The document discusses algorithm analysis and asymptotic notation. It introduces algorithms for computing prefix averages of an array in quadratic and linear time. Specifically:
- An algorithm that computes prefix averages by directly applying the definition runs in O(n^2) time as its inner loop iterates over i elements n times.
- A more efficient algorithm that maintains a running sum runs in O(n) time, as each of its n iterations performs a constant number of operations.
- Asymptotic analysis allows algorithms to be classified based on growth rate, ignoring constant factors. This provides an algorithm-independent analysis of computational complexity.
The document discusses algorithm analysis and asymptotic analysis. It introduces key concepts like best case, worst case, and average case running times. It explains that worst case analysis is most important and easiest to analyze. The document covers analyzing algorithms using pseudocode, counting primitive operations, and determining asymptotic running time using Big-O notation. Examples are provided to illustrate these concepts, including analyzing algorithms for finding the maximum element in an array and computing prefix averages.
Data Structures and Algorithms Lecture 2: Analysis of Algorithms, Asymptotic ...TechVision8
This document discusses analyzing the running time of algorithms. It introduces pseudocode as a way to describe algorithms, primitive operations that are used to count the number of basic steps an algorithm takes, and asymptotic analysis to determine an algorithm's growth rate as the input size increases. The key points covered are using big-O notation to focus on the dominant term and ignore lower-order terms and constants, and analyzing two algorithms for computing prefix averages to demonstrate asymptotic analysis.
This document discusses algorithm analysis and asymptotic notation. It introduces algorithms for computing prefix averages in arrays. One algorithm runs in quadratic time O(n^2) by applying the definition directly. A more efficient linear time O(n) algorithm is also presented that maintains a running sum. Asymptotic analysis determines the worst-case running time of an algorithm as a function of the input size using big-O notation. This provides an analysis of algorithms that is independent of implementation details and hardware.
Order notation is a mathematical method used to analyze algorithms as the problem size increases. It allows comparison of performance independent of machine-specific factors. Common notations include Big-O (upper bound), Big-Omega (lower bound), and Theta (tight bound). These describe the limiting behavior of execution time as the problem size approaches infinity and are used to classify algorithms by their running time growth rates like constant, logarithmic, linear, quadratic, and exponential.
The document discusses algorithm analysis and computational complexity, specifically focusing on time complexity and big O notation. It defines key concepts like best case, average case, and worst case scenarios. Common time complexities like constant, logarithmic, linear, quadratic, and exponential functions are examined. Examples are provided to demonstrate how to calculate the time complexity of different algorithms using big O notation. The document emphasizes that worst case analysis is most useful for program design and comparing algorithms.
The document discusses data structures and algorithms. It defines key concepts like algorithms, programs, data structures, and asymptotic analysis. It explains how to analyze algorithms to determine their efficiency, including analyzing best, worst, and average cases. Common notations for describing asymptotic running time like Big-O, Big-Omega, and Big-Theta are introduced. The document provides examples of analyzing sorting algorithms like insertion sort and calculating running times. It also discusses techniques for proving an algorithm's correctness like assertions and loop invariants.
The document discusses algorithm analysis and asymptotic analysis. It introduces key concepts like best case, worst case, and average case running times. It explains that worst case analysis is most important and easiest to analyze. The document covers analyzing algorithms using pseudocode, counting primitive operations, and determining asymptotic running time using Big-O notation. Examples are provided to illustrate these concepts, including analyzing algorithms for finding the maximum element in an array and computing prefix averages.
Data Structures and Algorithms Lecture 2: Analysis of Algorithms, Asymptotic ...TechVision8
This document discusses analyzing the running time of algorithms. It introduces pseudocode as a way to describe algorithms, primitive operations that are used to count the number of basic steps an algorithm takes, and asymptotic analysis to determine an algorithm's growth rate as the input size increases. The key points covered are using big-O notation to focus on the dominant term and ignore lower-order terms and constants, and analyzing two algorithms for computing prefix averages to demonstrate asymptotic analysis.
This document discusses algorithm analysis and asymptotic notation. It introduces algorithms for computing prefix averages in arrays. One algorithm runs in quadratic time O(n^2) by applying the definition directly. A more efficient linear time O(n) algorithm is also presented that maintains a running sum. Asymptotic analysis determines the worst-case running time of an algorithm as a function of the input size using big-O notation. This provides an analysis of algorithms that is independent of implementation details and hardware.
Order notation is a mathematical method used to analyze algorithms as the problem size increases. It allows comparison of performance independent of machine-specific factors. Common notations include Big-O (upper bound), Big-Omega (lower bound), and Theta (tight bound). These describe the limiting behavior of execution time as the problem size approaches infinity and are used to classify algorithms by their running time growth rates like constant, logarithmic, linear, quadratic, and exponential.
The document discusses algorithm analysis and computational complexity, specifically focusing on time complexity and big O notation. It defines key concepts like best case, average case, and worst case scenarios. Common time complexities like constant, logarithmic, linear, quadratic, and exponential functions are examined. Examples are provided to demonstrate how to calculate the time complexity of different algorithms using big O notation. The document emphasizes that worst case analysis is most useful for program design and comparing algorithms.
The document discusses data structures and algorithms. It defines key concepts like algorithms, programs, data structures, and asymptotic analysis. It explains how to analyze algorithms to determine their efficiency, including analyzing best, worst, and average cases. Common notations for describing asymptotic running time like Big-O, Big-Omega, and Big-Theta are introduced. The document provides examples of analyzing sorting algorithms like insertion sort and calculating running times. It also discusses techniques for proving an algorithm's correctness like assertions and loop invariants.
Measuring the performance of algorithm, in terms of Big(O), theta, and Omega notation with respect to their Scientific notation like Worst, Average, Best, cases
Unit 1: Fundamentals of the Analysis of Algorithmic Efficiency, Units for Measuring Running Time, PROPERTIES OF AN ALGORITHM, Growth of Functions, Algorithm - Analysis, Asymptotic Notations, Recurrence Relation and problems
Asymptotic analysis allows the comparison of algorithms based on how their running time grows with input size rather than experimental testing. It involves analyzing an algorithm's pseudo-code to count primitive operations and expressing the running time using asymptotic notation like O(f(n)) which ignores constants. Common time complexities include constant O(1), logarithmic O(log n), linear O(n), quadratic O(n^2), and exponential O(2^n). Faster algorithms have lower asymptotic complexity.
Algorithm And analysis Lecture 03& 04-time complexity.Tariq Khan
This document discusses algorithm efficiency and complexity analysis. It defines key terms like algorithms, asymptotic complexity, Big O notation, and different complexity classes. It provides examples of analyzing time complexity for different algorithms like loops, nested loops, and recursive functions. The document explains that Big O notation allows analyzing algorithms independent of machine or input by focusing on the highest order term as the problem size increases. Overall, the document introduces methods for measuring an algorithm's efficiency and analyzing its time and space complexity asymptotically.
This document discusses algorithm analysis and complexity. It introduces algorithm analysis as a way to predict and compare algorithm performance. Different algorithms for computing factorials and finding the maximum subsequence sum are presented, along with their time complexities. The importance of efficient algorithms for problems involving large datasets is discussed.
This document provides an overview of algorithm analysis and asymptotic complexity. It discusses learning outcomes related to analyzing algorithm efficiency using Big O, Omega, and Theta notation. Key points covered include:
- Defining the problem size n and relating algorithm running time to n
- Distinguishing between best-case, worst-case, and average-case complexity
- Using asymptotic notation like Big O to give upper bounds on complexity rather than precise calculations
- Common asymptotic categories like O(n), O(n^2), O(n log n) that classify algorithm growth rates
Data Structure & Algorithms - Mathematicalbabuk110
This document discusses various mathematical notations and asymptotic analysis used for analyzing algorithms. It covers floor and ceiling functions, remainder function, summation symbol, factorial function, permutations, exponents, logarithms, Big-O, Big-Omega and Theta notations. It provides examples of calculating time complexity of insertion sort and bubble sort using asymptotic notations. It also discusses space complexity analysis and how to calculate the space required by an algorithm.
This document provides an overview of algorithms and asymptotic notation. It discusses that asymptotic notation allows algorithms to be compared based on how their running time grows relative to the input size. The key points covered include:
- Asymptotic notation describes the asymptotic behavior of a function, such as how fast algorithm running time grows relative to the input.
- Big O notation describes the worst case upper bound. If f(n) is O(g(n)), f(n) grows no faster than g(n).
- Common time complexities include O(1), O(log n), O(n), O(n log n), O(n^2).
- The dominating factor determines
This document discusses algorithms and their analysis. It begins with definitions of algorithms and their characteristics. Different methods for specifying algorithms are described, including pseudocode. The goal of algorithm analysis is introduced as comparing algorithms based on running time and other factors. Common algorithm analysis methods like worst-case, best-case, and average-case are defined. Big-O notation and time/space complexity are explained. Common algorithm design strategies like divide-and-conquer and randomized algorithms are summarized. Specific algorithms like repeated element detection and primality testing are mentioned.
how to calclute time complexity of algortihmSajid Marwat
This document discusses algorithm analysis and complexity. It defines key terms like asymptotic complexity, Big-O notation, and time complexity. It provides examples of analyzing simple algorithms like a sum function to determine their time complexity. Common analyses include looking at loops, nested loops, and sequences of statements. The goal is to classify algorithms according to their complexity, which is important for large inputs and machine-independent. Algorithms are classified based on worst, average, and best case analyses.
Data structures and algorithms involve organizing data to solve problems efficiently. An algorithm describes computational steps, while a program implements an algorithm. Key aspects of algorithms include efficiency as input size increases. Experimental studies measure running time but have limitations. Pseudocode describes algorithms at a high level. Analysis counts primitive operations to determine asymptotic running time, ignoring constant factors. The best, worst, and average cases analyze efficiency. Asymptotic notation like Big-O simplifies analysis by focusing on how time increases with input size.
This document discusses algorithm analysis and complexity. It defines key terms like algorithm, asymptotic complexity, Big-O notation, and time complexity. It provides examples of analyzing simple algorithms like summing array elements. The running time is expressed as a function of input size n. Common complexities like constant, linear, quadratic, and exponential time are introduced. Nested loops and sequences of statements are analyzed. The goal of analysis is to classify algorithms into complexity classes to understand how input size affects runtime.
Data Structure & Algorithms - Introductionbabuk110
This document introduces key concepts in data structures and algorithms including algorithms, programs, data structures, objects, and relations between data structures and objects. It discusses analyzing algorithms through measuring running time experimentally and theoretically using asymptotic analysis. Examples are provided of insertion sort and prefix averages algorithms, analyzing their best, worst, and average cases, and calculating their asymptotic running times as O(n) and O(n^2) respectively. The document outlines criteria for good algorithms and techniques for asymptotic analysis including Big-O notation.
This document provides an overview of algorithms including definitions, characteristics, design, and analysis. It defines an algorithm as a finite step-by-step procedure to solve a problem and discusses their key characteristics like input, definiteness, effectiveness, finiteness, and output. The document outlines the design of algorithms using pseudo-code and their analysis in terms of time and space complexity using asymptotic notations like Big O, Big Omega, and Big Theta. Examples are provided to illustrate linear search time complexity and the use of different notations to determine algorithm efficiency.
This document discusses algorithm analysis concepts such as time complexity, space complexity, and big-O notation. It explains how to analyze the complexity of algorithms using techniques like analyzing loops, nested loops, and sequences of statements. Common complexities like O(1), O(n), and O(n^2) are explained. Recurrence relations and solving them using iteration methods are also covered. The document aims to teach how to measure and classify the computational efficiency of algorithms.
Algorithm and its Properties
Computational Complexity
TIME COMPLEXITY
SPACE COMPLEXITY
Complexity Analysis and Asymptotic notations.
Big-oh-notation (O)
Omega-notation (Ω)
Theta-notation (Θ)
The Best, Average, and Worst Case Analyses.
COMPLEXITY Analyses EXAMPLES.
Comparing GROWTH RATES
The document discusses analyzing the efficiency of algorithms. It notes that algorithms can be correct but the best depends on efficiency. Efficiency depends on time and space complexity, where time complexity is the amount of time needed for complete execution and space complexity is the amount of memory required. Different algorithms have different efficiencies that can be analyzed based on how processing time and memory usage increase with larger input sizes.
This document discusses algorithm analysis tools. It explains that algorithm analysis is used to determine which of several algorithms to solve a problem is most efficient. Theoretical analysis counts primitive operations to approximate runtime as a function of input size. Common complexity classes like constant, linear, quadratic, and exponential time are defined based on how quickly runtime grows with size. Big-O notation represents the asymptotic upper bound of a function's growth rate to classify algorithms.
This document discusses algorithms complexity and data structures efficiency. It covers topics like time and memory complexity, asymptotic notation, fundamental data structures like arrays, lists, trees and hash tables, and choosing proper data structures. Computational complexity is important for algorithm design and efficient programming. The document provides examples of analyzing complexity for different algorithms.
Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...shadow0702a
This document serves as a comprehensive step-by-step guide on how to effectively use PyCharm for remote debugging of the Windows Subsystem for Linux (WSL) on a local Windows machine. It meticulously outlines several critical steps in the process, starting with the crucial task of enabling permissions, followed by the installation and configuration of WSL.
The guide then proceeds to explain how to set up the SSH service within the WSL environment, an integral part of the process. Alongside this, it also provides detailed instructions on how to modify the inbound rules of the Windows firewall to facilitate the process, ensuring that there are no connectivity issues that could potentially hinder the debugging process.
The document further emphasizes on the importance of checking the connection between the Windows and WSL environments, providing instructions on how to ensure that the connection is optimal and ready for remote debugging.
It also offers an in-depth guide on how to configure the WSL interpreter and files within the PyCharm environment. This is essential for ensuring that the debugging process is set up correctly and that the program can be run effectively within the WSL terminal.
Additionally, the document provides guidance on how to set up breakpoints for debugging, a fundamental aspect of the debugging process which allows the developer to stop the execution of their code at certain points and inspect their program at those stages.
Finally, the document concludes by providing a link to a reference blog. This blog offers additional information and guidance on configuring the remote Python interpreter in PyCharm, providing the reader with a well-rounded understanding of the process.
Measuring the performance of algorithm, in terms of Big(O), theta, and Omega notation with respect to their Scientific notation like Worst, Average, Best, cases
Unit 1: Fundamentals of the Analysis of Algorithmic Efficiency, Units for Measuring Running Time, PROPERTIES OF AN ALGORITHM, Growth of Functions, Algorithm - Analysis, Asymptotic Notations, Recurrence Relation and problems
Asymptotic analysis allows the comparison of algorithms based on how their running time grows with input size rather than experimental testing. It involves analyzing an algorithm's pseudo-code to count primitive operations and expressing the running time using asymptotic notation like O(f(n)) which ignores constants. Common time complexities include constant O(1), logarithmic O(log n), linear O(n), quadratic O(n^2), and exponential O(2^n). Faster algorithms have lower asymptotic complexity.
Algorithm And analysis Lecture 03& 04-time complexity.Tariq Khan
This document discusses algorithm efficiency and complexity analysis. It defines key terms like algorithms, asymptotic complexity, Big O notation, and different complexity classes. It provides examples of analyzing time complexity for different algorithms like loops, nested loops, and recursive functions. The document explains that Big O notation allows analyzing algorithms independent of machine or input by focusing on the highest order term as the problem size increases. Overall, the document introduces methods for measuring an algorithm's efficiency and analyzing its time and space complexity asymptotically.
This document discusses algorithm analysis and complexity. It introduces algorithm analysis as a way to predict and compare algorithm performance. Different algorithms for computing factorials and finding the maximum subsequence sum are presented, along with their time complexities. The importance of efficient algorithms for problems involving large datasets is discussed.
This document provides an overview of algorithm analysis and asymptotic complexity. It discusses learning outcomes related to analyzing algorithm efficiency using Big O, Omega, and Theta notation. Key points covered include:
- Defining the problem size n and relating algorithm running time to n
- Distinguishing between best-case, worst-case, and average-case complexity
- Using asymptotic notation like Big O to give upper bounds on complexity rather than precise calculations
- Common asymptotic categories like O(n), O(n^2), O(n log n) that classify algorithm growth rates
Data Structure & Algorithms - Mathematicalbabuk110
This document discusses various mathematical notations and asymptotic analysis used for analyzing algorithms. It covers floor and ceiling functions, remainder function, summation symbol, factorial function, permutations, exponents, logarithms, Big-O, Big-Omega and Theta notations. It provides examples of calculating time complexity of insertion sort and bubble sort using asymptotic notations. It also discusses space complexity analysis and how to calculate the space required by an algorithm.
This document provides an overview of algorithms and asymptotic notation. It discusses that asymptotic notation allows algorithms to be compared based on how their running time grows relative to the input size. The key points covered include:
- Asymptotic notation describes the asymptotic behavior of a function, such as how fast algorithm running time grows relative to the input.
- Big O notation describes the worst case upper bound. If f(n) is O(g(n)), f(n) grows no faster than g(n).
- Common time complexities include O(1), O(log n), O(n), O(n log n), O(n^2).
- The dominating factor determines
This document discusses algorithms and their analysis. It begins with definitions of algorithms and their characteristics. Different methods for specifying algorithms are described, including pseudocode. The goal of algorithm analysis is introduced as comparing algorithms based on running time and other factors. Common algorithm analysis methods like worst-case, best-case, and average-case are defined. Big-O notation and time/space complexity are explained. Common algorithm design strategies like divide-and-conquer and randomized algorithms are summarized. Specific algorithms like repeated element detection and primality testing are mentioned.
how to calclute time complexity of algortihmSajid Marwat
This document discusses algorithm analysis and complexity. It defines key terms like asymptotic complexity, Big-O notation, and time complexity. It provides examples of analyzing simple algorithms like a sum function to determine their time complexity. Common analyses include looking at loops, nested loops, and sequences of statements. The goal is to classify algorithms according to their complexity, which is important for large inputs and machine-independent. Algorithms are classified based on worst, average, and best case analyses.
Data structures and algorithms involve organizing data to solve problems efficiently. An algorithm describes computational steps, while a program implements an algorithm. Key aspects of algorithms include efficiency as input size increases. Experimental studies measure running time but have limitations. Pseudocode describes algorithms at a high level. Analysis counts primitive operations to determine asymptotic running time, ignoring constant factors. The best, worst, and average cases analyze efficiency. Asymptotic notation like Big-O simplifies analysis by focusing on how time increases with input size.
This document discusses algorithm analysis and complexity. It defines key terms like algorithm, asymptotic complexity, Big-O notation, and time complexity. It provides examples of analyzing simple algorithms like summing array elements. The running time is expressed as a function of input size n. Common complexities like constant, linear, quadratic, and exponential time are introduced. Nested loops and sequences of statements are analyzed. The goal of analysis is to classify algorithms into complexity classes to understand how input size affects runtime.
Data Structure & Algorithms - Introductionbabuk110
This document introduces key concepts in data structures and algorithms including algorithms, programs, data structures, objects, and relations between data structures and objects. It discusses analyzing algorithms through measuring running time experimentally and theoretically using asymptotic analysis. Examples are provided of insertion sort and prefix averages algorithms, analyzing their best, worst, and average cases, and calculating their asymptotic running times as O(n) and O(n^2) respectively. The document outlines criteria for good algorithms and techniques for asymptotic analysis including Big-O notation.
This document provides an overview of algorithms including definitions, characteristics, design, and analysis. It defines an algorithm as a finite step-by-step procedure to solve a problem and discusses their key characteristics like input, definiteness, effectiveness, finiteness, and output. The document outlines the design of algorithms using pseudo-code and their analysis in terms of time and space complexity using asymptotic notations like Big O, Big Omega, and Big Theta. Examples are provided to illustrate linear search time complexity and the use of different notations to determine algorithm efficiency.
This document discusses algorithm analysis concepts such as time complexity, space complexity, and big-O notation. It explains how to analyze the complexity of algorithms using techniques like analyzing loops, nested loops, and sequences of statements. Common complexities like O(1), O(n), and O(n^2) are explained. Recurrence relations and solving them using iteration methods are also covered. The document aims to teach how to measure and classify the computational efficiency of algorithms.
Algorithm and its Properties
Computational Complexity
TIME COMPLEXITY
SPACE COMPLEXITY
Complexity Analysis and Asymptotic notations.
Big-oh-notation (O)
Omega-notation (Ω)
Theta-notation (Θ)
The Best, Average, and Worst Case Analyses.
COMPLEXITY Analyses EXAMPLES.
Comparing GROWTH RATES
The document discusses analyzing the efficiency of algorithms. It notes that algorithms can be correct but the best depends on efficiency. Efficiency depends on time and space complexity, where time complexity is the amount of time needed for complete execution and space complexity is the amount of memory required. Different algorithms have different efficiencies that can be analyzed based on how processing time and memory usage increase with larger input sizes.
This document discusses algorithm analysis tools. It explains that algorithm analysis is used to determine which of several algorithms to solve a problem is most efficient. Theoretical analysis counts primitive operations to approximate runtime as a function of input size. Common complexity classes like constant, linear, quadratic, and exponential time are defined based on how quickly runtime grows with size. Big-O notation represents the asymptotic upper bound of a function's growth rate to classify algorithms.
This document discusses algorithms complexity and data structures efficiency. It covers topics like time and memory complexity, asymptotic notation, fundamental data structures like arrays, lists, trees and hash tables, and choosing proper data structures. Computational complexity is important for algorithm design and efficient programming. The document provides examples of analyzing complexity for different algorithms.
Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...shadow0702a
This document serves as a comprehensive step-by-step guide on how to effectively use PyCharm for remote debugging of the Windows Subsystem for Linux (WSL) on a local Windows machine. It meticulously outlines several critical steps in the process, starting with the crucial task of enabling permissions, followed by the installation and configuration of WSL.
The guide then proceeds to explain how to set up the SSH service within the WSL environment, an integral part of the process. Alongside this, it also provides detailed instructions on how to modify the inbound rules of the Windows firewall to facilitate the process, ensuring that there are no connectivity issues that could potentially hinder the debugging process.
The document further emphasizes on the importance of checking the connection between the Windows and WSL environments, providing instructions on how to ensure that the connection is optimal and ready for remote debugging.
It also offers an in-depth guide on how to configure the WSL interpreter and files within the PyCharm environment. This is essential for ensuring that the debugging process is set up correctly and that the program can be run effectively within the WSL terminal.
Additionally, the document provides guidance on how to set up breakpoints for debugging, a fundamental aspect of the debugging process which allows the developer to stop the execution of their code at certain points and inspect their program at those stages.
Finally, the document concludes by providing a link to a reference blog. This blog offers additional information and guidance on configuring the remote Python interpreter in PyCharm, providing the reader with a well-rounded understanding of the process.
Batteries -Introduction – Types of Batteries – discharging and charging of battery - characteristics of battery –battery rating- various tests on battery- – Primary battery: silver button cell- Secondary battery :Ni-Cd battery-modern battery: lithium ion battery-maintenance of batteries-choices of batteries for electric vehicle applications.
Fuel Cells: Introduction- importance and classification of fuel cells - description, principle, components, applications of fuel cells: H2-O2 fuel cell, alkaline fuel cell, molten carbonate fuel cell and direct methanol fuel cells.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
artificial intelligence and data science contents.pptxGauravCar
What is artificial intelligence? Artificial intelligence is the ability of a computer or computer-controlled robot to perform tasks that are commonly associated with the intellectual processes characteristic of humans, such as the ability to reason.
› ...
Artificial intelligence (AI) | Definitio
Null Bangalore | Pentesters Approach to AWS IAMDivyanshu
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Discover the latest insights on Data Driven Maintenance with our comprehensive webinar presentation. Learn about traditional maintenance challenges, the right approach to utilizing data, and the benefits of adopting a Data Driven Maintenance strategy. Explore real-world examples, industry best practices, and innovative solutions like FMECA and the D3M model. This presentation, led by expert Jules Oudmans, is essential for asset owners looking to optimize their maintenance processes and leverage digital technologies for improved efficiency and performance. Download now to stay ahead in the evolving maintenance landscape.