Writing Python scripts is an excellent exercise for learning this programming language.
... but.there's more to that as well, for hackers.
Sometimes tools are not available on attacked system, and even worse - we can't install these either.
But - as it happens often in practice, I read - if the target system has python interpreter, we can use our own scripts - we can use our own tools.
Here's list of some scripts that can be used in hacking - even if only as general ideas, to be enhanced and customized for our particular needs:
1. Introduction.
1.1. Hello World.
2. Basic Networking.
2.1. TCP Client,
2.2. TCP Server,
2.3. UDP Client,
2.4. UDP Server.
3. Webpage Scraping.
3.1. Words Count.
4. File Download/Upload.
4.1. File Download over HTTPS or over HTTP.
I'll try to keep the above list up-to-date, as I write more scripts.
Wednesday, 8 October 2025
Sunday, 21 September 2025
Artificial Intelligence & Ethical Hacking.
I've understood, and decided that AI can't be ignored anymore. I've asked the ChatGPT service the three questions, important questions for my pentesting career planning:
1. Can AI replace ethical hackers?
2. How pentesters can use AI to help them in their work?
3. Can AI analyze output of the LinPeas.sh script?
The 3rd question is an example, it shows how AI can be interacted with when a specific, tedious task is to be done.
Long answers short:
AI can augment human pentesters, to hasten some of their tedious work.
AI can, for example, be used to parse and interpret output of the LinPEAS.sh script, saving pentester a lot of time and effort during the post-exploitation phase, when one tries to escalate his or her privileges.
So, humans should remain in control, be the 'pentest leaders', that use AI for help in some of their tedious tasks.
To do their work well, however, the 'pentest leaders' should know & understand the nuances of the cybersecurity fields, especially the pentesting parts. We should be aware that AI will sometimes fail, and should be able to do some things manually, when this happens.
This can be very good news for pentesters, as human pentesters who will adapt to use AI in their work will be competitive, and will remain competitive in the job market.
Speaking abstractly:
As the History teaches us,
During the Times of Great Changes, those who Adapt will come Victorious,
Those who Oppose Changes will be left behind.
--
Important: Before using AI during the Real-Life Pentest (as opposed to Capture the Flag exercises on some hacking sites like the Hack the Box or Try Hack Me), check with lawyer if it's legal, if it's not violation of NDA or contract signed with customer.
Question #1:
Can AI Replace Ethical Hackers?
Answer:
That’s a deep question. Let’s break it down:
1. What ethical hackers do?
Ethical hackers (or penetration testers) simulate cyberattacks on systems to uncover vulnerabilities before malicious actors exploit them. Their role isn’t just technical — it also involves:
- Understanding business contexts and risks,
- Thinking creatively and unpredictably, like an attacker would.
- Communicating findings clearly to stakeholders.
- Advising on remediation strategies and balancing security with usability.
2. What AI can do today?
AI is already being applied in security testing and operations:
- Automation: AI can scan for known vulnerabilities, misconfigurations, and patterns at scale
much faster than humans.
- Exploit simulation: Some AI systems can chain together attack paths or fuzz inputs to find
weaknesses.
- Threat intelligence: AI can analyze vast amounts of threat data, flag anomalies, and
predict possible attack vectors.
- Red teaming tools: Emerging AI-driven platforms can mimic attacker behaviors and adapt
in real time.
3. Limitations of AI:
- Contextual judgment: AI struggles to weigh risks in a business or regulatory context.
For example, it might find a vulnerability but can’t decide if it’s truly critical to
that company’s operations.
- Creativity: Real attackers innovate — they don’t just follow known patterns. Humans excel
at thinking "outside the box" in ways AI models don’t yet replicate.
- Ethics & responsibility: Deciding how to responsibly disclose vulnerabilities or balance
customer trust is still very human.
- Adversarial risk: Attackers can manipulate AI-driven systems, feeding them misleading data
or evading detection.
4. Likely future.
Instead of replacing, AI will augment ethical hackers:
- AI will handle repetitive scanning, data analysis, and simulation.
- Hackers will focus on creative exploitation, strategy, and interpretation.
- The partnership could make assessments faster, deeper, and more accurate.
Bottom line:
AI won’t replace ethical hackers, but it will significantly change their role.
Ethical hackers of the future will need to work with AI, guiding it and applying human insight, where machines fall short.
Question #2:
I am a pentester. how can i use AI to help in my work?
Answer:
That’s a great question — AI can be a real force-multiplier in penetration testing if you know how to harness it. Here are some ways you can integrate AI into your workflow, broken down by phase of a typical engagement:
1. Reconnaissance & Information Gathering.
- OSINT automation: Use AI to parse large sets of WHOIS, Shodan, DNS, LinkedIn, or GitHub
data, quickly extracting relationships (employees, emails, exposed repos, tech stack).
- Text summarization: Feed in scraped content (press releases, job postings) and let AI
highlight likely internal systems, frameworks, or partners.
- Entity extraction: Train or prompt AI to spot emails, IPs, subdomains, or sensitive
strings from raw data dumps.
2. Scanning & Enumeration.
- Log interpretation: Paste Nmap or Nessus results and let AI group findings by
exploitability, prioritize by CVSS, or map to MITRE ATT&CK.
- Banner analysis: Feed AI strange service banners or version info; it can suggest likely
vulnerabilities, known exploits, or misconfigurations.
- Protocol fuzzing hints: AI can propose fuzzing payloads for edge-case input (common in
web apps, APIs, IoT).
3. Exploitation & Payload Crafting.
- Exploit adaptation: AI can help rewrite public PoCs to target slightly different
environments.
- Payload obfuscation ideas: Get suggestions for bypassing WAFs, IDS, or AV by altering
encodings, command structures, or macros.
- Reverse engineering aid: Summarize decompiled code, point out suspicious functions, or
hypothesize attack vectors.
4. Post-Exploitation & Reporting.
- Log analysis: Run harvested logs through AI to surface credentials, tokens, or lateral
movement opportunities.
- Privilege escalation guidance: Describe the host/system state, and AI can propose likely
misconfigurations or kernel exploits.
- Report writing: AI excels at turning raw notes into clear, professional findings with
executive summaries and technical detail (saves huge time).
5. Practical AI Uses in Daily Pentesting.
- Custom tool generation: Quickly script wrappers around APIs (Shodan, VirusTotal, Censys)
or automation for repetitive tasks.
- Cheat sheets: Ask AI for command snippets (Linux privilege escalation, PowerShell
one-liners, SQLi payloads).
- Knowledge distillation: Instead of digging through dozens of CVE writeups, AI can summarize
exploit requirements & impact.
Important note:
Always stay within legal scopes. AI can accelerate exploit generation or payload crafting, but don’t let it tempt you into going outside of your engagement boundaries.
Question #3:
Can AI analyze output of the LinPeas.sh script?
Answer:
Yes — AI can absolutely analyze the output of LinPEAS (LinPEAS.sh).
LinPEAS is a Linux privilege escalation auditing script that generates a lot of information about misconfigurations, weak permissions, vulnerable binaries, and other escalation vectors. The output is very verbose, so an AI can help by:
- Summarizing findings → Highlighting the most critical issues (e.g., SUID binaries,
writable cron jobs, weak sudo rules).
- Categorizing risks → Grouping findings by type (kernel exploits, file permissions,
network services, credentials, etc.).
- Explaining implications → For example, why a writable /etc/passwd is dangerous, or how
a misconfigured sudo rule could lead to root.
- Suggesting next steps → Recommending specific exploits or checks to attempt based on
the findings.
For example:
- If LinPEAS finds sudo -l allows running /usr/bin/vim without a password, AI could explain
that you can escalate to root via :!/bin/sh.
- If it detects a kernel version with a known exploit, AI can point you to the relevant
CVE and PoC.
If you share a snippet or full LinPEAS output here (you can redact sensitive info if needed), I can parse it and highlight the potential privilege escalation paths for you.
Do you want me to show you how I would analyze a sample LinPEAS output, or would you like to upload your script output for me to review directly?
Got it — whenever you’re ready, you can drop the LinPEAS output here, and I’ll go
through it to:
- Highlight the critical issues instead of leaving you with a giant wall of text.
- Explain what each finding means in plain language.
- Suggest possible exploitation paths (always with the caveat that you should only test
on systems you’re authorized to).
That way, instead of manually scanning hundreds of lines, you’ll get a prioritized list of "things worth looking at."
(...)
1. Can AI replace ethical hackers?
2. How pentesters can use AI to help them in their work?
3. Can AI analyze output of the LinPeas.sh script?
The 3rd question is an example, it shows how AI can be interacted with when a specific, tedious task is to be done.
AI can augment human pentesters, to hasten some of their tedious work.
AI can, for example, be used to parse and interpret output of the LinPEAS.sh script, saving pentester a lot of time and effort during the post-exploitation phase, when one tries to escalate his or her privileges.
So, humans should remain in control, be the 'pentest leaders', that use AI for help in some of their tedious tasks.
To do their work well, however, the 'pentest leaders' should know & understand the nuances of the cybersecurity fields, especially the pentesting parts. We should be aware that AI will sometimes fail, and should be able to do some things manually, when this happens.
This can be very good news for pentesters, as human pentesters who will adapt to use AI in their work will be competitive, and will remain competitive in the job market.
As the History teaches us,
During the Times of Great Changes, those who Adapt will come Victorious,
Those who Oppose Changes will be left behind.
--
Important: Before using AI during the Real-Life Pentest (as opposed to Capture the Flag exercises on some hacking sites like the Hack the Box or Try Hack Me), check with lawyer if it's legal, if it's not violation of NDA or contract signed with customer.
Can AI Replace Ethical Hackers?
Answer:
That’s a deep question. Let’s break it down:
Ethical hackers (or penetration testers) simulate cyberattacks on systems to uncover vulnerabilities before malicious actors exploit them. Their role isn’t just technical — it also involves:
- Understanding business contexts and risks,
- Thinking creatively and unpredictably, like an attacker would.
- Communicating findings clearly to stakeholders.
- Advising on remediation strategies and balancing security with usability.
2. What AI can do today?
AI is already being applied in security testing and operations:
- Automation: AI can scan for known vulnerabilities, misconfigurations, and patterns at scale
much faster than humans.
- Exploit simulation: Some AI systems can chain together attack paths or fuzz inputs to find
weaknesses.
- Threat intelligence: AI can analyze vast amounts of threat data, flag anomalies, and
predict possible attack vectors.
- Red teaming tools: Emerging AI-driven platforms can mimic attacker behaviors and adapt
in real time.
3. Limitations of AI:
- Contextual judgment: AI struggles to weigh risks in a business or regulatory context.
For example, it might find a vulnerability but can’t decide if it’s truly critical to
that company’s operations.
- Creativity: Real attackers innovate — they don’t just follow known patterns. Humans excel
at thinking "outside the box" in ways AI models don’t yet replicate.
- Ethics & responsibility: Deciding how to responsibly disclose vulnerabilities or balance
customer trust is still very human.
- Adversarial risk: Attackers can manipulate AI-driven systems, feeding them misleading data
or evading detection.
4. Likely future.
Instead of replacing, AI will augment ethical hackers:
- AI will handle repetitive scanning, data analysis, and simulation.
- Hackers will focus on creative exploitation, strategy, and interpretation.
- The partnership could make assessments faster, deeper, and more accurate.
Bottom line:
AI won’t replace ethical hackers, but it will significantly change their role.
Ethical hackers of the future will need to work with AI, guiding it and applying human insight, where machines fall short.
I am a pentester. how can i use AI to help in my work?
Answer:
That’s a great question — AI can be a real force-multiplier in penetration testing if you know how to harness it. Here are some ways you can integrate AI into your workflow, broken down by phase of a typical engagement:
- OSINT automation: Use AI to parse large sets of WHOIS, Shodan, DNS, LinkedIn, or GitHub
data, quickly extracting relationships (employees, emails, exposed repos, tech stack).
- Text summarization: Feed in scraped content (press releases, job postings) and let AI
highlight likely internal systems, frameworks, or partners.
- Entity extraction: Train or prompt AI to spot emails, IPs, subdomains, or sensitive
strings from raw data dumps.
2. Scanning & Enumeration.
- Log interpretation: Paste Nmap or Nessus results and let AI group findings by
exploitability, prioritize by CVSS, or map to MITRE ATT&CK.
- Banner analysis: Feed AI strange service banners or version info; it can suggest likely
vulnerabilities, known exploits, or misconfigurations.
- Protocol fuzzing hints: AI can propose fuzzing payloads for edge-case input (common in
web apps, APIs, IoT).
3. Exploitation & Payload Crafting.
- Exploit adaptation: AI can help rewrite public PoCs to target slightly different
environments.
- Payload obfuscation ideas: Get suggestions for bypassing WAFs, IDS, or AV by altering
encodings, command structures, or macros.
- Reverse engineering aid: Summarize decompiled code, point out suspicious functions, or
hypothesize attack vectors.
4. Post-Exploitation & Reporting.
- Log analysis: Run harvested logs through AI to surface credentials, tokens, or lateral
movement opportunities.
- Privilege escalation guidance: Describe the host/system state, and AI can propose likely
misconfigurations or kernel exploits.
- Report writing: AI excels at turning raw notes into clear, professional findings with
executive summaries and technical detail (saves huge time).
5. Practical AI Uses in Daily Pentesting.
- Custom tool generation: Quickly script wrappers around APIs (Shodan, VirusTotal, Censys)
or automation for repetitive tasks.
- Cheat sheets: Ask AI for command snippets (Linux privilege escalation, PowerShell
one-liners, SQLi payloads).
- Knowledge distillation: Instead of digging through dozens of CVE writeups, AI can summarize
exploit requirements & impact.
Important note:
Always stay within legal scopes. AI can accelerate exploit generation or payload crafting, but don’t let it tempt you into going outside of your engagement boundaries.
Can AI analyze output of the LinPeas.sh script?
Answer:
Yes — AI can absolutely analyze the output of LinPEAS (LinPEAS.sh).
- Summarizing findings → Highlighting the most critical issues (e.g., SUID binaries,
writable cron jobs, weak sudo rules).
- Categorizing risks → Grouping findings by type (kernel exploits, file permissions,
network services, credentials, etc.).
- Explaining implications → For example, why a writable /etc/passwd is dangerous, or how
a misconfigured sudo rule could lead to root.
- Suggesting next steps → Recommending specific exploits or checks to attempt based on
the findings.
For example:
- If LinPEAS finds sudo -l allows running /usr/bin/vim without a password, AI could explain
that you can escalate to root via :!/bin/sh.
- If it detects a kernel version with a known exploit, AI can point you to the relevant
CVE and PoC.
If you share a snippet or full LinPEAS output here (you can redact sensitive info if needed), I can parse it and highlight the potential privilege escalation paths for you.
Do you want me to show you how I would analyze a sample LinPEAS output, or would you like to upload your script output for me to review directly?
[ my answer ]: not for now, but in future - probably yes.
Got it — whenever you’re ready, you can drop the LinPEAS output here, and I’ll go
through it to:
- Highlight the critical issues instead of leaving you with a giant wall of text.
- Explain what each finding means in plain language.
- Suggest possible exploitation paths (always with the caveat that you should only test
on systems you’re authorized to).
That way, instead of manually scanning hundreds of lines, you’ll get a prioritized list of "things worth looking at."
(...)
Tuesday, 4 February 2025
Cyber Security Course's Reference.
There are many roles in the Cybersecurity job market. One of these is Ethical Hacking, also known as 'Red Teaming', or as 'Pentesting' ('Penetration Testing').
I've enlisted myself for two Pentesting courses:
- Cyberium (started in December 2024),
- Hack The Box (started in August 2025).
Making reference notes as I progress.
These notes won't replace the full courses, but may prove to be useful for myself and others still - if only as a reference.
--
List of lessons:
1. Kali Linux Reference for Pentesters,
2. Foundations of Cyber Security,
3. Introduction to Pentesting,
4. Pentesting: Art of Reconaissance,
5. Pentesting: Scanning & Enumeration,
6. Pentesting: Vulnerability Analysis,
7. Pentesting: Manual Exploitation of Systems, Services & Applications,
8. Pentesting: Metasploit in Practice,
9. Pentesting: Attacks on Web Applications,
10. Pentesting: Documentation & Reporting,
11. Pentesting: Post-Exploitation,
12. Pentesting: Cloud Security / work in progress /,
13. Pentesting: Attacks on Active Directory / to be done /,
... and the final chapter, created by this blog's author.
14. Martial Arts Philosophy in Hacking.
See also, if You wish:
> [ Artificial Intelligence & Ethical Hacking ].
I've enlisted myself for two Pentesting courses:
- Cyberium (started in December 2024),
- Hack The Box (started in August 2025).
Making reference notes as I progress.
These notes won't replace the full courses, but may prove to be useful for myself and others still - if only as a reference.
--
List of lessons:
1. Kali Linux Reference for Pentesters,
2. Foundations of Cyber Security,
3. Introduction to Pentesting,
4. Pentesting: Art of Reconaissance,
5. Pentesting: Scanning & Enumeration,
6. Pentesting: Vulnerability Analysis,
7. Pentesting: Manual Exploitation of Systems, Services & Applications,
8. Pentesting: Metasploit in Practice,
9. Pentesting: Attacks on Web Applications,
10. Pentesting: Documentation & Reporting,
11. Pentesting: Post-Exploitation,
12. Pentesting: Cloud Security / work in progress /,
13. Pentesting: Attacks on Active Directory / to be done /,
... and the final chapter, created by this blog's author.
14. Martial Arts Philosophy in Hacking.
See also, if You wish:
> [ Artificial Intelligence & Ethical Hacking ].
Sunday, 1 September 2024
Computers, Memory Pyramid & Code Size Optimization.
What is a Computer?
In Computer Sciences, Computer - by definition - is processor with memory and input/output devices. Any electronic device that has these is considered Computer. This includes Smartphones and many other tools.
Memory, Persistent or Transient.
There are two types of memory / pl: 'Są dwa rodzaje pamięci' /:
- Persistent / pl: 'Trwała' /,
- Transient / pl: 'Ulotna' /.
Persistent objects are those which continue to exist even after the program that created them has stopped running.
Transient objects cease to exist when program that created them stops.
Pyramid of Needs.
There are many types of memory, differing in price and speed of access.
Starting from the most expensive but fastest, there are:
- processor's registers,
- layers of the processor's cache (L1-L3, for example),
- RAM (Random Access Memory),
- persistent SSD/HDD storage.
Code Size optimization.
Smaller programs can be very quick in their execution.
When the whole program fits - for example - in L2 Processor's Cache, there's no need to reach RAM via BUS, so the code runs very quickly - as it's closer to the processor than RAM.
What if a Program doesn't fit in Transient Memory?
When a program needs to be executed, it needs to be loaded into the transient memory first.
However, Modern Operating Systems can send currently unused Program's parts & other Resources / for example: graphics image files, sound files and/or text files / from Transient Memory to Persistent SDD/HDD Memory and retrieve other Resources/Part(s) from Persistent Memory to Transient Memory / Usually from disk to RAM /.
/ pl: 'Współczesne Systemy Operacyjne mogą wysłać aktualnie niewykorzystywane części Oprogramowania i innych zasobów na dysk... i sprowadzić inne zasoby/części z pamięci trwałej do ulotnej, najczęściej do pamięci RAM' /.
Let's note, however, that loading/storing data in persistent memory is much slower than loading/storing from/to Transient RAM.
This is an automated operation in Modern Operating Systems, so programmers do not need to worry so much about that. Computers just slow down sometimes - and SSD/HDD becomes quite busy, when doing that.
This sometimes causes 'Flickering' / pl: 'Migotanie', 'Szamotanie' /, however. Code & Data is loaded/unloaded from/to persistent memory too slowly and can cause a Computer System to slow down or crash, as the Computing Resources run out. / Mostly CPU usage & Memory usage /.
So - in theory at least - a Computer can try to run larger programs than Computer has RAM.
Often it fails, but in theory this can work well.
In Computer Sciences, Computer - by definition - is processor with memory and input/output devices. Any electronic device that has these is considered Computer. This includes Smartphones and many other tools.
Memory, Persistent or Transient.
There are two types of memory / pl: 'Są dwa rodzaje pamięci' /:
- Persistent / pl: 'Trwała' /,
- Transient / pl: 'Ulotna' /.
Persistent objects are those which continue to exist even after the program that created them has stopped running.
Transient objects cease to exist when program that created them stops.
Pyramid of Needs.
There are many types of memory, differing in price and speed of access.
Starting from the most expensive but fastest, there are:
- processor's registers,
- layers of the processor's cache (L1-L3, for example),
- RAM (Random Access Memory),
- persistent SSD/HDD storage.
Code Size optimization.
Smaller programs can be very quick in their execution.
When the whole program fits - for example - in L2 Processor's Cache, there's no need to reach RAM via BUS, so the code runs very quickly - as it's closer to the processor than RAM.
What if a Program doesn't fit in Transient Memory?
When a program needs to be executed, it needs to be loaded into the transient memory first.
However, Modern Operating Systems can send currently unused Program's parts & other Resources / for example: graphics image files, sound files and/or text files / from Transient Memory to Persistent SDD/HDD Memory and retrieve other Resources/Part(s) from Persistent Memory to Transient Memory / Usually from disk to RAM /.
/ pl: 'Współczesne Systemy Operacyjne mogą wysłać aktualnie niewykorzystywane części Oprogramowania i innych zasobów na dysk... i sprowadzić inne zasoby/części z pamięci trwałej do ulotnej, najczęściej do pamięci RAM' /.
Let's note, however, that loading/storing data in persistent memory is much slower than loading/storing from/to Transient RAM.
This is an automated operation in Modern Operating Systems, so programmers do not need to worry so much about that. Computers just slow down sometimes - and SSD/HDD becomes quite busy, when doing that.
This sometimes causes 'Flickering' / pl: 'Migotanie', 'Szamotanie' /, however. Code & Data is loaded/unloaded from/to persistent memory too slowly and can cause a Computer System to slow down or crash, as the Computing Resources run out. / Mostly CPU usage & Memory usage /.
So - in theory at least - a Computer can try to run larger programs than Computer has RAM.
Often it fails, but in theory this can work well.
Monday, 29 April 2024
Logic & Axioms.
There are many Logics, based on different Axioms.
/ EN: 'Axiom' = PL: 'Aksjomat' /.
Axioms are statements that are not proven, but assumed as true, taken on faith.
/ EN: 'assumption' = PL: 'założenie' /.
Depending on the Axioms used, Theorems can be proven or disproven, and the whole Mathematical & Logical Apparatus can be developed.
Basing on Boolean Algebra, double negation evaluates to confirmation, but in some languages - polish for example - double negation does not mean confirmation, it does mean emphasis on negation, giving negation more power.
There are rules for negating Quantifiers as well.
/ EN: 'negation' = PL: 'zaprzeczenie' /,
/ EN: 'confirmation' = PL: 'potwierdzenie' /,
/ EN: 'emphasis' = PL: 'nacisk' /,
/ EN: 'quantifier' = PL: 'kwantyfikator' /.
/ według Algebry Boole'a i Teorii Kwantyfikatorów wylicza się do:
'istnieje taki moment w czasie kiedy zgodzę się na te warunki'. /
/ a w mowie potocznej znaczy: 'nie istnieje taki moment w czasie kiedy zgodzę się na te warunki'. /
Therefore, Speech of the Art, Literature, can have the Logic based on different Axioms than Boolean Algebra.
We can say that: 'Life is more than Boolean Algebra and Quantifiers' when we want to use casual, non-logical talks.
When Boolean Algebra and Quantifiers are useful then? It's useful in computer programming, or when we want to talk logically and precisely, or when we want to express our wishes logically and precisely. But when we opt for logical and precise speech, let's make sure first that other people we talk with understand our logic.
We can also define the addition operation / it's an Axiom too / differently as well.
We can have an exception:
1+1 = 2, 1+2 = 3, 2+1 = 3, 1+3 = 4, 3+1 = 4, 2+2 = 5, 1+4 = 5, 4+1 = 5, 2+3 = 5, 3+2 = 5, 1+5 = 6, 5+1 = 6, 2+4 = 6, ...
We can also redefine the addition operation differently on the more general, more universal scale:
n+2 in our redefined addition operation is n+2+1 in classical addition operation.
By doing so, by changing Axioms, we just have revolutionized the Mathematics. ;)
Many different theorems apply now, but at least we know that we can make expression 2+2 = 5 to be evaluated as true in a certain Context - even if this brings more or less desired effects in process ;).
We are free to assume any Axioms we want, examples can be multiplied infinitely.
Which Logic is 'better' than other, then?
... it depends on the assumed Criteria, which might be Axioms as well.
Tuesday, 6 February 2024
The Enigma Cipher of WW2 & the Turing Machine.
Polish and British mathematicians were among the best of people who cracked the Adolph Hitler's cipher named Enigma, it happened during the World War 2nd.
Enigma breaking was hard, and the mathematicians were hunted by Germany's spies.
Enigma evolved, so parts of the cipher were to be cracked again and again. It was not about making an automaton once, and letting it work for the rest of WW2 ... but statistically it worked so the effort was continued.
Doing maths when time flew and lives were at stake.... so stressful. The Germany's spies added to the dangers & to the stress too.
Polish mathematicians had one of few of first computers ... it was nicknamed: 'Bomb', for it was so big invention. It increased efficiency of the enigma cipher cracking.
And there was Alan Turing's effort of course. He was a British scientist who laid foundation-theory of computer's construction.
His thinking is still present in computer sciences of modern days.
(We were taught about Languages & Automatons in Warsaw University when i was studying computer sciences. Turing's Machine was a part of this lecture).
> [ https://en.wikipedia.org/wiki/Turing_machine ].
The Turing Machine is programmed in a similar way to programming the Register Machine.
> [ https://en.wikipedia.org/wiki/Register_machine ].
--
Sources:
1. 'Cubits & Shrodinger's Cat. From Turing Machine to Quantum Computers' by John Gribbin.
(Polish Edition).
2. The internet (wikipedia & the ważniak mostly) and my own thinking.
3. My (unfinished because of health problems) education at Warsaw's University (Mathematics, Informatics & Mechanics Faculty).
Enigma breaking was hard, and the mathematicians were hunted by Germany's spies.
Enigma evolved, so parts of the cipher were to be cracked again and again. It was not about making an automaton once, and letting it work for the rest of WW2 ... but statistically it worked so the effort was continued.
Doing maths when time flew and lives were at stake.... so stressful. The Germany's spies added to the dangers & to the stress too.
Polish mathematicians had one of few of first computers ... it was nicknamed: 'Bomb', for it was so big invention. It increased efficiency of the enigma cipher cracking.
And there was Alan Turing's effort of course. He was a British scientist who laid foundation-theory of computer's construction.
His thinking is still present in computer sciences of modern days.
(We were taught about Languages & Automatons in Warsaw University when i was studying computer sciences. Turing's Machine was a part of this lecture).
> [ https://en.wikipedia.org/wiki/Turing_machine ].
The Turing Machine is programmed in a similar way to programming the Register Machine.
> [ https://en.wikipedia.org/wiki/Register_machine ].
--
Sources:
1. 'Cubits & Shrodinger's Cat. From Turing Machine to Quantum Computers' by John Gribbin.
(Polish Edition).
2. The internet (wikipedia & the ważniak mostly) and my own thinking.
3. My (unfinished because of health problems) education at Warsaw's University (Mathematics, Informatics & Mechanics Faculty).
Friday, 24 March 2023
Realtime Systems.
Introduction.
Real-Time systems are computer systems in which succesful task performance depends on two factors:
- succesful result of computation,
- exact deadline time in which task was performed.
When a task execution time exceeds it's time deadline, we can say that system failed.
Hard, Firm & Soft Real-time Systems.
In the 'Hard Real-time Systems' missing a deadline is a total system failure. Hard realtime systems are created when missing a deadline can result in hardware damage or costs lives or health.
In 'Firm Real-time Systems' infrequent deadline misses are tolerable, but may degrade the system's quality of service. The usefulness of a result is zero after its deadline, but no damage or personnel loss occurs.
In 'Soft Real-time Systems' the usefulness of a result degrades after its deadline, thereby degrading the system's quality of service.
Requirements.
For a computer system to meet criteria of the 'Hard Real-time System', requirements are:
- well understood & fast enough hardware,
- real-time operating system (for example: RTLinux),
- every software piece must adhere to the 'hard real-time requirements'; software must generate results in the deadline time.
Real-Time systems are computer systems in which succesful task performance depends on two factors:
- succesful result of computation,
- exact deadline time in which task was performed.
When a task execution time exceeds it's time deadline, we can say that system failed.
Hard, Firm & Soft Real-time Systems.
In the 'Hard Real-time Systems' missing a deadline is a total system failure. Hard realtime systems are created when missing a deadline can result in hardware damage or costs lives or health.
In 'Firm Real-time Systems' infrequent deadline misses are tolerable, but may degrade the system's quality of service. The usefulness of a result is zero after its deadline, but no damage or personnel loss occurs.
In 'Soft Real-time Systems' the usefulness of a result degrades after its deadline, thereby degrading the system's quality of service.
Requirements.
For a computer system to meet criteria of the 'Hard Real-time System', requirements are:
- well understood & fast enough hardware,
- real-time operating system (for example: RTLinux),
- every software piece must adhere to the 'hard real-time requirements'; software must generate results in the deadline time.
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