Cybersecurity solutions are traditionally static and signature-based. The traditional solutions
along with the use of analytic models, machine learning and big data could be improved by
automatically trigger mitigation or provide relevant awareness to control or limit consequences
of threats. This kind of intelligent solutions is covered in the context of Data Science for
Cybersecurity. Data Science provides a significant role in cybersecurity by utilising the power
of data (and big data), high-performance computing and data mining (and machine learning) to
protect users against cybercrimes. For this purpose, a successful data science project requires
an effective methodology to cover all issues and provide adequate resources. In this paper, we
are introducing popular data science methodologies and will compare them in accordance with
cybersecurity challenges. A comparison discussion has also delivered to explain methodologies’
strengths and weaknesses in case of cybersecurity projects.
Big data for cybersecurity - skilledfield slides - 25032021Mouaz Alnouri
Now more than ever, the landscape of cybersecurity is getting broader. Both small and large organizations are adopting Big Data technologies to enhance their security detection capabilities.
These slides are from a webinar conducted by Skilledfield, you will learn:
- Why Cybersecurity is a Big Data use case
- How we address Cybersecurity as Big Data Professionals
- How we keep up with the emerging cyber threats
- Benefits of Big Data Technologies for Cybersecurity
Security Analytics and Big Data: What You Need to KnowMapR Technologies
The number of attacks on organization's' IT infrastructure are continuously increasing. It is becoming more and more difficult to identify unknown threats, in particular. This problem requires the ability to store more data and better tools to analyze the data.
Learn in this webinar why big data is enabling new security analytics solutions and why the MapR Quick Start Solution for Security Analytics offers an easy starting point for faster and deeper security analytics.
44CON 2014 - Security Analytics Beyond Cyber, Phil Huggins44CON
44CON 2014 - Security Analytics Beyond Cyber, Phil Huggins
A quick summary of the current state of big data technology and data science approaches used in cyber / network defender security analytics including summary use cases, a walk through of a reference architecture and breakdown of the required skills. Focus is on the knowledge needed to run a proof of concept and establish a programme for early benefits. Will then also include a view on the future of extending the platforms and capabilities of security analytics to cover performance metrics and data-driven security management approaches.
Wolters Kluwer and Risk.Net present the current challenges, priorities and trends influencing banks’ investment in risktech and assesses how they can drive better value in the future. Survey report.
Big data for cybersecurity - skilledfield slides - 25032021Mouaz Alnouri
Now more than ever, the landscape of cybersecurity is getting broader. Both small and large organizations are adopting Big Data technologies to enhance their security detection capabilities.
These slides are from a webinar conducted by Skilledfield, you will learn:
- Why Cybersecurity is a Big Data use case
- How we address Cybersecurity as Big Data Professionals
- How we keep up with the emerging cyber threats
- Benefits of Big Data Technologies for Cybersecurity
Security Analytics and Big Data: What You Need to KnowMapR Technologies
The number of attacks on organization's' IT infrastructure are continuously increasing. It is becoming more and more difficult to identify unknown threats, in particular. This problem requires the ability to store more data and better tools to analyze the data.
Learn in this webinar why big data is enabling new security analytics solutions and why the MapR Quick Start Solution for Security Analytics offers an easy starting point for faster and deeper security analytics.
44CON 2014 - Security Analytics Beyond Cyber, Phil Huggins44CON
44CON 2014 - Security Analytics Beyond Cyber, Phil Huggins
A quick summary of the current state of big data technology and data science approaches used in cyber / network defender security analytics including summary use cases, a walk through of a reference architecture and breakdown of the required skills. Focus is on the knowledge needed to run a proof of concept and establish a programme for early benefits. Will then also include a view on the future of extending the platforms and capabilities of security analytics to cover performance metrics and data-driven security management approaches.
Wolters Kluwer and Risk.Net present the current challenges, priorities and trends influencing banks’ investment in risktech and assesses how they can drive better value in the future. Survey report.
Streaming Cyber Security into Graph: Accelerating Data into DataStax Graph an...Keith Kraus
Traditional security tools like security information and event managers (SIEMs) are struggling to keep up with the terabytes of event data (250M to 2B events) being generated each day from an ever-growing number of devices. Cybersecurity has become a data problem, and enterprises need to reply with scalable solutions to enable effective hunting and combat evolving attacks. Rethinking the cybersecurity problem as a data-centric problem led Accenture Labs’s Cybersecurity team to use emerging big data tools along with new approaches such as graph databases and analysis to exploit the connected nature of the data to its advantage. Joshua Patterson, Michael Wendt, and Keith Kraus explain how Accenture Labs’s Cybersecurity team is using Apache Kafka, Spark, and Flink to stream data into Blazegraph and Datastax Graph to accelerate cyber defense.
Leveraging Datastax Graph and Blazegraph allows Accenture Labs to greatly accelerate query and analysis performance compared to traditional security tools like SIEM. Josh, Michael, and Keith share the challenges of fitting cybersecurity data into each of the graph structures, as well as the ways they exploited the connectedness of events to discover new threats that would have been missed in traditional SIEM tools. In addition, they explain how they use GPUs to accelerate graph analysis by using Blazegraph DASL. Josh, Michael, and Keith end by demonstrating how to efficiently and effectively stream data into these graph databases using best-in-breed technologies such as Apache Kafka, Spark, and Flink and touch on why Kudu is becoming an integral part of Accenture’s technology stack. Utilizing these technologies, clients have supercharged their security analysts’ cyber-hunting abilities and are uncovering threats faster.
Artificial intelligence has been a buzz word that is impacting every industry in the world. With the rise of such advanced technology, there will be always a question regarding its impact on our social life, environment and economy thus impacting all efforts exerted towards sustainable development. In the information era, enormous amounts of data have become available on hand to decision makers. Big data refers to datasets that are not only big, but also high in variety and velocity, which makes them difficult to handle using traditional tools and techniques. Due to the rapid growth of such data, solutions need to be studied and provided in order to handle and extract value and knowledge from these datasets for different industries and business operations. Numerous use cases have shown that AI can ensure an effective supply of information to citizens, users and customers in times of crisis. This paper aims to analyse some of the different methods and scenario which can be applied to AI and big data, as well as the opportunities provided by the application in various business operations and crisis management domains.
OpenText PowerDOCS: A Cloud Solution for Document GenerationMarc St-Pierre
OpenText offers a comprehensive cloud solution that functions as a single source for document generation across all use cases, channels, technology platforms, and business systems.
The slide has details on below points:
1. Introduction to Machine Learning
2. What are the challenges in acceptance of Machine Learning in Banks
3. How to overcome the challenges in adoption of Machine Learning in Banks
4. How to find new use cases of Machine Learning
5. Few current interesting use cases of Machine Learning
Please contact me (shekup@gmail.com) or connect with me on LinkedIn (https://www.linkedin.com/in/shekup/) for more explanation on ML and how it may help your business.
The slides are inspired by:
Survey & interviews done by me with Bankers & Technology Professionals
Presentation from Google NEXT 2017
Presentation by DATUM on Youtube
Royal Society Machine Learning
Big Data & Social Analytics Course from MIT & GetSmarter
How to Operationalize Big Data Security AnalyticsInterset
Analytics tools and analysis tools are not the same. Here is how to accelerate threat-detection activities with a holistic, strategic security-analytics solution.
CSPCR: Cloud Security, Privacy and Compliance Readiness - A Trustworthy Fram...IJECEIAES
The privacy, handling, management and security of information in a cloud environment are complex and tedious tasks to achieve. With minimum investment and reduced cost of operations an organization can avail and apply the benefits of cloud computing into its business. This computing paradigm is based upon a pay as per your usage model. Moreover, security, privacy, compliance, risk management and service level agreement are critical issues in cloud computing environment. In fact, there is dire need of a model which can tackle and handle all the security and privacy issues. Therefore, we suggest a CSPCR model for evaluating the preparation of an organization to handle or to counter the threats, hazards in cloud computing environment. CSPCR discusses rules and regulations which are considered as pre-requisites in migrating or shifting to cloud computing services.
Think big data, and think opportunity. That is, think beyond storing and managing data, and leverage analytics to derive more value than imaginable from your business intelligence. This white paper offers a forward thinking, collaborative approach to analyzing data and changing the way you think about business.
The Myths + Realities of Machine-Learning CybersecurityInterset
Dr. Chase Cunningham, Principal Analyst at Forrester Research, joined Interset’s CTO, Stephan Jou, for a chat about what machine learning means and how enterprises can successfully deploy security analytics strengthened by this type of artificial intelligence. (For more information, visit Interset.com.)
To Serve and Protect: Making Sense of Hadoop Security Inside Analysis
The Briefing Room with Dr. Robin Bloor and HP Security Voltage
Live Webcast September 22, 2015
Watch the archive: https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=45ece7082b1d7c2cc8179bc7a1a69ea5
Hadoop is rapidly becoming a development platform and dominant server environment, and organizations are keen to take advantage of its massively scalable – and relatively inexpensive – resources. It is not, however, without its limitations, and it often requires a contingent of complementary components in order to behave within an information architecture. One area often overlooked is security, a factor that, if not considered from the onset, can insert great risk when putting sensitive data in Hadoop.
Register for this episode of The Briefing Room to learn from veteran Analyst Dr. Robin Bloor as he discusses how security was never a design point for Hadoop and what organizations can do about it. He’ll be briefed by Sudeep Venkatesh of HP Security Voltage, who will explain the intricacies surrounding a secure Hadoop implementation. He will show how techniques like format-preserving and partial-field encryption can allow for analytics over protected data, with zero performance impact.
Visit InsideAnalysis.com for more information.
Presented at ISACA Indonesia Monthly Technical Meeting, 11 Dec 2019 at Telkom Landmark.
Key takeaways from my presentation:
1. Cloud customers have to understand the share responsibilities between customer and cloud provider
2. Different cloud service model (IaaS, PaaS, SaaS) has different audit methodology
3. Customer’s IT Auditor have to be trained to have the skills needed to audit the cloud service
4. Understanding IAM in Cloud is very important. Each Cloud Service Provider has different IAM mechanism
5. Understanding different type of audit logs in cloud platform is important for IT Auditor
Cloud computing offers a very important approach to achieving lasting strategic advantages by rapidly adapting to complex challenges in IT management and data analytics. This paper discusses the business impact and analytic transformation opportunities of cloud computing. Moreover, it highlights the differences among two cloud architectures—Utility Clouds and Data Clouds—with illustrative examples of how Data Clouds are shaping new advances in Intelligence Analysis.
We have concentrated on a range of strategies, methodologies, and distinct fields of research in this article, all of which are useful and relevant in the field of data mining technologies. As we all know, numerous multinational corporations and major corporations operate in various parts of the world. Each location of business may create significant amounts of data. Corporate decision-makers need access to all of these data sources in order to make strategic decisions.
Applying Classification Technique using DID3 Algorithm to improve Decision Su...IJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
International Journal of Modern Engineering Research (IJMER) covers all the fields of engineering and science: Electrical Engineering, Mechanical Engineering, Civil Engineering, Chemical Engineering, Computer Engineering, Agricultural Engineering, Aerospace Engineering, Thermodynamics, Structural Engineering, Control Engineering, Robotics, Mechatronics, Fluid Mechanics, Nanotechnology, Simulators, Web-based Learning, Remote Laboratories, Engineering Design Methods, Education Research, Students' Satisfaction and Motivation, Global Projects, and Assessment…. And many more.
Streaming Cyber Security into Graph: Accelerating Data into DataStax Graph an...Keith Kraus
Traditional security tools like security information and event managers (SIEMs) are struggling to keep up with the terabytes of event data (250M to 2B events) being generated each day from an ever-growing number of devices. Cybersecurity has become a data problem, and enterprises need to reply with scalable solutions to enable effective hunting and combat evolving attacks. Rethinking the cybersecurity problem as a data-centric problem led Accenture Labs’s Cybersecurity team to use emerging big data tools along with new approaches such as graph databases and analysis to exploit the connected nature of the data to its advantage. Joshua Patterson, Michael Wendt, and Keith Kraus explain how Accenture Labs’s Cybersecurity team is using Apache Kafka, Spark, and Flink to stream data into Blazegraph and Datastax Graph to accelerate cyber defense.
Leveraging Datastax Graph and Blazegraph allows Accenture Labs to greatly accelerate query and analysis performance compared to traditional security tools like SIEM. Josh, Michael, and Keith share the challenges of fitting cybersecurity data into each of the graph structures, as well as the ways they exploited the connectedness of events to discover new threats that would have been missed in traditional SIEM tools. In addition, they explain how they use GPUs to accelerate graph analysis by using Blazegraph DASL. Josh, Michael, and Keith end by demonstrating how to efficiently and effectively stream data into these graph databases using best-in-breed technologies such as Apache Kafka, Spark, and Flink and touch on why Kudu is becoming an integral part of Accenture’s technology stack. Utilizing these technologies, clients have supercharged their security analysts’ cyber-hunting abilities and are uncovering threats faster.
Artificial intelligence has been a buzz word that is impacting every industry in the world. With the rise of such advanced technology, there will be always a question regarding its impact on our social life, environment and economy thus impacting all efforts exerted towards sustainable development. In the information era, enormous amounts of data have become available on hand to decision makers. Big data refers to datasets that are not only big, but also high in variety and velocity, which makes them difficult to handle using traditional tools and techniques. Due to the rapid growth of such data, solutions need to be studied and provided in order to handle and extract value and knowledge from these datasets for different industries and business operations. Numerous use cases have shown that AI can ensure an effective supply of information to citizens, users and customers in times of crisis. This paper aims to analyse some of the different methods and scenario which can be applied to AI and big data, as well as the opportunities provided by the application in various business operations and crisis management domains.
OpenText PowerDOCS: A Cloud Solution for Document GenerationMarc St-Pierre
OpenText offers a comprehensive cloud solution that functions as a single source for document generation across all use cases, channels, technology platforms, and business systems.
The slide has details on below points:
1. Introduction to Machine Learning
2. What are the challenges in acceptance of Machine Learning in Banks
3. How to overcome the challenges in adoption of Machine Learning in Banks
4. How to find new use cases of Machine Learning
5. Few current interesting use cases of Machine Learning
Please contact me (shekup@gmail.com) or connect with me on LinkedIn (https://www.linkedin.com/in/shekup/) for more explanation on ML and how it may help your business.
The slides are inspired by:
Survey & interviews done by me with Bankers & Technology Professionals
Presentation from Google NEXT 2017
Presentation by DATUM on Youtube
Royal Society Machine Learning
Big Data & Social Analytics Course from MIT & GetSmarter
How to Operationalize Big Data Security AnalyticsInterset
Analytics tools and analysis tools are not the same. Here is how to accelerate threat-detection activities with a holistic, strategic security-analytics solution.
CSPCR: Cloud Security, Privacy and Compliance Readiness - A Trustworthy Fram...IJECEIAES
The privacy, handling, management and security of information in a cloud environment are complex and tedious tasks to achieve. With minimum investment and reduced cost of operations an organization can avail and apply the benefits of cloud computing into its business. This computing paradigm is based upon a pay as per your usage model. Moreover, security, privacy, compliance, risk management and service level agreement are critical issues in cloud computing environment. In fact, there is dire need of a model which can tackle and handle all the security and privacy issues. Therefore, we suggest a CSPCR model for evaluating the preparation of an organization to handle or to counter the threats, hazards in cloud computing environment. CSPCR discusses rules and regulations which are considered as pre-requisites in migrating or shifting to cloud computing services.
Think big data, and think opportunity. That is, think beyond storing and managing data, and leverage analytics to derive more value than imaginable from your business intelligence. This white paper offers a forward thinking, collaborative approach to analyzing data and changing the way you think about business.
The Myths + Realities of Machine-Learning CybersecurityInterset
Dr. Chase Cunningham, Principal Analyst at Forrester Research, joined Interset’s CTO, Stephan Jou, for a chat about what machine learning means and how enterprises can successfully deploy security analytics strengthened by this type of artificial intelligence. (For more information, visit Interset.com.)
To Serve and Protect: Making Sense of Hadoop Security Inside Analysis
The Briefing Room with Dr. Robin Bloor and HP Security Voltage
Live Webcast September 22, 2015
Watch the archive: https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=45ece7082b1d7c2cc8179bc7a1a69ea5
Hadoop is rapidly becoming a development platform and dominant server environment, and organizations are keen to take advantage of its massively scalable – and relatively inexpensive – resources. It is not, however, without its limitations, and it often requires a contingent of complementary components in order to behave within an information architecture. One area often overlooked is security, a factor that, if not considered from the onset, can insert great risk when putting sensitive data in Hadoop.
Register for this episode of The Briefing Room to learn from veteran Analyst Dr. Robin Bloor as he discusses how security was never a design point for Hadoop and what organizations can do about it. He’ll be briefed by Sudeep Venkatesh of HP Security Voltage, who will explain the intricacies surrounding a secure Hadoop implementation. He will show how techniques like format-preserving and partial-field encryption can allow for analytics over protected data, with zero performance impact.
Visit InsideAnalysis.com for more information.
Presented at ISACA Indonesia Monthly Technical Meeting, 11 Dec 2019 at Telkom Landmark.
Key takeaways from my presentation:
1. Cloud customers have to understand the share responsibilities between customer and cloud provider
2. Different cloud service model (IaaS, PaaS, SaaS) has different audit methodology
3. Customer’s IT Auditor have to be trained to have the skills needed to audit the cloud service
4. Understanding IAM in Cloud is very important. Each Cloud Service Provider has different IAM mechanism
5. Understanding different type of audit logs in cloud platform is important for IT Auditor
Cloud computing offers a very important approach to achieving lasting strategic advantages by rapidly adapting to complex challenges in IT management and data analytics. This paper discusses the business impact and analytic transformation opportunities of cloud computing. Moreover, it highlights the differences among two cloud architectures—Utility Clouds and Data Clouds—with illustrative examples of how Data Clouds are shaping new advances in Intelligence Analysis.
We have concentrated on a range of strategies, methodologies, and distinct fields of research in this article, all of which are useful and relevant in the field of data mining technologies. As we all know, numerous multinational corporations and major corporations operate in various parts of the world. Each location of business may create significant amounts of data. Corporate decision-makers need access to all of these data sources in order to make strategic decisions.
Applying Classification Technique using DID3 Algorithm to improve Decision Su...IJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
International Journal of Modern Engineering Research (IJMER) covers all the fields of engineering and science: Electrical Engineering, Mechanical Engineering, Civil Engineering, Chemical Engineering, Computer Engineering, Agricultural Engineering, Aerospace Engineering, Thermodynamics, Structural Engineering, Control Engineering, Robotics, Mechatronics, Fluid Mechanics, Nanotechnology, Simulators, Web-based Learning, Remote Laboratories, Engineering Design Methods, Education Research, Students' Satisfaction and Motivation, Global Projects, and Assessment…. And many more.
Data mining is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. 1 Data mining is an interdisciplinary sub field of computer science and statistics with an overall goal to extract from a data set and transform the information into a comprehensible structure for further use. 1 2 3 4 The process of digging through data to discover hidden connections and predict future trends has a long history. Sometimes referred to as 'knowledge discovery' in databases, the term data mining wasn't coined until the 1990s. What was old is new again, as data mining technology keeps evolving to keep pace with the limitless potential of big data and affordable computing power. Over the last decade, advances in processing power and speed have enabled us to move beyond manual, tedious and time consuming practices to quick, easy and automated data analysis. The more complex the data sets collected, the more potential there is to uncover relevant insights. Rupashi Koul "Overview of Data Mining" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31368.pdf Paper Url :https://www.ijtsrd.com/engineering/computer-engineering/31368/overview-of-data-mining/rupashi-koul
Cluster Based Access Privilege Management Scheme for DatabasesEditor IJMTER
Knowledge discovery is carried out using the data mining techniques. Association rule mining,
classification and clustering operations are carried out under data mining. Clustering method is used to group up the
records based on the relevancy. Distance or similarity measures are used to estimate the transaction relationship.
Census data and medical data are referred as micro data. Data publish schemes are used to provide private data for
analysis. Privacy preservation is used to protect private data values. Anonymity is considered in the privacy
preservation process.
Data values are allowed to authorized users using the access control models. Privacy Protection Mechanism
(PPM) uses suppression and generalization of relational data to anonymize and satisfy privacy needs. Accuracyconstrained privacy-preserving access control framework is used to manage access control in relational database. The
access control policies define selection predicates available to roles while the privacy requirement is to satisfy the kanonymity or l-diversity. Imprecision bound constraint is assigned for each selection predicate. k-anonymous
Partitioning with Imprecision Bounds (k-PIB) is used to estimate accuracy and privacy constraints. Role-based Access
Control (RBAC) allows defining permissions on objects based on roles in an organization. Top Down Selection
Mondrian (TDSM) algorithm is used for query workload-based anonymization. The Top Down Selection Mondrian
(TDSM) algorithm is constructed using greedy heuristics and kd-tree model. Query cuts are selected with minimum
bounds in Top-Down Heuristic 1 algorithm (TDH1). The query bounds are updated as the partitions are added to the
output in Top-Down Heuristic 2 algorithm (TDH2). The cost of reduced precision in the query results is used in TopDown Heuristic 3 algorithm (TDH3). Repartitioning algorithm is used to reduce the total imprecision for the queries.
The privacy preserved access privilege management scheme is enhanced to provide incremental mining
features. Data insert, delete and update operations are connected with the partition management mechanism. Cell level
access control is provided with differential privacy method. Dynamic role management model is integrated with the
access control policy mechanism for query predicates.
DATA MINING IN EDUCATION : A REVIEW ON THE KNOWLEDGE DISCOVERY PERSPECTIVEIJDKP
Knowledge Discovery in Databases is the process of finding knowledge in massive amount of data where
data mining is the core of this process. Data mining can be used to mine understandable meaningful patterns from large databases and these patterns may then be converted into knowledge.Data mining is the process of extracting the information and patterns derived by the KDD process which helps in crucial decision-making.Data mining works with data warehouse and the whole process is divded into action plan to be performed on data: Selection, transformation, mining and results interpretation. In this paper, we have reviewed Knowledge Discovery perspective in Data Mining and consolidated different areas of data
mining, its techniques and methods in it.
FEDERATED LEARNING FOR PRIVACY-PRESERVING: A REVIEW OF PII DATA ANALYSIS IN F...ijseajournal
There has been tremendous growth in the field of AI and machine learning. The developments across these
fields have resulted in a considerable increase in other FinTech fields. Cyber security has been described
as an essential part of the developments associated with technology. Increased cyber security ensures that
people remain protected, and that data remains safe. New methods have been integrated into developing AI
that achieves cyber security. The data analysis capabilities of AI and its cyber security functions have
ensured that privacy has increased significantly. The ethical concept associated with data privacy has also
been advocated across most FinTech regulations. These concepts and considerations have all been
engaged with the need to achieve the required ethical requirements. The concept of federated learning is a
recently developed measure that achieves the abovementioned concept. It ensured the development of AI
and machine learning while keeping privacy in data analysis. The research paper effectively describes the
issue of federated learning for confidentiality. It describes the overall process associated with its
development and some of the contributions it has achieved. The widespread application of federated
learning in FinTech is showcased, and why federated learning is essential for overall growth in FinTech.
In the era of data-driven warfare, the integration of big data and machine learning (ML) techniques has
become paramount for enhancing defence capabilities. This research report delves into the applications of
big data and ML in the defence sector, exploring their potential to revolutionize intelligence gathering,
strategic decision-making, and operational efficiency. By leveraging vast amounts of data and advanced
algorithms, these technologies offer unprecedented opportunities for threat detection, predictive analysis,
and optimized resource allocation. However, their adoption also raises critical concerns regarding data
privacy, ethical implications, and the potential for misuse. This report aims to provide a comprehensive
understanding of the current state of big data and ML in defence, while examining the challenges and
ethical considerations that must be addressed to ensure responsible and effective implementation.
Big data is a broad term for data sets so large or complex that tr.docxhartrobert670
Big data is a broad term for data sets so large or complex that traditional data processing applications are inadequate. Challenges include analysis, capture, curation, search, sharing, storage, transfer, visualization, and information privacy. The term often refers simply to the use of predictive analytics or other certain advanced methods to extract value from data, and seldom to a particular size of data set.
Analysis of data sets can find new correlations, to "spot business trends, prevent diseases, combat crime and so on."[1] Scientists, practitioners of media and advertising and governments alike regularly meet difficulties with large data sets in areas including Internet search, finance and business informatics. Scientists encounter limitations in e-Science work, including meteorology, genomics,[2]connectomics, complex physics simulations,[3] and biological and environmental research.[4]
Data sets grow in size in part because they are increasingly being gathered by cheap and numerous information-sensing mobile devices, aerial (remote sensing), software logs, cameras, microphones, radio-frequency identification (RFID) readers, and wireless sensor networks.[5]
HYPERLINK "http://en.wikipedia.org/wiki/Big_data" \l "cite_note-6" [6]
HYPERLINK "http://en.wikipedia.org/wiki/Big_data" \l "cite_note-7" [7] The world's technological per-capita capacity to store information has roughly doubled every 40 months since the 1980s;[8] as of 2012, every day 2.5 exabytes (2.5×1018) of data were created;[9] The challenge for large enterprises is determining who should own big data initiatives that straddle the entire organization.[10]
Work with big data is necessarily uncommon; most analysis is of "PC size" data, on a desktop PC or notebook[11] that can handle the available data set.
Relational database management systems and desktop statistics and visualization packages often have difficulty handling big data. The work instead requires "massively parallel software running on tens, hundreds, or even thousands of servers".[12] What is considered "big data" varies depending on the capabilities of the users and their tools, and expanding capabilities make Big Data a moving target. Thus, what is considered to be "Big" in one year will become ordinary in later years. "For some organizations, facing hundreds of gigabytes of data for the first time may trigger a need to reconsider data management options. For others, it may take tens or hundreds of terabytes before data size becomes a significant consideration."[13]
Contents
· 1 Definition
· 2 Characteristics
· 3 Architecture
· 4 Technologies
· 5 Applications
· 5.1 Government
· 5.1.1 United States of America
· 5.1.2 India
· 5.1.3 United Kingdom
· 5.2 International development
· 5.3 Manufacturing
· 5.3.1 Cyber-Physical Models
· 5.4 Media
· 5.4.1 Internet of Things (IoT)
· 5.4.2 Technology
· 5.5 Private sector
· 5.5.1 Retail
· 5.5.2 Retail Banking
· 5.5.3 Real Estate
· 5.6 Science
· 5.6.1 Science and Resear ...
Characterizing and Processing of Big Data Using Data Mining TechniquesIJTET Journal
Abstract— Big data is a popular term used to describe the exponential growth and availability of data, both structured and unstructured. It concerns Large-Volume, Complex and growing data sets in both multiple and autonomous sources. Not only in science and engineering big data are now rapidly expanding in all domains like physical, bio logical etc...The main objective of this paper is to characterize the features of big data. Here the HACE theorem, that characterizes the features of the Big Data revolution, and proposes a Big Data processing model, from the data mining perspective, is used. The aggregation of mining, analysis, information sources, user interest modeling, privacy and security are involved in this model. To explore and extract the large volumes of data and useful information or knowledge respectively is the most fundamental challenge in Big Data. So we should have a tendency to analyze these problems and knowledge revolution.
Big data is to be implemented in as full way in real-time; it is still in a research. People
need to know what to do with enormous data. Insurance agencies are actively participating for the
analysis of patient's data which could be used to extract some useful information. Analysis is done in
term of discharge summary, drug & pharma, diagnostics details, doctor’s report, medical history,
allergies & insurance policies which are made by the application of map reduce and useful data is
extracted. We are analysing more number of factors like disease Types with its agreeing reasons,
insurance policy details along with sanctioned amount, family grade wise segregation.
Keywords: Big data, Stemming, Map reduce Policy and Hadoop.
An overview of cyber security data science from a perspective of machine lear...PhD Assistance
Machine learning (ML) is sometimes regarded as a subset of “Artificial Intelligence,” and it is strongly related to data science, data mining, and computational statistics.
For #Enquiry:
Website: https://www.phdassistance.com/blog/an-overview-of-cyber-security-data-science-from-a-perspective-of-machine-learning/
India: +91 91769 66446
Email: info@phdassistance.com
The Survey of Data Mining Applications And Feature Scope IJCSEIT Journal
In this paper we have focused a variety of techniques, approaches and different areas of the research which
are helpful and marked as the important field of data mining Technologies. As we are aware that many MNC’s
and large organizations are operated in different places of the different countries. Each place of operation
may generate large volumes of data. Corporate decision makers require access from all such sources and
take strategic decisions .The data warehouse is used in the significant business value by improving the
effectiveness of managerial decision-making. In an uncertain and highly competitive business
environment, the value of strategic information systems such as these are easily recognized however in
today’s business environment, efficiency or speed is not the only key for competitiveness. This type of huge
amount of data’s are available in the form of tera- to peta-bytes which has drastically changed in the areas
of science and engineering. To analyze, manage and make a decision of such type of huge amount of data
we need techniques called the data mining which will transforming in many fields. This paper imparts more
number of applications of the data mining and also o focuses scope of the data mining which will helpful in
the further research.
ANALYSIS OF LAND SURFACE DEFORMATION GRADIENT BY DINSAR cscpconf
The progressive development of Synthetic Aperture Radar (SAR) systems diversify the exploitation of the generated images by these systems in different applications of geoscience. Detection and monitoring surface deformations, procreated by various phenomena had benefited from this evolution and had been realized by interferometry (InSAR) and differential interferometry (DInSAR) techniques. Nevertheless, spatial and temporal decorrelations of the interferometric couples used, limit strongly the precision of analysis results by these techniques. In this context, we propose, in this work, a methodological approach of surface deformation detection and analysis by differential interferograms to show the limits of this technique according to noise quality and level. The detectability model is generated from the deformation signatures, by simulating a linear fault merged to the images couples of ERS1 / ERS2 sensors acquired in a region of the Algerian south.
4D AUTOMATIC LIP-READING FOR SPEAKER'S FACE IDENTIFCATIONcscpconf
A novel based a trajectory-guided, concatenating approach for synthesizing high-quality image real sample renders video is proposed . The lips reading automated is seeking for modeled the closest real image sample sequence preserve in the library under the data video to the HMM predicted trajectory. The object trajectory is modeled obtained by projecting the face patterns into an KDA feature space is estimated. The approach for speaker's face identification by using synthesise the identity surface of a subject face from a small sample of patterns which sparsely each the view sphere. An KDA algorithm use to the Lip-reading image is discrimination, after that work consisted of in the low dimensional for the fundamental lip features vector is reduced by using the 2D-DCT.The mouth of the set area dimensionality is ordered by a normally reduction base on the PCA to obtain the Eigen lips approach, their proposed approach by[33]. The subjective performance results of the cost function under the automatic lips reading modeled , which wasn’t illustrate the superior performance of the
method.
MOVING FROM WATERFALL TO AGILE PROCESS IN SOFTWARE ENGINEERING CAPSTONE PROJE...cscpconf
Universities offer software engineering capstone course to simulate a real world-working environment in which students can work in a team for a fixed period to deliver a quality product. The objective of the paper is to report on our experience in moving from Waterfall process to Agile process in conducting the software engineering capstone project. We present the capstone course designs for both Waterfall driven and Agile driven methodologies that highlight the structure, deliverables and assessment plans.To evaluate the improvement, we conducted a survey for two different sections taught by two different instructors to evaluate students’ experience in moving from traditional Waterfall model to Agile like process. Twentyeight students filled the survey. The survey consisted of eight multiple-choice questions and an open-ended question to collect feedback from students. The survey results show that students were able to attain hands one experience, which simulate a real world-working environment. The results also show that the Agile approach helped students to have overall better design and avoid mistakes they have made in the initial design completed in of the first phase of the capstone project. In addition, they were able to decide on their team capabilities, training needs and thus learn the required technologies earlier which is reflected on the final product quality
PROMOTING STUDENT ENGAGEMENT USING SOCIAL MEDIA TECHNOLOGIEScscpconf
Using social media in education provides learners with an informal way for communication. Informal communication tends to remove barriers and hence promotes student engagement. This paper presents our experience in using three different social media technologies in teaching software project management course. We conducted different surveys at the end of every semester to evaluate students’ satisfaction and engagement. Results show that using social media enhances students’ engagement and satisfaction. However, familiarity with the tool is an important factor for student satisfaction.
A SURVEY ON QUESTION ANSWERING SYSTEMS: THE ADVANCES OF FUZZY LOGICcscpconf
In real world computing environment with using a computer to answer questions has been a human dream since the beginning of the digital era, Question-answering systems are referred to as intelligent systems, that can be used to provide responses for the questions being asked by the user based on certain facts or rules stored in the knowledge base it can generate answers of questions asked in natural , and the first main idea of fuzzy logic was to working on the problem of computer understanding of natural language, so this survey paper provides an overview on what Question-Answering is and its system architecture and the possible relationship and
different with fuzzy logic, as well as the previous related research with respect to approaches that were followed. At the end, the survey provides an analytical discussion of the proposed QA models, along or combined with fuzzy logic and their main contributions and limitations.
DYNAMIC PHONE WARPING – A METHOD TO MEASURE THE DISTANCE BETWEEN PRONUNCIATIONS cscpconf
Human beings generate different speech waveforms while speaking the same word at different times. Also, different human beings have different accents and generate significantly varying speech waveforms for the same word. There is a need to measure the distances between various words which facilitate preparation of pronunciation dictionaries. A new algorithm called Dynamic Phone Warping (DPW) is presented in this paper. It uses dynamic programming technique for global alignment and shortest distance measurements. The DPW algorithm can be used to enhance the pronunciation dictionaries of the well-known languages like English or to build pronunciation dictionaries to the less known sparse languages. The precision measurement experiments show 88.9% accuracy.
INTELLIGENT ELECTRONIC ASSESSMENT FOR SUBJECTIVE EXAMS cscpconf
In education, the use of electronic (E) examination systems is not a novel idea, as Eexamination systems have been used to conduct objective assessments for the last few years. This research deals with randomly designed E-examinations and proposes an E-assessment system that can be used for subjective questions. This system assesses answers to subjective questions by finding a matching ratio for the keywords in instructor and student answers. The matching ratio is achieved based on semantic and document similarity. The assessment system is composed of four modules: preprocessing, keyword expansion, matching, and grading. A survey and case study were used in the research design to validate the proposed system. The examination assessment system will help instructors to save time, costs, and resources, while increasing efficiency and improving the productivity of exam setting and assessments.
TWO DISCRETE BINARY VERSIONS OF AFRICAN BUFFALO OPTIMIZATION METAHEURISTICcscpconf
African Buffalo Optimization (ABO) is one of the most recent swarms intelligence based metaheuristics. ABO algorithm is inspired by the buffalo’s behavior and lifestyle. Unfortunately, the standard ABO algorithm is proposed only for continuous optimization problems. In this paper, the authors propose two discrete binary ABO algorithms to deal with binary optimization problems. In the first version (called SBABO) they use the sigmoid function and probability model to generate binary solutions. In the second version (called LBABO) they use some logical operator to operate the binary solutions. Computational results on two knapsack problems (KP and MKP) instances show the effectiveness of the proposed algorithm and their ability to achieve good and promising solutions.
DETECTION OF ALGORITHMICALLY GENERATED MALICIOUS DOMAINcscpconf
In recent years, many malware writers have relied on Dynamic Domain Name Services (DDNS) to maintain their Command and Control (C&C) network infrastructure to ensure a persistence presence on a compromised host. Amongst the various DDNS techniques, Domain Generation Algorithm (DGA) is often perceived as the most difficult to detect using traditional methods. This paper presents an approach for detecting DGA using frequency analysis of the character distribution and the weighted scores of the domain names. The approach’s feasibility is demonstrated using a range of legitimate domains and a number of malicious algorithmicallygenerated domain names. Findings from this study show that domain names made up of English characters “a-z” achieving a weighted score of < 45 are often associated with DGA. When a weighted score of < 45 is applied to the Alexa one million list of domain names, only 15% of the domain names were treated as non-human generated.
GLOBAL MUSIC ASSET ASSURANCE DIGITAL CURRENCY: A DRM SOLUTION FOR STREAMING C...cscpconf
The amount of piracy in the streaming digital content in general and the music industry in specific is posing a real challenge to digital content owners. This paper presents a DRM solution to monetizing, tracking and controlling online streaming content cross platforms for IP enabled devices. The paper benefits from the current advances in Blockchain and cryptocurrencies. Specifically, the paper presents a Global Music Asset Assurance (GoMAA) digital currency and presents the iMediaStreams Blockchain to enable the secure dissemination and tracking of the streamed content. The proposed solution provides the data owner the ability to control the flow of information even after it has been released by creating a secure, selfinstalled, cross platform reader located on the digital content file header. The proposed system provides the content owners’ options to manage their digital information (audio, video, speech, etc.), including the tracking of the most consumed segments, once it is release. The system benefits from token distribution between the content owner (Music Bands), the content distributer (Online Radio Stations) and the content consumer(Fans) on the system blockchain.
IMPORTANCE OF VERB SUFFIX MAPPING IN DISCOURSE TRANSLATION SYSTEMcscpconf
This paper discusses the importance of verb suffix mapping in Discourse translation system. In
discourse translation, the crucial step is Anaphora resolution and generation. In Anaphora
resolution, cohesion links like pronouns are identified between portions of text. These binders
make the text cohesive by referring to nouns appearing in the previous sentences or nouns
appearing in sentences after them. In Machine Translation systems, to convert the source
language sentences into meaningful target language sentences the verb suffixes should be
changed as per the cohesion links identified. This step of translation process is emphasized in
the present paper. Specifically, the discussion is on how the verbs change according to the
subjects and anaphors. To explain the concept, English is used as the source language (SL) and
an Indian language Telugu is used as Target language (TL)
EXACT SOLUTIONS OF A FAMILY OF HIGHER-DIMENSIONAL SPACE-TIME FRACTIONAL KDV-T...cscpconf
In this paper, based on the definition of conformable fractional derivative, the functional
variable method (FVM) is proposed to seek the exact traveling wave solutions of two higherdimensional
space-time fractional KdV-type equations in mathematical physics, namely the
(3+1)-dimensional space–time fractional Zakharov-Kuznetsov (ZK) equation and the (2+1)-
dimensional space–time fractional Generalized Zakharov-Kuznetsov-Benjamin-Bona-Mahony
(GZK-BBM) equation. Some new solutions are procured and depicted. These solutions, which
contain kink-shaped, singular kink, bell-shaped soliton, singular soliton and periodic wave
solutions, have many potential applications in mathematical physics and engineering. The
simplicity and reliability of the proposed method is verified.
AUTOMATED PENETRATION TESTING: AN OVERVIEWcscpconf
The using of information technology resources is rapidly increasing in organizations,
businesses, and even governments, that led to arise various attacks, and vulnerabilities in the
field. All resources make it a must to do frequently a penetration test (PT) for the environment
and see what can the attacker gain and what is the current environment's vulnerabilities. This
paper reviews some of the automated penetration testing techniques and presents its
enhancement over the traditional manual approaches. To the best of our knowledge, it is the
first research that takes into consideration the concept of penetration testing and the standards
in the area.This research tackles the comparison between the manual and automated
penetration testing, the main tools used in penetration testing. Additionally, compares between
some methodologies used to build an automated penetration testing platform.
CLASSIFICATION OF ALZHEIMER USING fMRI DATA AND BRAIN NETWORKcscpconf
Since the mid of 1990s, functional connectivity study using fMRI (fcMRI) has drawn increasing
attention of neuroscientists and computer scientists, since it opens a new window to explore
functional network of human brain with relatively high resolution. BOLD technique provides
almost accurate state of brain. Past researches prove that neuro diseases damage the brain
network interaction, protein- protein interaction and gene-gene interaction. A number of
neurological research paper also analyse the relationship among damaged part. By
computational method especially machine learning technique we can show such classifications.
In this paper we used OASIS fMRI dataset affected with Alzheimer’s disease and normal
patient’s dataset. After proper processing the fMRI data we use the processed data to form
classifier models using SVM (Support Vector Machine), KNN (K- nearest neighbour) & Naïve
Bayes. We also compare the accuracy of our proposed method with existing methods. In future,
we will other combinations of methods for better accuracy.
VALIDATION METHOD OF FUZZY ASSOCIATION RULES BASED ON FUZZY FORMAL CONCEPT AN...cscpconf
In order to treat and analyze real datasets, fuzzy association rules have been proposed. Several
algorithms have been introduced to extract these rules. However, these algorithms suffer from
the problems of utility, redundancy and large number of extracted fuzzy association rules. The
expert will then be confronted with this huge amount of fuzzy association rules. The task of
validation becomes fastidious. In order to solve these problems, we propose a new validation
method. Our method is based on three steps. (i) We extract a generic base of non redundant
fuzzy association rules by applying EFAR-PN algorithm based on fuzzy formal concept analysis.
(ii) we categorize extracted rules into groups and (iii) we evaluate the relevance of these rules
using structural equation model.
PROBABILITY BASED CLUSTER EXPANSION OVERSAMPLING TECHNIQUE FOR IMBALANCED DATAcscpconf
In many applications of data mining, class imbalance is noticed when examples in one class are
overrepresented. Traditional classifiers result in poor accuracy of the minority class due to the
class imbalance. Further, the presence of within class imbalance where classes are composed of
multiple sub-concepts with different number of examples also affect the performance of
classifier. In this paper, we propose an oversampling technique that handles between class and
within class imbalance simultaneously and also takes into consideration the generalization
ability in data space. The proposed method is based on two steps- performing Model Based
Clustering with respect to classes to identify the sub-concepts; and then computing the
separating hyperplane based on equal posterior probability between the classes. The proposed
method is tested on 10 publicly available data sets and the result shows that the proposed
method is statistically superior to other existing oversampling methods.
CHARACTER AND IMAGE RECOGNITION FOR DATA CATALOGING IN ECOLOGICAL RESEARCHcscpconf
Data collection is an essential, but manpower intensive procedure in ecological research. An
algorithm was developed by the author which incorporated two important computer vision
techniques to automate data cataloging for butterfly measurements. Optical Character
Recognition is used for character recognition and Contour Detection is used for imageprocessing.
Proper pre-processing is first done on the images to improve accuracy. Although
there are limitations to Tesseract’s detection of certain fonts, overall, it can successfully identify
words of basic fonts. Contour detection is an advanced technique that can be utilized to
measure an image. Shapes and mathematical calculations are crucial in determining the precise
location of the points on which to draw the body and forewing lines of the butterfly. Overall,
92% accuracy were achieved by the program for the set of butterflies measured.
SOCIAL MEDIA ANALYTICS FOR SENTIMENT ANALYSIS AND EVENT DETECTION IN SMART CI...cscpconf
Smart cities utilize Internet of Things (IoT) devices and sensors to enhance the quality of the city
services including energy, transportation, health, and much more. They generate massive
volumes of structured and unstructured data on a daily basis. Also, social networks, such as
Twitter, Facebook, and Google+, are becoming a new source of real-time information in smart
cities. Social network users are acting as social sensors. These datasets so large and complex
are difficult to manage with conventional data management tools and methods. To become
valuable, this massive amount of data, known as 'big data,' needs to be processed and
comprehended to hold the promise of supporting a broad range of urban and smart cities
functions, including among others transportation, water, and energy consumption, pollution
surveillance, and smart city governance. In this work, we investigate how social media analytics
help to analyze smart city data collected from various social media sources, such as Twitter and
Facebook, to detect various events taking place in a smart city and identify the importance of
events and concerns of citizens regarding some events. A case scenario analyses the opinions of
users concerning the traffic in three largest cities in the UAE
SOCIAL NETWORK HATE SPEECH DETECTION FOR AMHARIC LANGUAGEcscpconf
The anonymity of social networks makes it attractive for hate speech to mask their criminal
activities online posing a challenge to the world and in particular Ethiopia. With this everincreasing
volume of social media data, hate speech identification becomes a challenge in
aggravating conflict between citizens of nations. The high rate of production, has become
difficult to collect, store and analyze such big data using traditional detection methods. This
paper proposed the application of apache spark in hate speech detection to reduce the
challenges. Authors developed an apache spark based model to classify Amharic Facebook
posts and comments into hate and not hate. Authors employed Random forest and Naïve Bayes
for learning and Word2Vec and TF-IDF for feature selection. Tested by 10-fold crossvalidation,
the model based on word2vec embedding performed best with 79.83%accuracy. The
proposed method achieve a promising result with unique feature of spark for big data.
GENERAL REGRESSION NEURAL NETWORK BASED POS TAGGING FOR NEPALI TEXTcscpconf
This article presents Part of Speech tagging for Nepali text using General Regression Neural
Network (GRNN). The corpus is divided into two parts viz. training and testing. The network is
trained and validated on both training and testing data. It is observed that 96.13% words are
correctly being tagged on training set whereas 74.38% words are tagged correctly on testing
data set using GRNN. The result is compared with the traditional Viterbi algorithm based on
Hidden Markov Model. Viterbi algorithm yields 97.2% and 40% classification accuracies on
training and testing data sets respectively. GRNN based POS Tagger is more consistent than the
traditional Viterbi decoding technique.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
2. 2 Computer Science & Information Technology (CS & IT)
complex and sophisticated machine learning techniques to create predictive and analytic models
to identify and detect and respond threats. Behavioural analytics also could transform traditional
signature-based detection techniques to the new behaviour-based predictive solutions.
According to data analysis potentials in cybersecurity, National Institute of Standards and
Technology has developed a framework consists asset risk identification (and threat
consequences), information protection, intruders detection, responding to intruders and business
recovery [3].
As a result, Data Science provides significant role in cybersecurity by utilising the power of data
(and big data), high-performance computing and data mining (and machine learning) to protect
users against cybercrimes. For this purpose, a successful data science project requires an effective
methodology to cover all issues and provide adequate resources.
In this paper, we are introducing popular data science methodologies and will compare them in
accordance with cybersecurity challenges. Section 2 describes general Data Science overview
along with its relation to the cybersecurity concept. Section 3 provides information about popular
data science methodologies in details. Four different methodologies have been explained in this
section. A comparison discussion is delivered in section 4 to explain methodologies’ strengths
and weaknesses against each other along with a summary table. In the end, we recommend a
methodology that might cover all requirements to provide the most possible efficient data science
cybersecurity project.
2. DATA SCIENCE
Data science could enhance and improve decision-making process by providing data-driven
predictions. This requires principles, processes and techniques to understand a problem through
an automated evaluation and data analysis. A successful data science has to employ data mining
algorithms to solve business problems from a data perspective [4].
Data science is a set of basic concept which leads to the principled extraction of information and
knowledge from data. It is very similar to the data mining tries to extract this information via
technologies and applied and utilised for relationship management and behaviour analysis in
order to recognize patterns, values and user interests [4]. CFJ Wu identified differences between
traditional pure statistics and modern data science practices in 1997. He described these
significant factors as Data Collection, Data Modelling and Analysis, and Problem Solving and
Decision Support [5]. Data science is a recursive process which requires iterative performing.
Fayyad says Data Mining is a component of knowledge discovery in database process [6]. The
knowledge discovery in database (KDD) was composed in 1989 to refer to the wide practice of
obtaining and acquiring knowledge in data to stress the high-level application of certain data
mining techniques. According to Fayyad et al. definition, KDD is the process of utilising data
mining techniques to draw out knowledge based on the specification of measures and threshold,
making use of a database together with any necessary pre-processing, sampling and transforming
the database [7]. Therefore, a knowledge discovery process requires at least, Selection, Pre-
processing, Transformation, Data mining, Interpretation and Evaluation steps. Knowledge
discovery in database in cybersecurity domain interpreted into two major concepts. These
concepts are User Data Discovery (UDD) with is a user profiling process and Data-Driven
Decision-making which is a decision-making process based on data analysis [8].
3. Computer Science & Information Technology (CS & IT) 3
2.1. Data-Driven Decision-making
Data-driven decision-making (DDD) is the term for the process and technique of taking decisions
based on data analysis and information evaluation rather than strictly on intuition [4].
DDD is not a binary practice to provide all or nothing. It could be employed in cybersecurity
domain with different levels of engagement. Provost et al. demonstrate two types of decisions: 1)
decisions which are based on data discovery 2) decisions which are based on frequent decision-
making processes particularly at considerable dimensions or massive scale. This kind of decision-
making processes might gain from an even minor increase in reliability and precision based on
information evaluation and data analysis [4].
Figure 1 describes data science and data-driven decision-making relation. Data Science overlaps
data-driven decision-making because cybersecurity decisions and choices could increasingly be
created instantly and automatically by computer systems [4].
Figure (1): Data-Drive Decision-making through Data Science
Data processing and data engineering are essential to support data science tasks and very
beneficial for data-driven decision-making, effective transaction processing and online pattern
recognition. Big Data is simply a term for datasets which are very large for conventional data
processing and require new methods and technologies. Therefore, big data technologies are in fact
utilised for applying data processing to support data mining strategies and data-driven decision-
making tasks [4]. Modern efficient cybersecurity solutions are depend on big data because more
data creates more accurate and precise analysis [1].
Data analytic thinking is a crucial element of data science. Underlying the comprehensive
collection of methods and strategies for mining information is a significantly smaller set of basic
concepts comprising data science. Understanding the essential concepts and having a data
analytic thinking structure and framework could help cybersecurity researchers to boost data-
driven decision-making process.
2.2. User Data Discovery
User Data Discovery (UDD) is the process of producing profile of users from historical
information and past details. This particular information might be personal data, academic
documents, geographical details or other private activities.
The primary function of user profiling process is capturing user’s information about interest
domain. These information may be used to understand more about individual’s knowledge
and skills and to improve user satisfaction or help to make a proper decision. Typically, it
evolves data mining techniques and machine learning strategies. UDD process is a type of
4. 4 Computer Science & Information Technology (CS & IT)
knowledge discovery in database or the new version, knowledge data discovery model and
requires similar steps to be established [8].
User profiling is usually either knowledge-based or behaviour based. The knowledge-based
strategy uses statistical models to categorise a user in the closest model based on dynamic
attributes. Typically, Questionnaires and interviews could be utilised to acquire this
particular user knowledge [9].
Behaviour-based strategy employs the user’s behaviours and actions as a model to observe
beneficial patterns by applying machine learning techniques. These behaviours could be
extracted through monitoring and logging tasks [9].
Recognizing user behaviour in real time is an important element of providing appropriate
information and help to take suitable action or decision in cybersecurity projects. Typically
it is a human task that experts would provide in the information security domain, but it is
possible to employ user modelling to make this process automatic by using an application or
intelligent agent [10].
UDD could obtain appropriate, adequate and accurate information about a user’s interests
and characteristics and demonstrate them with minimal user intervention [11] to offer
appropriate awareness with relevant mitigation recommendation based on the security situation.
An intelligent cybersecurity solution should take into account the various attributes and features
of a user and a security situation to create a customized solution based on the notion and concept
of user profile [12].
3. DATA SCIENCE METHODOLOGY
Several theoretical and empirical researchers have considered basic concepts and principles
of knowledge extraction from data. These basic methods and fundamental principles are
concluded from numerous data analytic studies [4].
Extracting beneficial knowledge from data should be dealt with systematic processes and
procedures through well-defined steps.
Data science needs attentive consideration and result evaluation in the context it is used
because the extracted knowledge is significant to assist the decision-making process in a
particular application [4].
“Breaking the business problem up into components corresponding to estimating
probabilities and computing or estimating values, along with a structure for recombining the
components, is broadly useful.” [4].
The correlation finding is one of the data science concepts which should be considered in
relation to the cybersecurity. It typically provides details on data items that supply
information about other data items, particularly, recognized quantities which reduce the
uncertainty of unknown quantities [4].
Entities which are identical with regard to known features or attributes, oftentimes are
identical with regard to unknown features or attributes. Computing similarity (pattern
recognition) is among the primary resources of data science [4]. It is also significant to pay
quite close attention to the existence of confounding elements, most likely unseen ones.
5. Computer Science & Information Technology (CS & IT) 5
A methodology is a general approach that guides the techniques and activities within a
specific domain. The methodology does not rely on certain technologies or tools. Instead, a
methodology delivers a framework to acquire results by using a wide range of methods,
processes and heuristics [13].
Predictive model creation, pattern recognition and underlying discovery problems through
data analysis are usually a standard practice. Data science provides plenty of evolving data
analysis technologies to constructing these models. Emerging analytics methods and action
automation provide strong machine learning models to solve sophisticated analytic problems
such as DDDs. To create an appropriate data analytic model it is required to use a data
science methodology that could provide and supply a guiding strategy regardless of
technology, data volumes or approaches.
There are several methodologies available for data mining and data science problems such as
CRISP-DM and SAS SEMMA and KDD process but Gregory Piatetsky confirms CRISP-DM
remains the top methodology for data mining projects with 42% in 2014. The KDD Process
has used by 7.5% [14].
Rollins demonstrates a foundational methodology which is similar to CRISP-DM, SEMMA and
KDD Process but also emphasizes a number of new methods in data science including big data
usage, the incorporation of text analytics into predictive modelling and process automation [13].
Microsoft also introduces Team Data Science Process (TDSP) which recommends a lifecycle for
data science projects [15].
Before applying any of these methodologies to cybersecurity projects, it might be helpful to
review and compare their essential features. For this reason, this paper provides a comparison
between KDD Process, CRISP-DM, TDSP and the foundational methodology for data science
(FMDS). FMDS and CRISP-DM have been chosen, because they are considered to be the most
popular but SAS SEMMA is not in this review because there is a big decline in applying it
(from 13% in 2007 to 8.5% in 2014) [14]. The KDD Process has also included because it
provides initial and basic requirements of knowledge discovery. TDSP has been chosen because it
is customized for machine learning or artificial intelligence projects which are considerably
linked to cybersecurity applications.
3.1. KDD Process
The KDD process offered by Fayyad et al. in 1996 [7]. It is the method of using data mining
techniques to extract knowledge based on particular measures and thresholds in a database by
employing any necessary pre-processing, sampling or data transformation actions [7].
Furthermore, the application domain perception is needed to be considered during the KDD
process development, improvement and enhancement. Figure 2 illustrates the KDD process.
The KDD process has 5 steps as the following [7].
1. Selection: It means generating or producing a target data set or concentrating on a subset of
variables or data samples in a database.
2. Pre-processing: This phase tries to obtain consistent data by cleaning or pre-processing selected
data.
3. Transformation: Reducing feature dimensionality by using data transformation methods is
performing in this phase.
6. 6 Computer Science & Information Technology (CS & IT)
4. Data Mining: Trying to recognize patterns of attention or behaviours by using data mining
techniques in a specific form should perform in this step. (Typically, prediction)
5. Interpretation/Evaluation: Mined pattern should be assessed and interpreted in the final phase.
Figure (2): The KDD Process
3.2. CRISP-DM
In 1996, SPSS and Teradata developed Cross Industry Standard Process for Data Mining (CRISP-
DM) in an effort initially composed with NCR, Daimler and OHRA. It is a cycle of six high-level
phases which describe the analytic process [16, 17].
CRISP-DM is still a beneficial tool but details and specifics needed to be updated for
cybersecurity projects such as those including Big Data. The original site is not active anymore
but IBM SPSS modeller still supports it [14].
Figure 3 demonstrates the CRISP-DM six stages, but their sequence is not strict. CRISP-DM is
very well documented and there are many case studies which have used it [16].
Figure (3): The CRISP-DM life cycle
7. Computer Science & Information Technology (CS & IT) 7
CRISP-DM’s structured, well defined and extremely documented process is independent of data
mining tools and this important factor is crucial to make the project successful [16].
The CRISP-DM is so logical and seems like common sense. There are many methodologies and
advanced analytic platforms which are actually based on CRISP-DM steps because the use of a
commonly practised methodology gains quality and efficiency. Vorhies says CRISP-DM provides
strong guidance for even the most advanced of today’s data science projects [17].
The six phases are the following [16]:
1. Business understanding: It is designed to focus on understanding the problem or project goals
and requirements from a business perspective (here is a cybersecurity application) and then
transforming this perception into data mining problem description and preliminary approach.
2. Data Understanding: This phase begins with an initial data collection and then with tasks in
order to acquaint with data, to recognise data quality problems, to find out primarily insights into
the details or even to identify interesting subsets to develop hypotheses for hidden information. It
typically creates a set of raw data.
3. Data Preparation: This phase covers all tasks and activities to build the final required dataset
from the first raw data.
4. Modelling: Through this stage, modelling techniques and strategies are selected and applied,
and their specific parameters and prerequisites should be identified and calibrated regarding the
type of data to optimal values.
5. Evaluation: At this point, the obtained model or models which seem to provide high quality
based on loss function completely evaluated and the actions executed to ensure they generalise
against hidden data and to be certain it correctly archives the key business goals. The final result
is the selection of sufficient model or models.
6. Deployment: This stage means deploying a code representation of the final model or models in
order to evaluate or even categorise new data as it arises to generate a mechanism for the use of
new data in the formula of the first problem. Even if the goal of the model is to provide
knowledge or to understand the data, the knowledge acquired have to be organised, structured and
presented in a means which could be used. This includes all the data preparation and required
steps which are needed to treat raw data to achieve the final result in the same way as developed
during model construction.
3.3. Foundational Methodology for Data Science
This methodology has some similarities and consists many features of KDD Process and CRISP-
DM, but in addition, it provides a number of new practices such as use of extremely large data
volumes, text and image analytics, deep learning, artificial intelligence and language processing
[13]. The FMDS’s ten steps illustrate an iterative nature of the problem-solving process for
utilising data to discover security insights. Figure 4 demonstrates FMDS process.
Foundational Methodology for Data Science consists the following steps [18]:
1. Business understanding: This very first phase provides a basic foundation for a profitable and
effective resolution of a business problem (a cybersecurity challenge in here) regardless of its size
and complexity
8. 8 Computer Science & Information Technology (CS & IT)
Figure (4): Foundational Methodology for Data Science
2. Analytic approach: As soon as the problem clearly stated, it is required to determine an analytic
approach by identifying suitable machine learning technique to solve it and obtain the desired
result.
3. Data requirements: The analytic approach that has been chosen in the second phase, defines
needed data requirements including specific data content, formats and representations which are
instructed by cybersecurity knowledge.
4. Data collection: In this primary data gathering phase, it is required to identify and collect
available data resources (structured, unstructured and semi-structured) which are related and
applicable to the problem domain. It is important to cover any data collection gap by revising data
requirements and gathering brand new and/or additional data.
It is also significant to use high-performance platforms and in-database analytic functionality to
work with huge datasets for sampling and sub-setting to obtain all available data.
5. Data understanding: Descriptive statistics and visualisation methods are useful in this phase to
understand data content, evaluate data quality and explore data insights. This could be required to
revise the earlier phase to close data collection gaps.
6. Data preparation: This phase comprises all tasks to construct dataset which will be utilised in
the modelling phase. These tasks include data cleaning, data merging from several sources,
dealing with missing data, data transformation into more useful variables, duplication elimination,
and finding invalid values. In addition, feature engineering and text analytics are possible to be
utilised to provide new structured variables, defining and enriching the predictors and boosting or
improving the model’s reliability, accuracy and precision. A collaboration of cybersecurity
knowledge and existing structured variables could be very useful for feature engineering. This
phase is probably the most time-consuming stage, but high-performance and parallel computing
systems could reduce the time required and prepare data quickly from huge datasets.
7. Modelling: This phase concentrate on predictive or descriptive model development based on
earlier described analytic approach and by using the first version of the prepared dataset as a
training set (historical data). The modelling process is extremely iterative as it provides
9. Computer Science & Information Technology (CS & IT) 9
intermediate insights and reputable refinement of data preparation and model specification. It is
significantly helpful to try several algorithms with specific parameters to find the ideal model.
8. Evaluation: Before deployment, it is crucial to evaluate the quality and efficacy of the
developed model to realise whether it completely and appropriately addresses the cybersecurity
problem. This evaluation involves computing of several different diagnostic measures and other
outputs including tables and graphs by using a testing set. This testing set is actually independent
of the training set but follows the identical probability distribution and has known results.
9. Deployment: Once the developed model approved and accredited in the evaluation phase that
covers the cybersecurity challenge appropriately, it should be deployed into the production
environment or perhaps in a comparable test environment. Typically, it will be used in a limited
and specific way until all performance variables entirely evaluated. Deployment could be as basic
as producing a simple report with proper suggestions or perhaps, embedding the model in an
elaborated or sophisticated workflow and scoring process handled by a customised application.
10. Feedback: This final phase, collects outcomes from the implemented edition of the analytic
model to analyse and feedback its functionality, performance and efficiency in accordance with
the deployment environment.
3.4. Team Data Science Process
The Team Data Science Process (TDSP) is a data science methodology to provide efficient
predictive analytics. TDSP These solutions are including artificial intelligence and machine
learning. It boots data science project agility, team working and learning by employing best
practices and successful structures from Microsoft [15]. TDSP supports both exploratory and ad-
hoc projects. Figure 5 illustrates TDSP five stages.
Figure (5): Team Data Science Process Lifecycle
TDSP supports development of projects which have already employed CRISP-DM and KDD
process. It is very customizable based on project’s size and dimensions [19].
The TDSP lifecycle consists integrative phases as the following [19].
10. 10 Computer Science & Information Technology (CS & IT)
1. Business Understanding: Initially, a question which describes the problem objectives should be
defined clearly and explicitly. Relevant predictive model and required data source/s also have to
be identified in this step.
2. Data Acquisition and Understanding: Data collection starts in this phase by transferring data
into the target location to be utilised by analytic operations. The raw data is needed to be cleaned
and incomplete to incorrect values should be identified. Data summarization and visualization
might help to find required cleaning procedures. Data visualization also could help to measure if
data features and collected amount of data are adequate over the time period. At the end of this
stage, it might be necessary to go back to the first step for more data collection.
3. Modelling: Feature engineering and model training are two elements of this phase. Feature
engineering provides attributes and data features which are required for machine learning
algorithm. Algorithm selection, model creation and predictive model evaluation are also sub-
components of this step. Collected data should be divided into training and testing datasets to
train and evaluate machine learning model. It is important to employ different algorithms and
parameters to find the best suitable solution to support the problem.
4. Deployment: Predictive model and data pipeline are needed to be produced in this step. It could
be a real-time or a batch analysis model depends on the required application. The final data
product should be accredited by the customer.
5. Customer Acceptance: The final phase is customer acceptance which should be performed by
confirming data pipeline, predictive model and product deployment.
4. DISCUSSION
All data science methodologies where discussed consist four common iterative stages including
problem definition/ formulation, data gathering, data modelling and data product development
except the KDD process.
Figure (6): General Data Science Methodology
According to the above explanations, table 1 demonstrates a summary of the presented
correspondences.
Comparing the KDD process with the CRISP-DM presents that KDD process does not cover the
business understanding and also deployment phases in CRISP-DM methodology.
As it is mentioned above, the business understanding phase provides a comprehension perception
of the application domain and pertinent prior knowledge and also objectives of required solution.
Furthermore, the deployment phase incorporates knowledge or modelling code into a system or
11. Computer Science & Information Technology (CS & IT) 11
application to build a data product. These two significant phases have been missed in KDD
process.
Table (1): Summary of Data Science methodologies and their phases
KDD Process CRISP-DM FMDS TDSP
-
Business Understanding Business Understanding
Business Understanding- Analytic Approach
- Data Requirements
Selection
Data Understanding
Data Collection
Data Acquisition and
Understanding
Pre Processing Data Understanding
Transformation Data Preparation Data Preparation
Data Mining Modelling Modelling
ModellingInterpretation/
Evaluation
Evaluation Evaluation
- Deployment Deployment Deployment
- - Feedback Customer Acceptance
Comparing the CRISP-DM with the FMDS illustrates that CRISP-DM has not the analytic
approach and the feedback phases. The analytic approach is required to recognize appropriate
statistical or machine learning techniques before entering to the data gathering steps. This phase
could be very useful to identify the suitable data collection strategy and data resources but it is
missed in the CRISP-DM methodology. The feedback phase also has been missed that is very
beneficial to optimize the system to achieve high-performance functionality and efficient result.
A comparison between FMDS and TDSP presents that they are very similar but FMDS has more
details steps in general. Feedback also is a part of FMDS cycle which could create new
requirements to improve the data product in an iterative process but customer acceptance is a
stage out of the circle in the TDSP. FMDS detailed stages could be more useful for a wide range
of projects but TDSP uses a specific set of Microsoft tools and infrastructure to deliver intelligent
applications by deploying machine learning or AI models.
Concerning the remaining phases present the following:
• The Data understanding phase is in both CRSIP-DM and FMDS can be recognized as the
collaboration of Selection (Collection) and Pre-Processing phases in the KDD process but
Data Acquisition and Understanding in TDSP also covers Transformation stage in the
KDD process. However, data requirements which are related to the analytic approach
phase and provides required data content are missed in both KDD process and CRISP-
DM. Selecting an analytic approach is integrated into the business understanding phase of
TDSP but cybersecurity projects might gain more benefits in details from this task when
it is an independent step in the FMDS particularly when data resources are separated and
requires a different method or level of access.
• Business understanding and problem formulation is an initial phase that makes the data
understanding phase more efficient by recommending data formats and representations
but data requirement analysis is missed in the CRISP-DM. It might be very crucial in
cybersecurity projects particularly when data resources are unstructured or semi-
structured.
• The data preparation phase has the similar function as the transformation phase in KDD
process and it is included in the Data Acquisition and Understanding phase of TDSP by
using some tools.
12. 12 Computer Science & Information Technology (CS & IT)
• The modelling phase might be recognised with data mining phase which is very limited
in the KDD process. Modelling phase in TDSP also has the evaluation task included by
using some tools such as scoring and performance monitoring, but it is an independent
phase in other methodologies.
• The evaluation phase is also included in all three methodologies.
In spite of CRISP-DM strengths, there is more emphasis that should be considered for modern
cybersecurity projects.
• It is required that methodology could also handle data blending from several sources.
This should be deployed through a completely repeatable process. FMDS and TDSP
provide this feature in the Data Preparation and Data Acquisition and Understanding
phases. TDSP provides Microsoft tools to support On-Premises, Cloud, Databases and
Files but FDMS is independent of any platform or a specific tool. This might makes
cybersecurity projects more reliable and efficient.
• Choosing the most appropriate degree of reliability and accuracy for the problem is very
crucial to make sure there is no need to spend excessive time on data preparation or
modelling to enhance accuracy when it could not be utilised. This feature also included in
the FMDS methodology.
• The entire analytic algorithm should be tested and evaluated to make sure there are
working in all situations and not just for sample modeller. The evaluation, deployment
and feedback cycle in the FMDS could provide this requirement as well as Model
training task of Modelling phase in the TDSP. Feedback phase in FMDS might create
new data science questions to optimize the cybersecurity product or make new
functionalities for it.
• It is significant to consider quality during model simplification by ensuring that decision
elements such as missing data reduction, synthetic features generation and unseen data
holdout are properly managed. The evaluation, deployment and feedback cycle in the
FMDS could bring this need better than simple quality insurance in the evaluation phase
of CRISP-DM.
• Data science lifecycle is very well defined in the FMDS and connections are clearly
determined between every stage but TDSP’s stages are all linked together (except
customer acceptance) and it is possible to move into any stages from anyone else.
• TDSP lifecycle is designed for predictive analytic projects by using machine learning or
artificial intelligence models. FMDS is more general and independent of any platform,
tool, model or algorithm. Both are functional for exploratory or ad-hoc cybersecurity
analytics projects by some customization.
5. CONCLUSION
In conclusion, a cybersecurity data science project requires four general steps. The first step has
to be a problem definition by formulating a security challenge. In accordance with problem
definition and appropriate formula, it is necessary to gather required information in the second
step. The collected information should be employed in the third step and in an analysis process to
provide adequate data which is expected to predict or provide a resolution for the defined
13. Computer Science & Information Technology (CS & IT) 13
problem. The final step is a production step which deploys relevant modules and a system to run
the whole process automatically and regularly when it is needed.
Figure (7): FMDS for Cybersecurity projects
Regarding the general process and in accordance to Table 1, the FMDS covers all beneficial
attributes of CRISP-DM methodology but fills data gathering gaps and also provides extra steps
to optimize and enhance the model and results by mathematical prescriptive analytics and using
high-performance computing. It is also platform and tool independent but TDSP is not. Because it
is designed in details with clearly divided steps, it could be fully customized to fit in any
cybersecurity project.
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