Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/575508
Title: Analysis of Hardware Assisted Security Techniques for Malware Detection and Device Authentication
Researcher: Praveen Kumar, E
Guide(s): Priyanka, S
Keywords: Machine Learning
Malware Detection
Multi-Server Environment
University: Vellore Institute of Technology (VIT-AP)
Completed Date: 2024
Abstract: In recent years, the area of cybersecurity has expanded to include hardware-based newlinevulnerabilities in addition to software-based threats. Malicious hardware, often known newlineas hardware malware, refers to the modification or exploitation of a computer system s or electronic device s components and circuitry in order to compromise security. Hardware malware operates at a lower level than standard software-based attacks, making it more difficult to identify and prevent. Hardware malware poses serious risks to system security as our reliance on interconnected gadgets and essential infrastructure systems increases. Traditional malware detection methods have mostly concentrated on software-based threats, largely ignoring hardware-level vulnerabilities. newlineSecurity risks have significantly increased as a result of the rapid proliferation of newlinedevices that are internet-connected across a variety of applications. Traditional cryp- newlinetographic primitives, which necessitate the secrecy of the key, serve as the foundation newlinefor the protection that these devices receive from conventional security measures. Usu- newlineally, secret keys are kept in non-volatile memories (NVMs) as device identification newline(IDs), which are then utilized for information encryption and authentication using cryptographic methods. But there are a few problems with this conventional method. The first is that secret keys kept in NVMs are vulnerable to many different kinds of attacks and are simple to clone or exploit. These secrets must also be stored and maintained in NVMs, which is a complicated and expensive process. Internet of Things (IoT) applications face numerous challenges related to the creation of random keys as well as secure key exchange. newlinePhysical Unclonable Functions (PUF) are a promising technology that can be used newlinein various applications to generate keys securely and perform authentication. Physical newlineUnclonable Functions (PUF) use the natural physical differences in integrated circuits newlineto provide unique, unanticipated outcomes, making them appropri
Pagination: xv,156
URI: http://hdl.handle.net/10603/575508
Appears in Departments:Department of Electronics Engineering

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02_prelim pages.pdf486.61 kBAdobe PDFView/Open
03_contents.pdf55.37 kBAdobe PDFView/Open
04_abstract.pdf64.3 kBAdobe PDFView/Open
05_chapter_1.pdf1.08 MBAdobe PDFView/Open
06_chapter_2.pdf466.15 kBAdobe PDFView/Open
07_chapter_3.pdf1.46 MBAdobe PDFView/Open
08_chapter_4.pdf754.27 kBAdobe PDFView/Open
09_chapter_5.pdf1.48 MBAdobe PDFView/Open
10_annexures.pdf133.71 kBAdobe PDFView/Open
80_recommendation.pdf85.13 kBAdobe PDFView/Open
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