized reserve allotment optimization model is adapted and the allocation scheme is correct according to the cyber-attack impact. The proposed alteration methods are validating through a case study, showing efficiency in defensive alongside the cyber-attack impacts.

A Study of Machine Learning Techniques in Cryptography for Cybersecurity

2021 ◽  
Vol 1 (4) ◽  
pp. 22-26
Author(s):  
Ankita Saha ◽  
Chanda Pathak ◽  
Sourav Saha
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Network Architecture ◽  
Cyber Attacks ◽  
Machine Learning Techniques ◽  
Cyber Attack ◽  
Sensitive Data ◽  
Security Controls ◽  
Learning Techniques ◽  
Scope Of Application

The importance of cybersecurity is on the rise as we have become more technologically dependent on the internet than ever before. Cybersecurity implies the process of protecting and recovering computer systems, networks, devices, and programs from any cyber attack. Cyber attacks are an increasingly sophisticated and evolving danger to our sensitive data, as attackers employ new methods to circumvent traditional security controls. Cryptanalysis is mainly used to crack cryptographic security systems and gain access to the contents of the encrypted messages, even if the key is unknown. It focuses on deciphering the encrypted data as it works with ciphertext, ciphers, and cryptosystems to understand how they work and find techniques for weakening them. For classical cryptanalysis, the recovery of ciphertext is difficult as the time complexity is exponential. The traditional cryptanalysis requires a significant amount of time, known plaintexts, and memory. Machine learning may reduce the computational complexity in cryptanalysis. Machine learning techniques have recently been applied in cryptanalysis, steganography, and other data-securityrelated applications. Deep learning is an advanced field of machine learning which mainly uses deep neural network architecture. Nowadays, deep learning techniques are usually explored extensively to solve many challenging problems of artificial intelligence. But not much work has been done on deep learning-based cryptanalysis. This paper attempts to summarize various machine learning based approaches for cryptanalysis along with discussions on the scope of application of deep learning techniques in cryptography.

scholarly journals Advances in the Application of Machine Learning Techniques for Power System Analytics: A Survey

Energies ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 4776
Author(s):  
Seyed Mahdi Miraftabzadeh ◽  
Michela Longo ◽  
Federica Foiadelli ◽  
Marco Pasetti ◽  
Raul Igual
Keyword(s):  
Machine Learning ◽  
Power Systems ◽  
Smart Grids ◽  
State Of The Art ◽  
Smart Cities ◽  
Power Grids ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
New Research ◽  
Traditional Approaches

The recent advances in computing technologies and the increasing availability of large amounts of data in smart grids and smart cities are generating new research opportunities in the application of Machine Learning (ML) for improving the observability and efficiency of modern power grids. However, as the number and diversity of ML techniques increase, questions arise about their performance and applicability, and on the most suitable ML method depending on the specific application. Trying to answer these questions, this manuscript presents a systematic review of the state-of-the-art studies implementing ML techniques in the context of power systems, with a specific focus on the analysis of power flows, power quality, photovoltaic systems, intelligent transportation, and load forecasting. The survey investigates, for each of the selected topics, the most recent and promising ML techniques proposed by the literature, by highlighting their main characteristics and relevant results. The review revealed that, when compared to traditional approaches, ML algorithms can handle massive quantities of data with high dimensionality, by allowing the identification of hidden characteristics of (even) complex systems. In particular, even though very different techniques can be used for each application, hybrid models generally show better performances when compared to single ML-based models.

Flood-prone area mapping using machine learning techniques: a case study of Quang Binh province, Vietnam

2021 ◽  
Author(s):  
Chinh Luu ◽  
Quynh Duy Bui ◽  
Romulus Costache ◽  
Luan Thanh Nguyen ◽  
Thu Thuy Nguyen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Prone Area ◽  
Flood Prone Area

Machine Learning Assisted Interpretation: Integrated Fault Prediction Extraction Case Study from the Groningen Gas Field, Netherlands

2021 ◽  
pp. 1-67
Author(s):  
Stewart Smith ◽  
Olesya Zimina ◽  
Surender Manral ◽  
Michael Nickel
Keyword(s):  
Machine Learning ◽  
Fault Detection ◽  
Seismic Data ◽  
Fault Prediction ◽  
Machine Learning Techniques ◽  
Gas Field ◽  
Seismic Fault ◽  
Learning Techniques ◽  
Groningen Gas Field

Seismic fault detection using machine learning techniques, in particular the convolution neural network (CNN), is becoming a widely accepted practice in the field of seismic interpretation. Machine learning algorithms are trained to mimic the capabilities of an experienced interpreter by recognizing patterns within seismic data and classifying them. Regardless of the method of seismic fault detection, interpretation or extraction of 3D fault representations from edge evidence or fault probability volumes is routine. Extracted fault representations are important to the understanding of the subsurface geology and are a critical input to upstream workflows including structural framework definition, static reservoir and petroleum system modeling, and well planning and de-risking activities. Efforts to automate the detection and extraction of geological features from seismic data have evolved in line with advances in computer algorithms, hardware, and machine learning techniques. We have developed an assisted fault interpretation workflow for seismic fault detection and extraction, demonstrated through a case study from the Groningen gas field of the Upper Permian, Dutch Rotliegend; a heavily faulted, subsalt gas field located onshore, NE Netherlands. Supervised using interpreter-led labeling, we apply a 2D multi-CNN to detect faults within a 3D pre-stack depth migrated seismic dataset. After prediction, we apply a geometric evaluation of predicted faults, using a principal component analysis (PCA) to produce geometric attribute representations (strike azimuth and planarity) of the fault prediction. Strike azimuth and planarity attributes are used to validate and automatically extract consistent 3D fault geometries, providing geological context to the interpreter and input to dependent workflows more efficiently.

scholarly journals Leveraging Business Data Analytics and Machine Learning Techniques for Competitive Advantage: Case Study Evidence from Small Businesses

2021 ◽  
Vol 2 (1) ◽  
pp. 73-87
Author(s):  
Rathimala Kannan ◽  
Intan Soraya Rosdi ◽  
Kannan Ramakrishna ◽  
Haziq Riza Abdul Rasid ◽  
Mohamed Haryz Izzudin Mohamed Rafy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Competitive Advantage ◽  
Small Businesses ◽  
Data Analytics ◽  
Research Question ◽  
Machine Learning Techniques ◽  
Multiple Sources ◽  
Learning Techniques ◽  
Business Data

Data analytics is the essential component in deriving insights from data obtained from multiple sources. It represents the technology, methods and techniques used to obtain insights from massive datasets. As data increases, companies are looking for ways to gain relevant business insights underneath layers of data and information, to help them better understand new business ventures, opportunities, business trends and complex challenges. However, to date, while the extensive benefits of business data analytics to large organizations are widely published, micro, small, and medium sized organisations have not fully grasped the potential benefits to be gained from data analytics using machine learning techniques. This study is guided by the research question of how data analytics using machine learning techniques can benefit small businesses. Using the case study method, this paper outlines how small businesses in two different industries i.e. healthcare and retail can leverage data analytics and machine learning techniques to gain competitive advantage from the data. Details on the respective benefits gained by the small business owners featured in the two case studies provide important answers to the research question.

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