Big Data Analysis-based Security Situational Awareness for Smart Grid

Advanced communications and data processing technologies bring great benefits to the smart grid. However, cyber-security threats also extend from the information system to the smart grid. The existing security works for smart grid focus on traditional protection and detection methods. However, a lot of threats occur in a very short time and overlooked by exiting security components. These threats usually have huge impacts on smart gird and disturb its normal operation. Moreover, it is too late to take action to defend against the threats once they are detected, and damages could be difficult to repair. To address this issue, this paper proposes a security situational awareness mechanism based on the analysis of big data in the smart grid. Fuzzy cluster based analytical method, game theory and reinforcement learning are integrated seamlessly to perform the security situational analysis for the smart grid. The simulation and experimental results show the advantages of our scheme in terms of high efficiency and low error rate for security situational awareness

Risk assessment of blasting operations in open pit mines using FAHP method

Purpose. In the mining blasting operation, fragmentation is the most important output. Fly rock, ground vibration, air blast, and environmental effects are detrimental effects of blasting operations. Identifying and ranking the risk of blasting operations is considered as the most important stage in project management. Methods. In this research, the problem of identifying and ranking the factors constituting the risk in blasting operations is considered with the methodology of the Fuzzy Analytical Hierarchy Process (FAHP). Criteria and sub-criteria have been determined based on historical research studies, field studies, and expert opinions for designing a hierarchical process. Findings. Based on FAHP scores, non-control of the sub-criterion of health and safety (C3), blast operation results (C18) and knowledge, and skill and staffing (C2) with a score of 0.377, 0.334, and 0.294 respectively are the most effective sub-criterion for the creation of blasting operations risk. According to the score, the sub-criterion C18 is the most effective sub-criterion in providing the blasting operations risk. Effects and results of blasting operations (D8), with a score of 0.334 as the most effective criterion, and natural hazards (D10), with a score of 0.015, were the last priorities in the factors causing blasting operations risk. Originality. Regarding the risk rating of blasting operations, the control of the sub-criteria C3, C18, and C2, and the D8 criterion, is of particular importance in reducing the risk of blasting operations and improving project management. Practical implications. The evaluation of human resource performance and increase in the level of knowledge and skills and occupational safety and control of all outputs of blasting operations is necessary. Therefore, selecting the most important project risks and taking actions to remove them is essential for risk management.Мета. Визначення ризиків проведення вибухових робіт та їх оцінка на основі використанням нечіткого методу аналізу ієрархій (НМАІ) для покращення управління якістю проектів. Методика. В рамках даного дослідження, проблеми визначення та оцінки ризиків вибухових робіт розглядалися із застосуванням нечіткого методу аналізу ієрархій. На базі аналізу історичних даних і польового дослідження з урахуванням експертних оцінок були визначені критерії та підкритерії для побудови ієрархій. Результати. За результатами НМАІ, неконтролюючий підкритерій здоров’я та безпеки (С3), підкритерій результатів вибухових робіт (С18), знань, умінь і кадрів (С2) зі значеннями 0.377, 0.334 і 0.294 відповідно найбільш ефективні в появі ризику проведення вибухових робіт. Підкритерій С18 чинить найбільший вплив на ризик проведення вибухових робіт. Критерій результатів і наслідків вибухових робіт (D8) з найефективнішим значенням 0.334 та критерій природних катастроф (D10) зі значенням 0.015 є останніми пріоритетами серед чинників, які визначають ризик проведення вибухових робіт. Наукова новизна. Отримав доповнення та подальший розвиток науково-методичний підхід до визначення ризиків при проведенні вибухових робіт, заснований на їх ранжуванні з використанням системи виявлених критеріїв і підкритеріїв методом НМАІ. Практична значимість. Для успішного керування проектом важливо визначати найсерйозніші ризики проекту й вжити заходів щодо їх усунення. Відносно ранжирування ризиків проведення вибухових робіт управління підкритеріями C3, C18 і C2, а також критерієм D8, особливо важливо для зниження цих ризиків та покращення якості управління проектом.Цель. Определение рисков проведения взрывных работ и их оценка на основе использования нечеткого метода анализа иерархий (НМАИ) для улучшения управления качеством проектов. Методика. В рамках данного исследования, проблемы определения и оценки рисков взрывных работ рассматривались с применением нечеткого метода анализа иерархий. На базе анализа исторических данных и полевого исследования с учетом экспертных оценок были определены, критерии и подкритерии для построения иерархий. Результаты. По результатам НМАИ, неконтролирующий подкритерий здоровья и безопасности (С3), подкритерий результатов взрывных работ (С18), знаний, умений и кадров (С2) со значениями 0.377, 0.334 и 0.294 соответственно наиболее эффективны в появлении риска проведения взрывных работ. Подкритерий С18 оказывает самое большое влияние на риск проведения взрывных работ. Критерий результатов и последствий взрывных работ (D8) с самым эффективным значением 0.334 и критерий природных катастроф (D10) со значением 0.015 являются последними приоритетами среди факторов, которые определяют риск проведения взрывных работ. Научная новизна. Получил дополнение и дальнейшее развитие научно-методический подход к определению рисков при проведении взрывных работ, основанный на их ранжировании с использованием системы выявленных критериев и подкритериев методом НМАИ. Практическая значимость. Для успешного руководства проектом важно определять самые серьезные риски проекта и предпринять действия по их устранению. В отношении ранжирования рисков проведения взрывных работ управление подкритериями C3, C18 и C2, а также критерием D8, особенно важно для снижения этих рисков и улучшения руководства проектом.The authors would like to thank Mining Engineering Department, Islamic Azad University (South Tehran Branch) for supporting this research

Attributes of Big Data Analytics for Data-Driven Decision Making in Cyber-Physical Power Systems

Big data analytics is a virtually new term in power system terminology. This concept delves into the way a massive volume of data is acquired, processed, analyzed to extract insight from available data. In particular, big data analytics alludes to applications of artificial intelligence, machine learning techniques, data mining techniques, time-series forecasting methods. Decision-makers in power systems have been long plagued by incapability and weakness of classical methods in dealing with large-scale real practical cases due to the existence of thousands or millions of variables, being time-consuming, the requirement of a high computation burden, divergence of results, unjustifiable errors, and poor accuracy of the model. Big data analytics is an ongoing topic, which pinpoints how to extract insights from these large data sets. The extant article has enumerated the applications of big data analytics in future power systems through several layers from grid-scale to local-scale. Big data analytics has many applications in the areas of smart grid implementation, electricity markets, execution of collaborative operation schemes, enhancement of microgrid operation autonomy, management of electric vehicle operations in smart grids, active distribution network control, district hub system management, multi-agent energy systems, electricity theft detection, stability and security assessment by PMUs, and better exploitation of renewable energy sources. The employment of big data analytics entails some prerequisites, such as the proliferation of IoT-enabled devices, easily-accessible cloud space, blockchain, etc. This paper has comprehensively conducted an extensive review of the applications of big data analytics along with the prevailing challenges and solutions

Intelligent Security for Phishing Online using Adaptive Neuro Fuzzy Systems

Anti-phishing detection solutions employed in industry use blacklist-based approaches to achieve low false-positive rates, but blacklist approaches utilizes website URLs only. This study analyses and combines phishing emails and phishing web-forms in a single framework, which allows feature extraction and feature model construction. The outcome should classify between phishing, suspicious, legitimate and detect emerging phishing attacks accurately. The intelligent phishing security for online approach is based on machine learning techniques, using Adaptive Neuro-Fuzzy Inference System and a combination sources from which features are extracted. An experiment was performed using two-fold cross validation method to measure the system’s accuracy. The intelligent phishing security approach achieved a higher accuracy. The finding indicates that the feature model from combined sources can detect phishing websites with a higher accuracy. This paper contributes to phishing field a combined feature which sources in a single framework. The implication is that phishing attacks evolve rapidly; therefore, regular updates and being ahead of phishing strategy is the way forward

Applications in security and evasions in machine learning : a survey

In recent years, machine learning (ML) has become an important part to yield security and privacy in various applications. ML is used to address serious issues such as real-time attack detection, data leakage vulnerability assessments and many more. ML extensively supports the demanding requirements of the current scenario of security and privacy across a range of areas such as real-time decision-making, big data processing, reduced cycle time for learning, cost-efficiency and error-free processing. Therefore, in this paper, we review the state of the art approaches where ML is applicable more effectively to fulfill current real-world requirements in security. We examine different security applications' perspectives where ML models play an essential role and compare, with different possible dimensions, their accuracy results. By analyzing ML algorithms in security application it provides a blueprint for an interdisciplinary research area. Even with the use of current sophisticated technology and tools, attackers can evade the ML models by committing adversarial attacks. Therefore, requirements rise to assess the vulnerability in the ML models to cope up with the adversarial attacks at the time of development. Accordingly, as a supplement to this point, we also analyze the different types of adversarial attacks on the ML models. To give proper visualization of security properties, we have represented the threat model and defense strategies against adversarial attack methods. Moreover, we illustrate the adversarial attacks based on the attackers' knowledge about the model and addressed the point of the model at which possible attacks may be committed. Finally, we also investigate different types of properties of the adversarial attacks

Software reusability development through NFL approach for identifying security based inner relationships of affecting factors

In component based software reusability development process, the software developers have to choose the best components which are self adaptive future to overcome the functional errors, framework mismatches, violation of user level privacy issues and data leakage feasibilities. The software developers can build high quality software applications by taking the consideration of the reusable components which are more suitable to provide high level data security and privacy. This paper has proposing the neural based fuzzy framework based approach to estimate the reusable components which are directly and indirectly involve the security and privacy to improve the quality of the software system. This approach has considered the twenty effecting factors and fifty three attribute matrices. It has formed with three stages of execution scenarios. The first stage has executed with eleven effecting factors and eighteen attribute matrices for identification of supporting software reusability components, the second stage has executed with four effecting factors and thirty five attribute matrices for identification of sub-internal relationships in terms of security-privacy, and the third stage has executed with eight effecting factors and six attribute matrices for identification of sub of sub-internal relationships in terms of security risk estimation. This analytical finding proposes a fuzzy logic model to evaluate the most feasible effecting factors that influence the enterprise level data security-privacy practices at real time environment

Artificial Intelligence & Machine Learning in Finance: A literature review

In the 2020s, Artificial Intelligence (AI) has been increasingly becoming a dominant technology, and thanks to new computer technologies, Machine Learning (ML) has also experienced remarkable growth in recent years; however, Artificial Intelligence (AI) needs notable data scientist and engineers’ innovation to evolve. Hence, in this paper, we aim to infer the intellectual development of AI and ML in finance research, adopting a scoping review combined with an embedded review to pursue and scrutinize the services of these concepts. For a technical literature review, we goose-step the five stages of the scoping review methodology along with Donthu et al.’s (2021) bibliometric review method. This article highlights the trends in AI and ML applications (from 1989 to 2022) in the financial field of both developed and emerging countries. The main purpose is to emphasize the minutiae of several types of research that elucidate the employment of AI and ML in finance. The findings of our study are summarized and developed into seven fields: (1) Portfolio Management and Robo-Advisory, (2) Risk Management and Financial Distress (3), Financial Fraud Detection and Anti-money laundering, (4) Sentiment Analysis and Investor Behaviour, (5) Algorithmic Stock Market Prediction and High-frequency Trading, (6) Data Protection and Cybersecurity, (7) Big Data Analytics, Blockchain, FinTech. Further, we demonstrate in each field, how research in AI and ML enhances the current financial sector, as well as their contribution in terms of possibilities and solutions for myriad financial institutions and organizations. We conclude with a global map review of 110 documents per the seven fields of AI and ML application.   Keywords: Artificial Intelligence, Machine Learning, Finance, Scoping review, Casablanca Exchange Market. JEL Classification: C80 Paper type: Theoretical ResearchIn the 2020s, Artificial Intelligence (AI) has been increasingly becoming a dominant technology, and thanks to new computer technologies, Machine Learning (ML) has also experienced remarkable growth in recent years; however, Artificial Intelligence (AI) needs notable data scientist and engineers’ innovation to evolve. Hence, in this paper, we aim to infer the intellectual development of AI and ML in finance research, adopting a scoping review combined with an embedded review to pursue and scrutinize the services of these concepts. For a technical literature review, we goose-step the five stages of the scoping review methodology along with Donthu et al.’s (2021) bibliometric review method. This article highlights the trends in AI and ML applications (from 1989 to 2022) in the financial field of both developed and emerging countries. The main purpose is to emphasize the minutiae of several types of research that elucidate the employment of AI and ML in finance. The findings of our study are summarized and developed into seven fields: (1) Portfolio Management and Robo-Advisory, (2) Risk Management and Financial Distress (3), Financial Fraud Detection and Anti-money laundering, (4) Sentiment Analysis and Investor Behaviour, (5) Algorithmic Stock Market Prediction and High-frequency Trading, (6) Data Protection and Cybersecurity, (7) Big Data Analytics, Blockchain, FinTech. Further, we demonstrate in each field, how research in AI and ML enhances the current financial sector, as well as their contribution in terms of possibilities and solutions for myriad financial institutions and organizations. We conclude with a global map review of 110 documents per the seven fields of AI and ML application.   Keywords: Artificial Intelligence, Machine Learning, Finance, Scoping review, Casablanca Exchange Market. JEL Classification: C80 Paper type: Theoretical Researc

Mapping the Land-Use Suitability for Urban Sprawl Using Remote Sensing and GIS Under Different Scenarios

Urbanization is one of the important issues in fast developing countries, such as China, Turkey, Brazil, and South Africa. Therefore, sustainable urbanization strategies come into question while designing the cities. In this point, land-use suitability mapping for urban areas is of importance. Spatial information sciences, such as geographical information systems (GIS) and remote sensing are applied widely for mapping land-use suitability. In this study, Van City, which is the most crowded city in eastern Turkey, was evaluated by applying three different scenarios called ecological, economic, and sustainable. The multi-criteria evaluation technique was used in GIS environment in the mapping stage. Distance from roads, distance from urban boundary, hillshade, slope, elevation, land-use cover, and land-use ability factors were used as inputs in the analysis stage. The weights of each input factor were calculated according to urban change dynamics between 2002 and 2015. As a result of the study, the weighting approach using the natural change dynamics of Van City has a great potential to define objective weights. In addition, Van City was developed orderly on agricultural lands and grasslands, and it was not a sustainable development for the region because the main income is still agriculture and animal production, so a new strategy was designed in a sustainable scenario to prevent agriculture and grassland area loss in a mutual benefit between nature and human