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The document describes a dangerous location aware system to assist with children's safety. The system collects context data from the child's device and other sources, analyzes situations to detect potential dangers, and provides responses and interfaces to present information to parents and children. It consists of three main functions: situation analysis and decision making, context acquisition and representation, and response, action, and presentation. A prototype was created with graphical user interfaces for parents and children.
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Feature extraction and feature selection are the first tasks in pre-processing of input logs in order to detect cyber security threats and attacks while utilizing machine learning. When it comes to the analysis of heterogeneous data derived from different sources, these tasks are found to be time-consuming and difficult to be managed efficiently. In this paper, we present an approach for handling feature extraction and feature selection for security analytics of heterogeneous data derived from different network sensors. The approach is implemented in Apache Spark, using its python API, named pyspark.
1Project 2 DeliverablesSecurity Assessment Report (SAR)I.
1
Project 2 Deliverables
Security Assessment Report (SAR)
I. Title Page
II. Abstract
III. Organization
a. Purpose
b. Organizational structure
c. Network system description
d. Diagram of the organization (LAN, WAN, intranet, extranet, internet)
e. Identify system boundaries (inner networks separated from outside networks)
IV. Enterprise Threats
a. Internal Threats
b. External Threats
c. Threat Intelligence
i. OPM Breach
ii. OPM vs Your Organization
1. Differences
2. Similarities
d. Security Issues in the Organization
V. Scanning
a. Lab Results
VI. Relational Database Management System (RDBMS)
a. Role of Firewalls
b. Encryption Methods Used in Firewalls
c. RDBMS Auditing
i. Describe the auditing techniques and how it protects the security objectives of confidentiality, integrity, and availability
VII. Threat Identification
a. Organization Cyber Attacks
b. Remediation & Mitigation Techniques
i. Access Control
ii. Database Transaction
iii. Firewall Log Files
iv. Encryption
1. Purpose
2. Function
VIII. Conclusion
IX. References
Risk Assessment Report (RAR)
I. Title Page
II. Abstract
III. Risk and Remediation
a. Organizational Risk (NIST, 2012)
b. Organizational Remediation Efforts
c. Organizational Threats/Vulnerabilities
i. Likelihood of occurring
ii. Impact to organization
d. Cost/Benefit Analysis of Remediation Efforts
IV. Plan of Actions & Milestones (POA&Ms)
a. Creation
b. Monitoring
c. Closing
V. Conclusion
VI. References
References
1. Scarfone, K., & Hoffman, P. (2009). Guidelines on firewalls and firewall policy: Recommendations of the National Institute of Standards and Technology. (Special Publication 800-41). U.S. Department of Commerce, National Institute of Standards and Technology. Retrieved August 5, 2016, from http://csrc.nist.gov/publications/nistpubs/800-41-Rev1/sp800-41-rev1.pdf
2. U.S. Department of Commerce, National Institute of Standards and Technology (NIST). (2012). Information security: Guide for conducting risk assessments (Special Publication 800-30). Retrieved August 5, 2016, from http://nvlpubs.nist.gov/nistpubs/Legacy/SP/nistspecialpublication800-30r1.pdf
3. http://2012books.lardbucket.org/books/getting-the-most-out-of-information-systems-v1.3/index.html
4. Introduction to parallel & distributed algorithms by Carl Burch, Hendrix College, August 2009
5. https://lti.umuc.edu/contentadaptor/topics/byid/820a901d-e710-4e8c-9b19-8aaf41baf091
6. Singhal, A., Winograd, T., & Scarfone, K. (2007). Computer security: Guide to secure web services: Recommendations of the National Institute of Standards and Technology (Special Publication 800-95). Retrieved from http://csrc.nist.gov/publications/nistpubs/800-95/SP800-95.pdf
7. https://www.computer.org/cms/CYBSI/docs/Top-10-Flaws.pdf
8. https://umuc.equella.ecollege.com/file/6aa8bfb8-7053-4fed-94f6-2547e454c501/1/web/viewer.html?file=https://umuc.equella.ecollege.com/file/830d820d-c407-49df-ab83-2886fd3a7cbf/1/NISTCloudComputingStandardsRoadmap.pdf
9. ...
1Project 2 DeliverablesSecurity Assessment Report (SAR)I.
1
Project 2 Deliverables
Security Assessment Report (SAR)
I. Title Page
II. Abstract
III. Organization
a. Purpose
b. Organizational structure
c. Network system description
d. Diagram of the organization (LAN, WAN, intranet, extranet, internet)
e. Identify system boundaries (inner networks separated from outside networks)
IV. Enterprise Threats
a. Internal Threats
b. External Threats
c. Threat Intelligence
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ii. OPM vs Your Organization
1. Differences
2. Similarities
d. Security Issues in the Organization
V. Scanning
a. Lab Results
VI. Relational Database Management System (RDBMS)
a. Role of Firewalls
b. Encryption Methods Used in Firewalls
c. RDBMS Auditing
i. Describe the auditing techniques and how it protects the security objectives of confidentiality, integrity, and availability
VII. Threat Identification
a. Organization Cyber Attacks
b. Remediation & Mitigation Techniques
i. Access Control
ii. Database Transaction
iii. Firewall Log Files
iv. Encryption
1. Purpose
2. Function
VIII. Conclusion
IX. References
Risk Assessment Report (RAR)
I. Title Page
II. Abstract
III. Risk and Remediation
a. Organizational Risk (NIST, 2012)
b. Organizational Remediation Efforts
c. Organizational Threats/Vulnerabilities
i. Likelihood of occurring
ii. Impact to organization
d. Cost/Benefit Analysis of Remediation Efforts
IV. Plan of Actions & Milestones (POA&Ms)
a. Creation
b. Monitoring
c. Closing
V. Conclusion
VI. References
References
1. Scarfone, K., & Hoffman, P. (2009). Guidelines on firewalls and firewall policy: Recommendations of the National Institute of Standards and Technology. (Special Publication 800-41). U.S. Department of Commerce, National Institute of Standards and Technology. Retrieved August 5, 2016, from http://csrc.nist.gov/publications/nistpubs/800-41-Rev1/sp800-41-rev1.pdf
2. U.S. Department of Commerce, National Institute of Standards and Technology (NIST). (2012). Information security: Guide for conducting risk assessments (Special Publication 800-30). Retrieved August 5, 2016, from http://nvlpubs.nist.gov/nistpubs/Legacy/SP/nistspecialpublication800-30r1.pdf
3. http://2012books.lardbucket.org/books/getting-the-most-out-of-information-systems-v1.3/index.html
4. Introduction to parallel & distributed algorithms by Carl Burch, Hendrix College, August 2009
5. https://lti.umuc.edu/contentadaptor/topics/byid/820a901d-e710-4e8c-9b19-8aaf41baf091
6. Singhal, A., Winograd, T., & Scarfone, K. (2007). Computer security: Guide to secure web services: Recommendations of the National Institute of Standards and Technology (Special Publication 800-95). Retrieved from http://csrc.nist.gov/publications/nistpubs/800-95/SP800-95.pdf
7. https://www.computer.org/cms/CYBSI/docs/Top-10-Flaws.pdf
8. https://umuc.equella.ecollege.com/file/6aa8bfb8-7053-4fed-94f6-2547e454c501/1/web/viewer.html?file=https://umuc.equella.ecollege.com/file/830d820d-c407-49df-ab83-2886fd3a7cbf/1/NISTCloudComputingStandardsRoadmap.pdf
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Perdidos
El vuelo 815 de Oceanic Airlines se estrella en una remota isla del Pacífico después de desviarse de su ruta a Los Ángeles. Los pasajeros supervivientes, entre los que se encuentran Jack Shephard, Kate Austen y Sawyer, deben permanecer unidos para sobrevivir en la isla, donde ocurren fenómenos extraños. Jack emerge como líder provisional del grupo. Otros personajes incluyen a Charlie Pace, Sayid, Claire Littleton y Henry Gale, quienes tienen sus propias historias y motivos para estar en el avión.
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Feature extraction and feature selection are the first tasks in pre-processing of input logs in order to detect cyber security threats and attacks while utilizing machine learning. When it comes to the analysis of heterogeneous data derived from different sources, these tasks are found to be time-consuming and difficult to be managed efficiently. In this paper, we present an approach for handling feature extraction and feature selection for security analytics of heterogeneous data derived from different network sensors. The approach is implemented in Apache Spark, using its python API, named pyspark.
1Project 2 DeliverablesSecurity Assessment Report (SAR)I.
1
Project 2 Deliverables
Security Assessment Report (SAR)
I. Title Page
II. Abstract
III. Organization
a. Purpose
b. Organizational structure
c. Network system description
d. Diagram of the organization (LAN, WAN, intranet, extranet, internet)
e. Identify system boundaries (inner networks separated from outside networks)
IV. Enterprise Threats
a. Internal Threats
b. External Threats
c. Threat Intelligence
i. OPM Breach
ii. OPM vs Your Organization
1. Differences
2. Similarities
d. Security Issues in the Organization
V. Scanning
a. Lab Results
VI. Relational Database Management System (RDBMS)
a. Role of Firewalls
b. Encryption Methods Used in Firewalls
c. RDBMS Auditing
i. Describe the auditing techniques and how it protects the security objectives of confidentiality, integrity, and availability
VII. Threat Identification
a. Organization Cyber Attacks
b. Remediation & Mitigation Techniques
i. Access Control
ii. Database Transaction
iii. Firewall Log Files
iv. Encryption
1. Purpose
2. Function
VIII. Conclusion
IX. References
Risk Assessment Report (RAR)
I. Title Page
II. Abstract
III. Risk and Remediation
a. Organizational Risk (NIST, 2012)
b. Organizational Remediation Efforts
c. Organizational Threats/Vulnerabilities
i. Likelihood of occurring
ii. Impact to organization
d. Cost/Benefit Analysis of Remediation Efforts
IV. Plan of Actions & Milestones (POA&Ms)
a. Creation
b. Monitoring
c. Closing
V. Conclusion
VI. References
References
1. Scarfone, K., & Hoffman, P. (2009). Guidelines on firewalls and firewall policy: Recommendations of the National Institute of Standards and Technology. (Special Publication 800-41). U.S. Department of Commerce, National Institute of Standards and Technology. Retrieved August 5, 2016, from http://csrc.nist.gov/publications/nistpubs/800-41-Rev1/sp800-41-rev1.pdf
2. U.S. Department of Commerce, National Institute of Standards and Technology (NIST). (2012). Information security: Guide for conducting risk assessments (Special Publication 800-30). Retrieved August 5, 2016, from http://nvlpubs.nist.gov/nistpubs/Legacy/SP/nistspecialpublication800-30r1.pdf
3. http://2012books.lardbucket.org/books/getting-the-most-out-of-information-systems-v1.3/index.html
4. Introduction to parallel & distributed algorithms by Carl Burch, Hendrix College, August 2009
5. https://lti.umuc.edu/contentadaptor/topics/byid/820a901d-e710-4e8c-9b19-8aaf41baf091
6. Singhal, A., Winograd, T., & Scarfone, K. (2007). Computer security: Guide to secure web services: Recommendations of the National Institute of Standards and Technology (Special Publication 800-95). Retrieved from http://csrc.nist.gov/publications/nistpubs/800-95/SP800-95.pdf
7. https://www.computer.org/cms/CYBSI/docs/Top-10-Flaws.pdf
8. https://umuc.equella.ecollege.com/file/6aa8bfb8-7053-4fed-94f6-2547e454c501/1/web/viewer.html?file=https://umuc.equella.ecollege.com/file/830d820d-c407-49df-ab83-2886fd3a7cbf/1/NISTCloudComputingStandardsRoadmap.pdf
9. ...
1Project 2 DeliverablesSecurity Assessment Report (SAR)I.
1
Project 2 Deliverables
Security Assessment Report (SAR)
I. Title Page
II. Abstract
III. Organization
a. Purpose
b. Organizational structure
c. Network system description
d. Diagram of the organization (LAN, WAN, intranet, extranet, internet)
e. Identify system boundaries (inner networks separated from outside networks)
IV. Enterprise Threats
a. Internal Threats
b. External Threats
c. Threat Intelligence
i. OPM Breach
ii. OPM vs Your Organization
1. Differences
2. Similarities
d. Security Issues in the Organization
V. Scanning
a. Lab Results
VI. Relational Database Management System (RDBMS)
a. Role of Firewalls
b. Encryption Methods Used in Firewalls
c. RDBMS Auditing
i. Describe the auditing techniques and how it protects the security objectives of confidentiality, integrity, and availability
VII. Threat Identification
a. Organization Cyber Attacks
b. Remediation & Mitigation Techniques
i. Access Control
ii. Database Transaction
iii. Firewall Log Files
iv. Encryption
1. Purpose
2. Function
VIII. Conclusion
IX. References
Risk Assessment Report (RAR)
I. Title Page
II. Abstract
III. Risk and Remediation
a. Organizational Risk (NIST, 2012)
b. Organizational Remediation Efforts
c. Organizational Threats/Vulnerabilities
i. Likelihood of occurring
ii. Impact to organization
d. Cost/Benefit Analysis of Remediation Efforts
IV. Plan of Actions & Milestones (POA&Ms)
a. Creation
b. Monitoring
c. Closing
V. Conclusion
VI. References
References
1. Scarfone, K., & Hoffman, P. (2009). Guidelines on firewalls and firewall policy: Recommendations of the National Institute of Standards and Technology. (Special Publication 800-41). U.S. Department of Commerce, National Institute of Standards and Technology. Retrieved August 5, 2016, from http://csrc.nist.gov/publications/nistpubs/800-41-Rev1/sp800-41-rev1.pdf
2. U.S. Department of Commerce, National Institute of Standards and Technology (NIST). (2012). Information security: Guide for conducting risk assessments (Special Publication 800-30). Retrieved August 5, 2016, from http://nvlpubs.nist.gov/nistpubs/Legacy/SP/nistspecialpublication800-30r1.pdf
3. http://2012books.lardbucket.org/books/getting-the-most-out-of-information-systems-v1.3/index.html
4. Introduction to parallel & distributed algorithms by Carl Burch, Hendrix College, August 2009
5. https://lti.umuc.edu/contentadaptor/topics/byid/820a901d-e710-4e8c-9b19-8aaf41baf091
6. Singhal, A., Winograd, T., & Scarfone, K. (2007). Computer security: Guide to secure web services: Recommendations of the National Institute of Standards and Technology (Special Publication 800-95). Retrieved from http://csrc.nist.gov/publications/nistpubs/800-95/SP800-95.pdf
7. https://www.computer.org/cms/CYBSI/docs/Top-10-Flaws.pdf
8. https://umuc.equella.ecollege.com/file/6aa8bfb8-7053-4fed-94f6-2547e454c501/1/web/viewer.html?file=https://umuc.equella.ecollege.com/file/830d820d-c407-49df-ab83-2886fd3a7cbf/1/NISTCloudComputingStandardsRoadmap.pdf
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Resume
Yuanzhe Cai is seeking a full-time software engineer position. He has a Ph.D. in computer science from UT Arlington and experience developing database, big data, and social network analysis projects. His research focused on inferring answer quality and expertise in question/answer communities. He has strong skills in Java, databases, and data mining tools.
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alarm signal is raised.
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SafeAssign Originality Report
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%74Total Score: High riskVenkatesh Bodhupally
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Venkatesh Bodhupally
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Some of the tools applicable in the collection of live memory images in media include; volatility suite (Htun, Thwin & San, 2018). This tool or program analyzes the
RAM and has support from different operating systems such as Linux and windows. RAW and VMWare are also analyzable by this tool, with no issues arising. Rekall is
a tool used by investigators and responders since it features in analyzing other tools and acquiescing them. It's not a single application but a forensic framework
(Socała & Cohen, 2016). Helix ISO, a live disk that helps in capturing of memory images in a system and memory dumping. This type of tool has some risks associated
with it that make it not able to run directly into a system such as acquisition footprint Other tools include; process hacker which is an application that monitors
application, and it can be run when the machine that is on target is on use. The tool makes an investigator understand the issue affecting the system before a
snapshot of the memory is taken (Eden, Pontypridd, Cherdantseva, & Stoddart, 2016). The tool can also help in uncovering processes that are malicious and in
identifying terminated processes in a set period. Investigators also use or can use Belk soft RAM capture, which allows capturing of the volatile section of system
memory into a file. Belksoft RAM capture is a criminology device that has a free unpredictable memory, and it is used in catching the live RAM. Belksoft RAM capture
has drivers worth 32-bit and 64-bit; that's why this tool is used in overcoming anti-debugging as well as anti-dumping systems. Ftk Imager is a tool that catches the live
RAM. At a time picture, this type of tool makes a tiny bit alongside slack space. This type of tool is not capable of dividing or dissecting the memory dump that is
caught (Venkateswara Rao, & Ch.
6950SafeAssign Originality ReportDigital Fore.docx
%69
%5
%0
SafeAssign Originality Report
Digital Forensics Tools & Tchq - 202040 - CRN127 - Rucker • Week Eight Assignment
%74Total Score: High riskVenkatesh Bodhupally
Submission UUID: 680cd83f-65c1-b609-7c13-c42c95f8db1c
Total Number of Reports
1
Highest Match
74 %
forensictools.docx
Average Match
74 %
Submitted on
04/30/20
05:27 PM EDT
Average Word Count
564
Highest: forensictools.docx
%74Attachment 1
Institutional database (2)
Student paper Student paper
Scholarly journals & publications (2)
ProQuest document ProQuest document
Internet (1)
dfrws
Top sources (3)
Excluded sources (0)
View Originality Report - Old Design
Word Count: 564
forensictools.docx
2 5
4 3
1
2 Student paper 4 ProQuest document 5 Student paper
https://blackboard.nec.edu/webapps/mdb-sa-BB5b75a0e7334a9/originalityReport?attemptId=4b21db19-c753-4a4c-bf5f-5fa5c168286f&course_id=_47023_1&download=true&includeDeleted=true&print=true&force=true
Source Matches (13)
dfrws 66%
Student paper 100%
Student paper 100%
Running Head: INVESTIGATIONS AND FORENSICS 1
INVESTIGATIONS AND FORENSICS 4
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Venkatesh Bodhupally
NEC.
Some of the tools applicable in the collection of live memory images in media include; volatility suite (Htun, Thwin & San, 2018). This tool or program analyzes the
RAM and has support from different operating systems such as Linux and windows. RAW and VMWare are also analyzable by this tool, with no issues arising. Rekall is
a tool used by investigators and responders since it features in analyzing other tools and acquiescing them. It's not a single application but a forensic framework
(Socała & Cohen, 2016). Helix ISO, a live disk that helps in capturing of memory images in a system and memory dumping. This type of tool has some risks associated
with it that make it not able to run directly into a system such as acquisition footprint Other tools include; process hacker which is an application that monitors
application, and it can be run when the machine that is on target is on use. The tool makes an investigator understand the issue affecting the system before a
snapshot of the memory is taken (Eden, Pontypridd, Cherdantseva, & Stoddart, 2016). The tool can also help in uncovering processes that are malicious and in
identifying terminated processes in a set period. Investigators also use or can use Belk soft RAM capture, which allows capturing of the volatile section of system
memory into a file. Belksoft RAM capture is a criminology device that has a free unpredictable memory, and it is used in catching the live RAM. Belksoft RAM capture
has drivers worth 32-bit and 64-bit; that's why this tool is used in overcoming anti-debugging as well as anti-dumping systems. Ftk Imager is a tool that catches the live
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Computational model for artificial learning using formal concept analysis
The document presents a computational model for artificial learning using formal concept analysis. It proposes using formal concept analysis to describe the classification process and derive classification rules. The model was tested on several datasets and showed improved accuracy over support vector machines and classification and regression trees on most datasets based on various performance metrics. ROC curves were also generated to evaluate model performance. The proposed model aims to better understand and model the classification learning processes involved in human intelligence.
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This document discusses using a random forest classifier with feature selection to improve intrusion detection. It begins with background on intrusion detection systems and challenges. It then proposes using genetic algorithms for feature selection to identify the most important features from a dataset. A random forest classifier is used for classification, which combines decision trees to improve accuracy. The methodology involves feature selection, classification with random forest, and detection. Feature weights are calculated and cross-validation is used to analyze detection rates for individual attacks. The goal is to improve accuracy, reduce training time, and better detect minority attacks through this approach.
Attack Detection Availing Feature Discretion using Random Forest Classifier
The widespread use of the Internet has an adverse effect of being vulnerable to cyber attacks. Defensive
mechanisms like firewalls and IDSs have evolved with a lot of research contributions happening in these
areas. Machine learning techniques have been successfully used in these defense mechanisms especially
IDSs. Although they are effective to some extent in identifying new patterns and variants of existing
malicious patterns, many attacks are still left as undetected. The objective is to develop an algorithm for
detecting malicious domains based on passive traffic measurements. In this paper, an anomaly-based
intrusion detection system based on an ensemble based machine learning classifier called Random Forest
with gradient boosting is deployed. NSL-KDD cup dataset is used for analysis and out of 41 features, 32
features were identified as significant using feature discretion.
Resume
Koushik Modayur Chandramouleeswaran has extensive experience developing software using various programming languages and technologies. He is pursuing a Master of Science in Computer Science from The University of Texas at Arlington with a GPA of 3.62/4.0 and holds a Bachelor of Technology in IT from Bharath University in India with a GPA of 8.44/10. He has worked as an Associate and Programmer Analyst at Cognizant Technology Solutions, developing tools and reports to help clients save over $120k. Koushik has strong skills in Python, Java, R, databases, big data technologies and machine learning. He has completed several projects applying techniques like decision
Toddler monitoring system in vehicle using single shot detector-mobilenet and...
Road vehicles are today’s primary form of transportation; the safety of children passengers must take precedence. Numerous reports of toddler death in road vehicles, include heatstroke and accidents caused by negligent parents. In this research, we report a system developed to monitor and detect a toddler's presence in a vehicle and to classify the toddler's seatbelt status. The objective of the toddler monitoring system is to monitor the child's conditions to ensure the toddler's safety. The device senses the toddler's seatbelt status and warns the driver if the child is left in the car after the vehicle is powered off. The vision-based monitoring system employs deep learning algorithms to recognize infants and seatbelts, in the interior vehicle environment. Due to its superior performance, the Nvidia Jetson Nano was selected as the computational unit. Deep learning algorithms such as faster region-based convolutional neural network (R-CNN), single shot detector (SSD)- MobileNet, and single shot detector (SSD)-Inception was utilized and compared for detection and classification. From the results, the object detection algorithms using Jetson Nano achieved 80 FPS, with up to 82.98% accuracy, making it feasible for online and real-time in-vehicle monitoring with low power requirements.
AUTOMATIC THEFT SECURITY SYSTEM (SMART SURVEILLANCE CAMERA)
The proposed work aims to create a smart application camera, with the intention of eliminating the need for a human presence to detect any unwanted sinister activities, such as theft in this
case. Spread among the campus, are certain valuable biometric identification systems at arbitrary locations. The application monitosr these systems (hereafter referred to as “object”)
using our smart camera system based on an OpenCV platform.
By using OpenCV Haar Training, employing the Viola-Jones algorithm implementation in OpenCV, we teach the machine to identify the object in environmental conditions. An added
feature of face recognition is based on Principal Component Analysis (PCA) to generate Eigen Faces and the test images are verified by using distance based algorithm against the eigenfaces, like Euclidean distance algorithm or Mahalanobis Algorithm. If the object is misplaced, or an unauthorized user is in the extreme vicinity of the object, an alarm signal is raised.
An Overview of Supervised Machine Learning Paradigms and their Classifiers
Artificial Intelligence (AI) is the theory and development of computer systems capable of performing complex tasks that historically requires human intelligence such as recognizing speech, making decisions and identifying patterns. These tasks cannot be accomplished without the ability of the systems to learn. Machine learning is the ability of machines to learn from their past experiences. Just like humans, when machines learn under supervision, it is termed supervised learning. In this work, an in-depth knowledge on machine learning was expounded. Relevant literatures were reviewed with the aim of presenting the different types of supervised machine learning paradigms, their categories and classifiers.
Sanjaya: A Blind Assistance System
The document describes a blind assistance system called Sanjaya that uses object detection and depth estimation to help visually impaired individuals navigate environments. The system uses a SSD MobileNet model trained on the COCO dataset via TensorFlow's object detection API to identify objects in camera images in real-time. It then uses depth estimation to calculate distances and provides voice feedback alerts to users about detected objects and their proximity. The system aims to allow visually impaired people to have improved comprehension of their surroundings and navigation abilities.
Smart camera monitoring system
The document discusses developing a smart camera monitoring system to analyze environments, recognize entities, track motion paths, and process data to identify patterns. The system would use modules for entity detection, recognition, motion tracking, storing spatiotemporal data in a database, mining data to form patterns, and comparing input to available patterns to select and perform actions. Challenges include detecting faces in crowded areas, monitoring in hostile environments, and cost/design feasibility.
Multi-Dimensional Features Reduction of Consistency Subset Evaluator on Unsup...
This paper presents the application of multi dimensional feature reduction of Consistency Subset Evaluator (CSE) and Principal Component Analysis (PCA) and Unsupervised Expectation Maximization (UEM) classifier for imaging surveillance system. Recently, research in image processing has raised much interest in the security surveillance systems community. Weapon detection is one of the greatest challenges facing by the community recently. In order to overcome this issue, application of the UEM classifier is performed to focus on the need of detecting dangerous weapons. However, CSE and PCA are used to explore the usefulness of each feature and reduce the multi dimensional features to simplified features with no underlying hidden structure. In this paper, we take advantage of the simplified features and classifier to categorize images object with the hope to detect dangerous weapons effectively. In order to validate the effectiveness of the UEM classifier, several classifiers are used to compare the overall accuracy of the system with the compliment from the features reduction of CSE and PCA. These unsupervised classifiers include Farthest First, Densitybased Clustering and k-Means methods. The final outcome of this research clearly indicates that UEM has the ability in improving the classification accuracy using the extracted features from the multi-dimensional feature reduction of CSE. Besides, it is also shown that PCA is able to speed-up the computational time with the reduced dimensionality of the features compromising the slight decrease of accuracy.
Regression with Microsoft Azure & Ms Excel
Abdul Ahad Abro presented on data science, predictive analytics, machine learning algorithms, regression, classification, Microsoft Azure Machine Learning Studio, and academic publications. The presentation introduced key concepts in data science including machine learning, predictive analytics, regression, classification, and algorithms. It demonstrated regression analysis using Microsoft Azure Machine Learning Studio and Microsoft Excel. The methodology section described using a dataset from Azure for classification and linear regression in both Azure and Excel to compare results.
Decision Tree Based Algorithm for Intrusion Detection
An Intrusion Detection System (IDS) is a defense measure that supervises activities of the computer network and reports the malicious activities to the network administrator. Intruders do many attempts to gain access to the network and try to harm the organization’s data. Thus the security is the most important aspect for any type of organization. Due to these reasons, intrusion detection has been an important research issue. An IDS can be broadly classified as Signature based IDS and Anomaly based IDS. In our proposed work, the decision tree
algorithm is developed based on C4.5 decision tree approach. Feature selection and split value are important issues for constructing a decision tree. In this paper, the algorithm is designed to address these two issues. The most relevant features are selected using information gain and the split value is selected in such a way that makes the classifier unbiased towards most frequent values. Experimentation is performed on NSL-KDD (Network Security Laboratory Knowledge Discovery and Data Mining) dataset based on number of features. The time taken by the classifier to construct the model and the accuracy achieved is analyzed. It is concluded that the proposed Decision Tree Split (DTS) algorithm can be used for signature based intrusion detection.
Intrusion Detection System Based on K-Star Classifier and Feature Set Reduction
Abstract: Network security and Intrusion Detection Systems (IDS’s) is an important security related research
area. This paper applies K-star algorithm with filtering analysis in order to build a network intrusion detection
system. For our experimental analysis and as a case study, we have used the new NSL-KDD dataset, which is a
modified dataset for KDDCup 1999 intrusion detection benchmark dataset. With a split of 66.0% for the
training set and the remainder for the testing set a 2 class classifications has been implemented. WEKA which is
a java based open source software consists of a collection of machine learning algorithms for Data mining tasks
has been used in the testing process. The experimental results show that the proposed approach is very accurate
with low false positive rate and high true positive rate and it takes less learning time in comparison with other
existing approaches used for efficient network intrusion detection.
Keywords: Information Gain, Intrusion Detection System, Instance-based classifier, K-Star, Weka.
Implementing CSIRT based on some frameworks and maturity model
We implemented CSIRT based on some frameworks and maturity model including FIRST Service Framework, SIM3 and some document devised in Japan. We will explain how to use these documents in this presentation.
IRJET - Airplane Crash Analysis and Prediction using Machine Learning
This document discusses research on analyzing and predicting airplane crashes using machine learning techniques. The researchers conducted an analysis of airplane crash data, correlating it with accident factors. They used supervised machine learning algorithms like SVM, K-NN, AdaBoost and XGBoost for classification and prediction. Feature selection was used to choose the most relevant features for improving accuracy. The algorithms were trained and tested on datasets, with the most accurate one used for prediction to determine if a flight was "safe" or at "crash" risk based on input specifications. The goal was to help the aviation industry improve safety by better understanding factors that contribute to crashes.
GraphRAG for Life Science to increase LLM accuracy
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
5th LF Energy Power Grid Model Meet-up Slides
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
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Yuanzhe Cai is seeking a full-time software engineer position. He has a Ph.D. in computer science from UT Arlington and experience developing database, big data, and social network analysis projects. His research focused on inferring answer quality and expertise in question/answer communities. He has strong skills in Java, databases, and data mining tools.
AUTOMATIC THEFT SECURITY SYSTEM (SMART SURVEILLANCE CAMERA)
The proposed work aims to create a smart application camera, with the intention of eliminating
the need for a human presence to detect any unwanted sinister activities, such as theft in this
case. Spread among the campus, are certain valuable biometric identification systems at
arbitrary locations. The application monitosr these systems (hereafter referred to as “object”)
using our smart camera system based on an OpenCV platform.
By using OpenCV Haar Training, employing the Viola-Jones algorithm implementation in
OpenCV, we teach the machine to identify the object in environmental conditions. An added
feature of face recognition is based on Principal Component Analysis (PCA) to generate Eigen
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alarm signal is raised.
6950SafeAssign Originality ReportDigital Fore.docx
%69
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%0
SafeAssign Originality Report
Digital Forensics Tools & Tchq - 202040 - CRN127 - Rucker • Week Eight Assignment
%74Total Score: High riskVenkatesh Bodhupally
Submission UUID: 680cd83f-65c1-b609-7c13-c42c95f8db1c
Total Number of Reports
1
Highest Match
74 %
forensictools.docx
Average Match
74 %
Submitted on
04/30/20
05:27 PM EDT
Average Word Count
564
Highest: forensictools.docx
%74Attachment 1
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Student paper Student paper
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ProQuest document ProQuest document
Internet (1)
dfrws
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Excluded sources (0)
View Originality Report - Old Design
Word Count: 564
forensictools.docx
2 5
4 3
1
2 Student paper 4 ProQuest document 5 Student paper
https://blackboard.nec.edu/webapps/mdb-sa-BB5b75a0e7334a9/originalityReport?attemptId=4b21db19-c753-4a4c-bf5f-5fa5c168286f&course_id=_47023_1&download=true&includeDeleted=true&print=true&force=true
Source Matches (13)
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Student paper 100%
Student paper 100%
Running Head: INVESTIGATIONS AND FORENSICS 1
INVESTIGATIONS AND FORENSICS 4
Tools in Memory Forensics
Venkatesh Bodhupally
NEC.
Some of the tools applicable in the collection of live memory images in media include; volatility suite (Htun, Thwin & San, 2018). This tool or program analyzes the
RAM and has support from different operating systems such as Linux and windows. RAW and VMWare are also analyzable by this tool, with no issues arising. Rekall is
a tool used by investigators and responders since it features in analyzing other tools and acquiescing them. It's not a single application but a forensic framework
(Socała & Cohen, 2016). Helix ISO, a live disk that helps in capturing of memory images in a system and memory dumping. This type of tool has some risks associated
with it that make it not able to run directly into a system such as acquisition footprint Other tools include; process hacker which is an application that monitors
application, and it can be run when the machine that is on target is on use. The tool makes an investigator understand the issue affecting the system before a
snapshot of the memory is taken (Eden, Pontypridd, Cherdantseva, & Stoddart, 2016). The tool can also help in uncovering processes that are malicious and in
identifying terminated processes in a set period. Investigators also use or can use Belk soft RAM capture, which allows capturing of the volatile section of system
memory into a file. Belksoft RAM capture is a criminology device that has a free unpredictable memory, and it is used in catching the live RAM. Belksoft RAM capture
has drivers worth 32-bit and 64-bit; that's why this tool is used in overcoming anti-debugging as well as anti-dumping systems. Ftk Imager is a tool that catches the live
RAM. At a time picture, this type of tool makes a tiny bit alongside slack space. This type of tool is not capable of dividing or dissecting the memory dump that is
caught (Venkateswara Rao, & Ch.
6950SafeAssign Originality ReportDigital Fore.docx
%69
%5
%0
SafeAssign Originality Report
Digital Forensics Tools & Tchq - 202040 - CRN127 - Rucker • Week Eight Assignment
%74Total Score: High riskVenkatesh Bodhupally
Submission UUID: 680cd83f-65c1-b609-7c13-c42c95f8db1c
Total Number of Reports
1
Highest Match
74 %
forensictools.docx
Average Match
74 %
Submitted on
04/30/20
05:27 PM EDT
Average Word Count
564
Highest: forensictools.docx
%74Attachment 1
Institutional database (2)
Student paper Student paper
Scholarly journals & publications (2)
ProQuest document ProQuest document
Internet (1)
dfrws
Top sources (3)
Excluded sources (0)
View Originality Report - Old Design
Word Count: 564
forensictools.docx
2 5
4 3
1
2 Student paper 4 ProQuest document 5 Student paper
https://blackboard.nec.edu/webapps/mdb-sa-BB5b75a0e7334a9/originalityReport?attemptId=4b21db19-c753-4a4c-bf5f-5fa5c168286f&course_id=_47023_1&download=true&includeDeleted=true&print=true&force=true
Source Matches (13)
dfrws 66%
Student paper 100%
Student paper 100%
Running Head: INVESTIGATIONS AND FORENSICS 1
INVESTIGATIONS AND FORENSICS 4
Tools in Memory Forensics
Venkatesh Bodhupally
NEC.
Some of the tools applicable in the collection of live memory images in media include; volatility suite (Htun, Thwin & San, 2018). This tool or program analyzes the
RAM and has support from different operating systems such as Linux and windows. RAW and VMWare are also analyzable by this tool, with no issues arising. Rekall is
a tool used by investigators and responders since it features in analyzing other tools and acquiescing them. It's not a single application but a forensic framework
(Socała & Cohen, 2016). Helix ISO, a live disk that helps in capturing of memory images in a system and memory dumping. This type of tool has some risks associated
with it that make it not able to run directly into a system such as acquisition footprint Other tools include; process hacker which is an application that monitors
application, and it can be run when the machine that is on target is on use. The tool makes an investigator understand the issue affecting the system before a
snapshot of the memory is taken (Eden, Pontypridd, Cherdantseva, & Stoddart, 2016). The tool can also help in uncovering processes that are malicious and in
identifying terminated processes in a set period. Investigators also use or can use Belk soft RAM capture, which allows capturing of the volatile section of system
memory into a file. Belksoft RAM capture is a criminology device that has a free unpredictable memory, and it is used in catching the live RAM. Belksoft RAM capture
has drivers worth 32-bit and 64-bit; that's why this tool is used in overcoming anti-debugging as well as anti-dumping systems. Ftk Imager is a tool that catches the live
RAM. At a time picture, this type of tool makes a tiny bit alongside slack space. This type of tool is not capable of dividing or dissecting the memory dump that is
caught (Venkateswara Rao, & Ch ...
Computational model for artificial learning using formal concept analysis
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This document discusses using a random forest classifier with feature selection to improve intrusion detection. It begins with background on intrusion detection systems and challenges. It then proposes using genetic algorithms for feature selection to identify the most important features from a dataset. A random forest classifier is used for classification, which combines decision trees to improve accuracy. The methodology involves feature selection, classification with random forest, and detection. Feature weights are calculated and cross-validation is used to analyze detection rates for individual attacks. The goal is to improve accuracy, reduce training time, and better detect minority attacks through this approach.
Attack Detection Availing Feature Discretion using Random Forest Classifier
The widespread use of the Internet has an adverse effect of being vulnerable to cyber attacks. Defensive
mechanisms like firewalls and IDSs have evolved with a lot of research contributions happening in these
areas. Machine learning techniques have been successfully used in these defense mechanisms especially
IDSs. Although they are effective to some extent in identifying new patterns and variants of existing
malicious patterns, many attacks are still left as undetected. The objective is to develop an algorithm for
detecting malicious domains based on passive traffic measurements. In this paper, an anomaly-based
intrusion detection system based on an ensemble based machine learning classifier called Random Forest
with gradient boosting is deployed. NSL-KDD cup dataset is used for analysis and out of 41 features, 32
features were identified as significant using feature discretion.
Resume
Koushik Modayur Chandramouleeswaran has extensive experience developing software using various programming languages and technologies. He is pursuing a Master of Science in Computer Science from The University of Texas at Arlington with a GPA of 3.62/4.0 and holds a Bachelor of Technology in IT from Bharath University in India with a GPA of 8.44/10. He has worked as an Associate and Programmer Analyst at Cognizant Technology Solutions, developing tools and reports to help clients save over $120k. Koushik has strong skills in Python, Java, R, databases, big data technologies and machine learning. He has completed several projects applying techniques like decision
Toddler monitoring system in vehicle using single shot detector-mobilenet and...
Road vehicles are today’s primary form of transportation; the safety of children passengers must take precedence. Numerous reports of toddler death in road vehicles, include heatstroke and accidents caused by negligent parents. In this research, we report a system developed to monitor and detect a toddler's presence in a vehicle and to classify the toddler's seatbelt status. The objective of the toddler monitoring system is to monitor the child's conditions to ensure the toddler's safety. The device senses the toddler's seatbelt status and warns the driver if the child is left in the car after the vehicle is powered off. The vision-based monitoring system employs deep learning algorithms to recognize infants and seatbelts, in the interior vehicle environment. Due to its superior performance, the Nvidia Jetson Nano was selected as the computational unit. Deep learning algorithms such as faster region-based convolutional neural network (R-CNN), single shot detector (SSD)- MobileNet, and single shot detector (SSD)-Inception was utilized and compared for detection and classification. From the results, the object detection algorithms using Jetson Nano achieved 80 FPS, with up to 82.98% accuracy, making it feasible for online and real-time in-vehicle monitoring with low power requirements.
AUTOMATIC THEFT SECURITY SYSTEM (SMART SURVEILLANCE CAMERA)
The proposed work aims to create a smart application camera, with the intention of eliminating the need for a human presence to detect any unwanted sinister activities, such as theft in this
case. Spread among the campus, are certain valuable biometric identification systems at arbitrary locations. The application monitosr these systems (hereafter referred to as “object”)
using our smart camera system based on an OpenCV platform.
By using OpenCV Haar Training, employing the Viola-Jones algorithm implementation in OpenCV, we teach the machine to identify the object in environmental conditions. An added
feature of face recognition is based on Principal Component Analysis (PCA) to generate Eigen Faces and the test images are verified by using distance based algorithm against the eigenfaces, like Euclidean distance algorithm or Mahalanobis Algorithm. If the object is misplaced, or an unauthorized user is in the extreme vicinity of the object, an alarm signal is raised.
An Overview of Supervised Machine Learning Paradigms and their Classifiers
Artificial Intelligence (AI) is the theory and development of computer systems capable of performing complex tasks that historically requires human intelligence such as recognizing speech, making decisions and identifying patterns. These tasks cannot be accomplished without the ability of the systems to learn. Machine learning is the ability of machines to learn from their past experiences. Just like humans, when machines learn under supervision, it is termed supervised learning. In this work, an in-depth knowledge on machine learning was expounded. Relevant literatures were reviewed with the aim of presenting the different types of supervised machine learning paradigms, their categories and classifiers.
Sanjaya: A Blind Assistance System
The document describes a blind assistance system called Sanjaya that uses object detection and depth estimation to help visually impaired individuals navigate environments. The system uses a SSD MobileNet model trained on the COCO dataset via TensorFlow's object detection API to identify objects in camera images in real-time. It then uses depth estimation to calculate distances and provides voice feedback alerts to users about detected objects and their proximity. The system aims to allow visually impaired people to have improved comprehension of their surroundings and navigation abilities.
Smart camera monitoring system
The document discusses developing a smart camera monitoring system to analyze environments, recognize entities, track motion paths, and process data to identify patterns. The system would use modules for entity detection, recognition, motion tracking, storing spatiotemporal data in a database, mining data to form patterns, and comparing input to available patterns to select and perform actions. Challenges include detecting faces in crowded areas, monitoring in hostile environments, and cost/design feasibility.
Multi-Dimensional Features Reduction of Consistency Subset Evaluator on Unsup...
This paper presents the application of multi dimensional feature reduction of Consistency Subset Evaluator (CSE) and Principal Component Analysis (PCA) and Unsupervised Expectation Maximization (UEM) classifier for imaging surveillance system. Recently, research in image processing has raised much interest in the security surveillance systems community. Weapon detection is one of the greatest challenges facing by the community recently. In order to overcome this issue, application of the UEM classifier is performed to focus on the need of detecting dangerous weapons. However, CSE and PCA are used to explore the usefulness of each feature and reduce the multi dimensional features to simplified features with no underlying hidden structure. In this paper, we take advantage of the simplified features and classifier to categorize images object with the hope to detect dangerous weapons effectively. In order to validate the effectiveness of the UEM classifier, several classifiers are used to compare the overall accuracy of the system with the compliment from the features reduction of CSE and PCA. These unsupervised classifiers include Farthest First, Densitybased Clustering and k-Means methods. The final outcome of this research clearly indicates that UEM has the ability in improving the classification accuracy using the extracted features from the multi-dimensional feature reduction of CSE. Besides, it is also shown that PCA is able to speed-up the computational time with the reduced dimensionality of the features compromising the slight decrease of accuracy.
Regression with Microsoft Azure & Ms Excel
Abdul Ahad Abro presented on data science, predictive analytics, machine learning algorithms, regression, classification, Microsoft Azure Machine Learning Studio, and academic publications. The presentation introduced key concepts in data science including machine learning, predictive analytics, regression, classification, and algorithms. It demonstrated regression analysis using Microsoft Azure Machine Learning Studio and Microsoft Excel. The methodology section described using a dataset from Azure for classification and linear regression in both Azure and Excel to compare results.
Decision Tree Based Algorithm for Intrusion Detection
An Intrusion Detection System (IDS) is a defense measure that supervises activities of the computer network and reports the malicious activities to the network administrator. Intruders do many attempts to gain access to the network and try to harm the organization’s data. Thus the security is the most important aspect for any type of organization. Due to these reasons, intrusion detection has been an important research issue. An IDS can be broadly classified as Signature based IDS and Anomaly based IDS. In our proposed work, the decision tree
algorithm is developed based on C4.5 decision tree approach. Feature selection and split value are important issues for constructing a decision tree. In this paper, the algorithm is designed to address these two issues. The most relevant features are selected using information gain and the split value is selected in such a way that makes the classifier unbiased towards most frequent values. Experimentation is performed on NSL-KDD (Network Security Laboratory Knowledge Discovery and Data Mining) dataset based on number of features. The time taken by the classifier to construct the model and the accuracy achieved is analyzed. It is concluded that the proposed Decision Tree Split (DTS) algorithm can be used for signature based intrusion detection.
Intrusion Detection System Based on K-Star Classifier and Feature Set Reduction
Abstract: Network security and Intrusion Detection Systems (IDS’s) is an important security related research
area. This paper applies K-star algorithm with filtering analysis in order to build a network intrusion detection
system. For our experimental analysis and as a case study, we have used the new NSL-KDD dataset, which is a
modified dataset for KDDCup 1999 intrusion detection benchmark dataset. With a split of 66.0% for the
training set and the remainder for the testing set a 2 class classifications has been implemented. WEKA which is
a java based open source software consists of a collection of machine learning algorithms for Data mining tasks
has been used in the testing process. The experimental results show that the proposed approach is very accurate
with low false positive rate and high true positive rate and it takes less learning time in comparison with other
existing approaches used for efficient network intrusion detection.
Keywords: Information Gain, Intrusion Detection System, Instance-based classifier, K-Star, Weka.
Implementing CSIRT based on some frameworks and maturity model
We implemented CSIRT based on some frameworks and maturity model including FIRST Service Framework, SIM3 and some document devised in Japan. We will explain how to use these documents in this presentation.
IRJET - Airplane Crash Analysis and Prediction using Machine Learning
This document discusses research on analyzing and predicting airplane crashes using machine learning techniques. The researchers conducted an analysis of airplane crash data, correlating it with accident factors. They used supervised machine learning algorithms like SVM, K-NN, AdaBoost and XGBoost for classification and prediction. Feature selection was used to choose the most relevant features for improving accuracy. The algorithms were trained and tested on datasets, with the most accurate one used for prediction to determine if a flight was "safe" or at "crash" risk based on input specifications. The goal was to help the aviation industry improve safety by better understanding factors that contribute to crashes.
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5th LF Energy Power Grid Model Meet-up Slides
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It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
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Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
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With Skybuffer AI, various AI models can be integrated into a single communication channel such as Microsoft Teams. This integration empowers business users with insights drawn from SAP backend systems, enterprise documents, and the expansive knowledge of Generative AI. And the best part of it is that it is all managed through our intuitive no-code Action Server interface, requiring no extensive coding knowledge and making the advanced AI accessible to more users.
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The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
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- Understanding the DLAU tool and how to best utilize it
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Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
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