Today only got this iPhone 16 by dad he upgraded to new phone
I came all the way from iPhone 11 (still the beast)
Still a worry because it is 128gb already filled with 80 GB 40GB photos…
Planning to play games and I worry about battery
It last me 5-6 hours today at 100%
Any suggestions and opinions….?!
I’m currently a undergraduate in Computer Science and working in HCL for role called analyst and currently in my job working with application packaging and repackaging
I love to work with cloud and network
Will this current job of mine will help in my career?
I'm currently an undergraduate at a private university, and I'm struggling to earn some pocket money given the tough situation right now. Does anyone know where I can find remote or part-time jobs? Any advice would be greatly appreciated!
I'm currently an undergraduate at a private university, and I'm struggling to earn some pocket money given the tough situation right now. Does anyone know where I can find remote or part-time jobs? Any advice would be greatly appreciated!
Hello everyone,
I am a final-year Computer Science student working on a project titled:
**“Lightweight Machine Learning Based Intrusion Detection System for Simulated Cloud Environments.”**
The current idea is to build a lightweight network-based IDS that monitors network traffic in a small virtualised cloud-like setup and detects suspicious or malicious traffic.
My planned setup is:
Ubuntu virtual machines connected through a virtual network
One VM as a normal client
One VM as a server
One VM for controlled attack simulation
Traffic monitoring at the virtual gateway/network level
CICIDS2017 as the main dataset
Network flow features such as flow duration, packet count, packet size, bytes per second, packets per second, protocol, and traffic labels
I am planning to compare:
K-Means or Isolation Forest for anomaly detection
Random Forest and XGBoost for supervised classification
The attacks I am considering are:
DoS/DDoS
Brute force
Port scanning
Botnet-like traffic
Selected web attacks
The project will evaluate:
Accuracy
Precision
Recall
F1 score
False positive rate
Training time
Detection time
CPU and memory usage
I would appreciate advice on the following:
Is this scope realistic for a final-year project?
Where should the IDS be placed in the virtual network?
Which algorithms are most suitable for a lightweight IDS?
Should I use K-Means, Isolation Forest, or DBSCAN for anomaly detection?
Which CICIDS2017 features should I initially focus on?
How can I demonstrate that the solution is cloud-specific rather than only a dataset classification project?
What is a safe and manageable way to simulate the selected attacks in an isolated lab?
Are there any good open-source projects, papers, or tutorials I should study?
I am still learning the topic and would value explanations suitable for a beginner. I am not looking for someone to complete the project for me; I want guidance on designing and implementing it correctly.
Thank you.
Hello everyone,
I am a final-year Computer Science student working on a project titled:
“Lightweight Machine Learning Based Intrusion Detection System for Simulated Cloud Environments.”
The current idea is to build a lightweight network-based IDS that monitors network traffic in a small virtualised cloud-like setup and detects suspicious or malicious traffic.
My planned setup is:
Ubuntu virtual machines connected through a virtual network
One VM as a normal client
One VM as a server
One VM for controlled attack simulation
Traffic monitoring at the virtual gateway/network level
CICIDS2017 as the main dataset
Network flow features such as flow duration, packet count, packet size, bytes per second, packets per second, protocol, and traffic labels
I am planning to compare:
K-Means or Isolation Forest for anomaly detection
Random Forest and XGBoost for supervised classification
The attacks I am considering are:
DoS/DDoS
Brute force
Port scanning
Botnet-like traffic
Selected web attacks
The project will evaluate:
Accuracy
Precision
Recall
F1 score
False positive rate
Training time
Detection time
CPU and memory usage
I would appreciate advice on the following:
Is this scope realistic for a final-year project?
Where should the IDS be placed in the virtual network?
Which algorithms are most suitable for a lightweight IDS?
Should I use K-Means, Isolation Forest, or DBSCAN for anomaly detection?
Which CICIDS2017 features should I initially focus on?
How can I demonstrate that the solution is cloud-specific rather than only a dataset classification project?
What is a safe and manageable way to simulate the selected attacks in an isolated lab?
Are there any good open-source projects, papers, or tutorials I should study?
I am still learning the topic and would value explanations suitable for a beginner. I am not looking for someone to complete the project for me; I want guidance on designing and implementing it correctly.
Thank you.