Abstract :

The Internet Of Things Devices Are Growing Fast And That Has Made It Easier For Bad People To Attack Them With Sophisticated Malware. These Attacks Can Hurt How The Devices Work If The Network Is Available And If The Data Is Safe. The Old Way Of Keeping Things Safe Does Not Work Well Because It Is Slow And Not Good At Keeping Private Information Secret. So, We Made Something New Called Cyber Force++ To Help With This Problem. Cyber Force++ Is A Way To Keep Things Safe On The Internet Of Things. It Uses Machine Learning And Something Called Federated Reinforcement Learning To Stop Malware In Time And Keep Data Private. It Always Looks At What The Internet Of Things Devices Doing, Like How Much Work The Computer Is Doing How Much Memory It Is Using And What Is Happening On The Network. Then It Uses A Kind Of Machine Learning Called Random Forest To Look At All This Information And Figure Out If There Might Be A Malware Attack. It Can Even Guess How Bad The Attack Might Be. Suggest What To Do To Stop It. What Is Different About Cyber Force++ Is That It Uses Something Called Federated Learning. This Means That The Devices Can Work Together To Learn How To Stop Malware Without Sharing All Their Information. This Keeps The Data Private. Does Not Use Up Too Much Bandwidth. It Also Uses Something Called Reinforcement Learning To Pick The Way To Stop The Attack Based On How Bad It Is. The System Is Always. Getting Better And It Can Even Change The Network To Make It Harder For Bad People To Attack. The System Also Has A Dashboard To Look At What Is Happening In Real Time A Way To Manage Alerts And A Way For Administrators To Log In And Manage Everything. When We Tested Cyber Force++ It Worked Well. It Could Find Malware Attacks Predict What To Do To Stop Them And Make The Network Safer. It Is A Way To Keep The Internet Of Things Safe, From Bad People Who Want To Hurt Them

Published:

07-7-2026

Issue:

Vol. 26 No. 7 (2026)

Page Nos:

229-235

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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