• Ryan Husain
• Muneer Ahmed

In military contexts, the Internet of Things (IoT) typically refers to a wide variety of Internet-connected objects and nodes (e.g., medical equipment and wearable battle outfits). Cybercriminals find these Internet of Things devices and nodes to be a lucrative target, especially state-sponsored or nation- state entities. Malware is a popular method of assault. This study presents an in-depth Internet of Battlefield Things (IoBT) malware detection technique based on machine learning that uses the device's series of operational codes (OpCodes). OpCodes are converted into a vector space and a Using a deep Eigenspace learning technique, distinguish between benign and malicious applications. We also show how resilient our suggested method is to junk code insertion attacks and how reliable it is for detecting malware. Finally, we release our malware sample on Github in the hopes that it will help future research endeavors.

"ROBUST MALWARE DETECTION IN IOT DEVICES USING DEEP EIGENSPACE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 8, page no.e814-e819, August-2024, Available :http://www.jetir.org/papers/JETIR2408486.pdf

"ROBUST MALWARE DETECTION IN IOT DEVICES USING DEEP EIGENSPACE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 8, page no. ppe814-e819, August-2024, Available at : http://www.jetir.org/papers/JETIR2408486.pdf