A reactive system for specifying and running flexible cloud service business processes based on machine learning

2021 ◽  
Author(s):  
Imen Ben Fraj ◽  
Yousra BenDaly Hlaoui ◽  
Leila BenAyed
Keyword(s):  
Machine Learning ◽  
Business Processes ◽  
Cloud Service ◽  
Reactive System ◽  
Service Business

scholarly journals Getting ahead of the Arms Race: Hothousing the Coevolution of VirusTotal with a Packer

Entropy ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. 395
Author(s):  
Héctor D. Menéndez ◽  
David Clark ◽  
Earl T. Barr
Keyword(s):  
Machine Learning ◽  
Evolutionary Computation ◽  
Malware Detection ◽  
Cloud Service ◽  
False Positives ◽  
Arms Race ◽  
System Calls ◽  
A New Technique ◽  
Coevolutionary Arms Race

Malware detection is in a coevolutionary arms race where the attackers and defenders are constantly seeking advantage. This arms race is asymmetric: detection is harder and more expensive than evasion. White hats must be conservative to avoid false positives when searching for malicious behaviour. We seek to redress this imbalance. Most of the time, black hats need only make incremental changes to evade them. On occasion, white hats make a disruptive move and find a new technique that forces black hats to work harder. Examples include system calls, signatures and machine learning. We present a method, called Hothouse, that combines simulation and search to accelerate the white hat’s ability to counter the black hat’s incremental moves, thereby forcing black hats to perform disruptive moves more often. To realise Hothouse, we evolve EEE, an entropy-based polymorphic packer for Windows executables. Playing the role of a black hat, EEE uses evolutionary computation to disrupt the creation of malware signatures. We enter EEE into the detection arms race with VirusTotal, the most prominent cloud service for running anti-virus tools on software. During our 6 month study, we continually improved EEE in response to VirusTotal, eventually learning a packer that produces packed malware whose evasiveness goes from an initial 51.8% median to 19.6%. We report both how well VirusTotal learns to detect EEE-packed binaries and how well VirusTotal forgets in order to reduce false positives. VirusTotal’s tools learn and forget fast, actually in about 3 days. We also show where VirusTotal focuses its detection efforts, by analysing EEE’s variants.

Alignment and Identification of the Relationships between Key Performance Indicators in a Multilevel Tree Structure to Support Transportation Agency Decisions

2021 ◽  
pp. 036119812110190
Author(s):  
Hector Donaldo Mata ◽  
Mohammed Hadi ◽  
David Hale
Keyword(s):  
Machine Learning ◽  
Performance Indicators ◽  
Business Processes ◽  
Key Performance Indicators ◽  
Ordinary Least Squares ◽  
Learning Tools ◽  
Least Squares Regression ◽  
Upper Level ◽  
Selection Operator ◽  
Transportation Agencies

Transportation agencies utilize key performance indicators (KPIs) to measure the performance of their traffic networks and business processes. To make effective decisions based on these KPIs, there is a need to align the KPIs at the strategic, tactical, and operational decision levels and to set targets for these KPIs. However, there has been no known effort to develop methods to ensure this alignment producing a correlative model to explore the relationships to support the derivation of the KPI targets. Such development will lead to more realistic target setting and effective decisions based on these targets, ensuring that agency goals are met subject to the available resources. This paper presents a methodology in which the KPIs are represented in a tree-like structure that can be used to depict the association between metrics at the strategic, tactical, and operational levels. Utilizing a combination of business intelligence and machine learning tools, this paper demonstrates that it is possible not only to identify such relationships but also to quantify them. The proposed methodology compares the effectiveness and accuracy of multiple machine learning models including ordinary least squares regression (OLS), least absolute shrinkage and selection operator (LASSO), and ridge regression, for the identification and quantification of interlevel relationships. The output of the model allows the identification of which metrics have more influence on the upper-level KPI targets. The analysis can be performed at the system, facility, and segment levels, providing important insights on what investments are needed to improve system performance.

scholarly journals AI for augmenting human judgement in Business Processes Management

2021 ◽  
Vol 201 (3) ◽  
pp. 507-518
Author(s):  
Łukasz Osuszek ◽  
Stanisław Stanek
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Support ◽  
Business Processes ◽  
Workflow Management ◽  
Organizational Memory ◽  
Machine Learning Algorithms ◽  
Analytical Models ◽  
Case Based Reasoning ◽  
Historical Case

The paper outlines the recent trends in the evolution of Business Process Management (BPM) – especially the application of AI for decision support. AI has great potential to augment human judgement. Indeed, Machine Learning might be considered as a supplementary and complimentary solution to enhance and support human productivity throughout all aspects of personal and professional life. The idea of merging technologies for organizational learning and workflow management was first put forward by Wargitsch. Herein, completed business cases stored in an organizational memory are used to configure new workflows, while the selection of an appropriate historical case is supported by a case-based reasoning component. This informational environment has been recognized in the world as being effective and has become quite common because of the significant increase in the use of artificial intelligence tools. This article discusses also how automated planning techniques (one of the oldest areas in AI) can be used to enable a new level of automation and processing support. The authors of the article decided to analyse this topic and discuss the scientific state of the art and the application of AI in BPM systems for decision-making support. It should be noted that readily available software exists for the needs of the development of such systems in the field of artificial intelligence. The paper also includes a unique case study with production system of Decision Support, using controlled machine learning algorithms to predictive analytical models.

scholarly journals Discovering Business Processes from Email Logs using fastText and Process Mining

2020 ◽  
Author(s):  
Yaghoub rashnavadi ◽  
Sina Behzadifard ◽  
Reza Farzadnia ◽  
sina zamani
Keyword(s):  
Machine Learning ◽  
Business Processes ◽  
Oil And Gas ◽  
Process Mining ◽  
The Body ◽  
Process Models ◽  
Supervised Machine Learning ◽  
Implicit Information ◽  
Oil And Gas Sector

<p>Communication has never been more accessible than today. With the help of Instant messengers and Email Services, millions of people can transfer information with ease, and this trend has affected organizations as well. There are billions of organizational emails sent or received daily, and their main goal is to facilitate the daily operation of organizations. Behind this vast corpus of human-generated content, there is much implicit information that can be mined and used to improve or optimize the organizations’ operations. Business processes are one of those implicit knowledge areas that can be discovered from Email logs of an Organization, as most of the communications are followed inside Emails. The purpose of this research is to propose an approach to discover the process models in the Email log. In this approach, we combine two tools, supervised machine learning and process mining. With the help of supervised machine learning, fastText classifier, we classify the body text of emails to the activity-related. Then the generated log will be mined with process mining techniques to find process models. We illustrate the approach with a case study company from the oil and gas sector.</p>

scholarly journals Combining Regression and Clustering for Financial Analysis

2020 ◽  
Vol 2 (1) ◽  
pp. 109-118
Author(s):  
Andreea Ioana Chiriac
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cluster Analysis ◽  
Business Processes ◽  
Financial Analysis ◽  
Two Dimensions ◽  
Machine Learning Algorithms ◽  
Financial Ratio ◽  
Research Perspective ◽  
Systems Learning

Abstract Artificial Intelligence is used in business through machine learning algorithms. Machine learning is a part of computer science focused on computer systems learning to perform a specific task without using explicit instructions, relying on patterns and inference instead. Though it might seem like we’ve come a long way in the last ten years, which is true from a research perspective, the adoption of AI among corporations is still relatively low. Over time it became possible to automate more tasks and business processes than ever before. The benefit of using artificial intelligence is that does not require to program every step of the process, predicting at each step what could happen and how to resolve it. The algorithms decide for themselves in each case how the problems should be solved, based on the data that is used. I apply Python language to create a synthetic feature vector that allows visualizations in two dimensions for EDIBTA financial ratio. I use Mean-Square Error in order to evaluate the success, having the optimal parameters. In this section, I also mentioned about the purpose, goals, and applications of cluster analysis. I indicated about the basics of cluster analysis and how to do it and also did a demonstration on how to use K-Means.

scholarly journals CLOUD TECHNOLOGIES IN BUSINESS MANAGEMENT

2021 ◽  
Vol 4 (39) ◽  
pp. 294-301
Author(s):  
V. Gevko ◽  
O. Vivchar ◽  
V. Sharko ◽  
О. Radchenko ◽  
M. Budiaiev ◽  
...  
Keyword(s):  
Business Processes ◽  
Service Providers ◽  
De Novo ◽  
Cloud Service ◽  
Jel Classification ◽  
Digital Transformation ◽  
Cloud Services ◽  
Modern World ◽  
High Tech ◽  
Cloud Technologies

Abstract. The modern world is in the phase of active implementation of digitalization of most processes, including cloud technologies, which is also associated with the introduction of quarantine measures in most countries. Over the past decade, the average annual growth of the cloud services market has expanded by about 50% to more than $ 220 billion. This figure is projected to exceed $ 500 billion in the near future. Currently, a large number of cloud service companies are concentrated in the market. All companies can be classified into national, operating within one country, and international, operating in many countries simultaneously. In Ukraine, the leader is the high-tech company De Novo, which provides services in the segment of corporate clients based on VMware, EMC, Microsoft Azure solutions. Competitors are well-known foreign IT giants, namely: Oracle, IBM, Google, Microsoft, Amazon, HP and others. The Ministry of Digital Transformation of Ukraine plays an important role in this area. The latter signed an agreement with Microsoft to implement the Azure Expansion Program and accelerate Ukraine's digital transformation, including the development of Azure cloud services for the sum of more than $ 500 million. The transition from physical, outdated IT technologies to new cloud services will allow companies to significantly reduce the cost of maintaining their own staff of IT professionals and engineers, reduce dependence on IT equipment suppliers and increase productivity and efficiency of the decisions made. The management of the enterprise can choose one of the offered models from the companies-providers: IaaS, PaaS, SaaS. The choice will depend on the immediate management needs and financial capabilities of the enterprise. In Ukraine, the biggest share of users of cloud services belongs to large utility companies and state enterprises, namely: Dija, Ministry of Internal Affairs, Kyiv Municipal Enterprise "Informatics", Prozorro, Ukrposhta, E-Health (National Health Center of Ukraine), Naftogaz and several other organizations. Thus, the introduction of cloud services involves digital business transformations, namely: optimization of business processes, acceleration of innovation, cost reduction and so on. Key words: IT technologies, cloud technologies, cloud service providers, management, business. JEL Classification L86, M15 Formulas: 0; fig.: 1; tabl.: 2; bibl.: 21.

scholarly journals Predictive Maintenance of Cash Dispenser Using a Cognitive Prioritization Model

2021 ◽  
Vol 15 (1) ◽  
pp. 156-161
Author(s):  
Archana Dixit ◽  
Amol B. Mahamun
Keyword(s):  
Machine Learning ◽  
Environmental Factors ◽  
Business Processes ◽  
Time Window ◽  
Service Planning ◽  
Maintenance Cost ◽  
Business Impact ◽  
Revenue Growth ◽  
Machine Failure ◽  
Emerging Patterns

In this technical paper, we address the issue of predicting cash dispenser (addressed as ‘Device’ henceforth) failure by harnessing the power of humungous data from service history, logs, metrics, transactions, and plausible environmental factors. This study helps increase device availability, enhanced customer experience, manage risk &amp; compliance and revenue growth. It also helps reduce maintenance cost, travel cost, labour cost, downtime, repair duration and increase meantime between failures (MTBF) of individual components. This study uses a cognitive prioritization model which entails the following at its core; a) Machine Learning engineered features with highest influence on machine failure, b) Observation Windows, Transition Windows and Prediction Windows to accommodate various business processes and service planning delivery windows, and c) A forward-looking evaluation of emerging patterns to determine failure prediction score that is prioritized by business impact, for a predefined time window in the future. The model not only predicts failure score for the devices to be serviced, but it also reduces the service miss impact for the prediction windows.

Cloud Service Business Scenarios and Market Analysis1

2010 ◽  
pp. 43-86
Author(s):  
William Y. Chang ◽  
Hosame Abu-Amara ◽  
Jessica Feng Sanford
Keyword(s):  
Cloud Service ◽  
Service Business

Kernel-Based Machine Learning Models to Predict Mitigation Time During Cloud Security Attacks

2021 ◽  
Vol 17 (4) ◽  
pp. 75-88
Author(s):  
Padmaja Kadiri ◽  
Seshadri Ravala
Keyword(s):  
Machine Learning ◽  
Service Provider ◽  
Predictive Models ◽  
Cloud Service ◽  
Cloud Services ◽  
Security Threats ◽  
Learning Models ◽  
Security Attacks ◽  
Cloud Service Provider ◽  
Machine Learning Models

Security threats are unforeseen attacks to the services provided by the cloud service provider. Depending on the type of attack, the cloud service and its associated features will be unavailable. The mitigation time is an integral part of attack recovery. This research paper explores the different parameters that will aid in predicting the mitigation time after an attack on cloud services. Further, the paper presents machine learning models that can predict the mitigation time. The paper presents the kernel-based machine learning models that can predict the average mitigation time during security attacks. The analysis of the results shows that the kernel-based models show 87% accuracy in predicting the mitigation time. Furthermore, the paper explores the performance of the kernel-based machine learning models based on the regression-based predictive models. The regression model is used as a benchmark model to analyze the performance of the machine learning-based predictive models in the prediction of mitigation time in the wake of an attack.

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