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Research on Modularization-based Code Reuse Technology in Software System Development

Yu Hu

Yu Hu

Anhui Institute Of International Business, College Of Information EngineeringHefei, China
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Hu, Yu
  
25 set 2025
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Pubblicato online: 25 set 2025
Ricevuto: 16 gen 2025
Accettato: 06 mag 2025
DOI: https://doi.org/10.2478/amns-2025-1013
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© 2025 Yu Hu, published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure 1.

Overall architecture of ROP attack based on dynamic feature monitoring
Overall architecture of ROP attack based on dynamic feature monitoring

Figure 2.

EFG execution flow comparison
EFG execution flow comparison

Figure 3.

Overall ROPGMN process
Overall ROPGMN process

Figure 4.

Flowchart of the verification generated by the generated code generator
Flowchart of the verification generated by the generated code generator

Figure 5.

Performance comparison of SPECjvm98
Performance comparison of SPECjvm98

Applications and data sets

Applications Loophole The number of benign gadget chains The number of malicious gadget chains
Adobe flash 11.2.202.336 CVE-2014-0502 201166 164523
Nginx 1.4.0 CVE-2013-2028 125364 103745
Proftpd 1.3.0a CVE-2006-6563 91365 60132
Firefox 3.5.10 CVE-2010-1214 212635 186635

Different procedures test results

Applications Data set Training data FP FN Accuracy
Adobe flash 368777 295039 0.42% 0.54% 97.1%
Nginx 228855 183105 0.04% 0.65% 98.0%
Proftpd 152176 121760 0.32% 0.96% 98.3%
Firefox 407529 326042 0.06% 0.38% 99.5%
Average - - 0.14% 0.54% 98.0%

Code length variation

Code Size/SPEC Sw ift Code(KB PIC Code(KB) #Compiled M ethods Average Inc.(B)
compress 353.87 376.71 459 54
jess 610.78 652.41 863 50
db 381.67 404.97 478 53
javac 1050.04 1099.67 1266 43
mtrt 510.06 534.33 616 41
jack 684.34 734 712 75
Sum/Avg 3591.08 3802.79 4383 52

Time overhead of SPEC CPU2006 Benchmark

Benchmark Original running time(*s) New running time(*s) Overhead
perlbench 246 246 0
bzip2 394 394 0
gcc 225 225 0
mcf 296 296 0
gobmk 413 413 0
hmmer 317 317 0
sjeng 451 451 0
libquantum 280 280 0
h264ref 443 443 0
omnetpp 335 335 0
astar 393 393 0
xalancbmk 212 212 0

Comparison of different CFI mechanisms

Safety level ISA extension Compiler change Runtime overhead Non-invasive detection
CFI I N N 24% N
(CCS’05)
CCFI I Y Y 3%~6% N
(CCS’15)
CodeArmor III N N 6.7% N
(S&P’17)
HAFIX I Y Y 3.1% N
(DAC’15)
HCIC II N N 0.94% N
(JIOT’18)
μCFI III N Y 13% N
(CCS’18)
Ours III N N 0% Y

Comparison of neural networks with SVM and LR

Method FP FN Accuracy
DNN 0.05% 0.6% 99%
LR 8.3% 20.3% 82.1%
SVM 7.7% 25.1% 74.6%
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