Cybersecurity Threats and Firewalls

Simplifying a problem by focusing on essential parts and ignoring details.

Breaking down a complex problem into smaller, solvable parts.

Finding patterns in data, problems or solutions.

Step-by-step instructions to solve a problem.

A human-made object or system using computational thinking.

Step-by-step representation of an algorithm using both English and programming-like commands.

Visual representation of an algorithm using special shapes.

Key strategies for analyzing and solving problems in a computer science context.

Clearly state the problem you want to solve.

Identify the needed information (inputs) and the expected result (outputs).

Break down the problem into smaller sub-problems, then describe how each sub-problem will be solved

Choose how to organize and store data (e.g., arrays, stacks).

Determine the order of actions in the program (e.g., loops, conditionals).

Describe the actions that transform the inputs into outputs. Start at a high level and refine until specific actions.

Steps to create a computational artifact, starting from defining the problem all the way to developing the computation steps.

Verifying that a computational artifact (algorithm or code) works correctly with potential input errors or boundary cases.

Manually simulating the execution of an algorithm to verify correctness, track variable changes, and ensure logic flow.

Understand the algorithm's logic flow, choose test inputs, and initialize variables.

Hand execute the algorithm, note inputs/processing, updated variables and flow control.

Continue to trace steps until the algorithm finishes, check final output for accuracy for initial steps.

A table that shows how variables change during the tracing of an algorithm.

Validating if the algorithm performs correctly across a range of inputs (including invalid ones).

A sorting algorithm that repeatedly steps through the list, compares adjacent elements, and swaps them if they are in the wrong order until the list is fully sorted.

A single pass through the entire list, comparing and potentially swapping adjacent elements.

Two elements that are directly next to each other in the list.

Exchanging the positions of two elements within the list.

A search algorithm that efficiently finds a target value in a sorted list by repeatedly dividing the search interval in half.

A problem-solving strategy where a complex problem is broken down into smaller, more manageable subproblems.

The element located in the center of the sorted list.

The portion of the list that still needs to be searched for the target value.

The process of assessing an algorithm's performance, efficiency, correctness, and clarity to determine if it meets its intended goals.

How accurately an algorithm produces the expected output for different inputs.

Measures how well an algorithm uses computer resources (time and memory) to solve a problem.

How easy it is to understand and follow the instructions in an algorithm.

How consistently an algorithm produces the same output for the same input.

Algorithms designed to arrange data in a specific order, usually ascending or descending.

A sorting algorithm that repeatedly compares adjacent elements and swaps them to put them in the correct order. It starts by considering the first element as already sorted, then inserts the remaining elements one at a time into their correct position.

An algorithm is a set of steps to solve a problem, while pseudocode is a way to describe an algorithm using both English and programming-like commands.