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NS-Presentation-v2.pptx

29 de Mar de 2023
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NS-Presentation-v2.pptx

  1. NOWSECURE PROJECT Collins, Stacey D Defersha, Endale A Jacobson, Rhiannon N Peterka, Joseph D Spring 2022
  2. WHAT TYPE OF PATTERN OF SECURITY RISK OR MALWARE DID YOU ATTEMPT TO FIND?
  3. SUMMARY IN REVIEW • 1007 distinct apps associated to 79 distinct countries. • 207 (20.6%) of apps have key size vulnerabilities. • There was no significant difference in key size vulnerabilities based on geographic region from which the app originated.
  4. BACKGROUND Use of a digital signature is important for users of applications to know the data being transferred is coming from the correct users. Appropriate key sizes, as well as other cryptographic based security measures, are necessary in protecting information, specifically for apps that deal with sensitive information. In the NowSecure provided data, Apps that have a key size vulnerability have a keysize_check variable value equal to True. If keysize_check is True, then the app uses a weak key size which could lead to forged digital signatures, and if False then no such vulnerability was found. Please note that a True value does not indicate that a digital signature has been forged, but simply that the vulnerability exists within the application that could lead to a forgery
  5. SECURITY ISSUES RELATED TO THE KEY SIZE dependent variable: • keysize_check Independent variables: • secure_random_check • change_cipher_spec_check • certificate_validity_check • sqlcipher_key_leakage_check
  6. HOW DID YOU GO ABOUT FINDING THIS SECURITY RISK ACROSS ALL THE NOWSECURE APPS?
  7. APPROACH • Conducted two phases of Experiments  Model 1) Using 2 of the proposed independent variables • secure_random_check • sqlcipher_key_leakage_check  two were removed because they didn't vary - all values were false • change_cipher_spec_check • certificate_validity_check  Model 2) Including all variables with chi-sq p-value <0.25
  8. CHI-SQ TABLE Variable Name Chi-Sq p-value DF secure_random_check 4.59 0.032 1 sends_sms_check 2.58 0.109 1 dirtycow_check 1.99 0.158 1 javascript_interface_check 1.87 0.171 1 allow_backup_check 1.81 0.179 1 decode_apk_check 1.65 0.199 1 publisher_global_location 3.85 0.427 4 auto_generated_screenshots_check 0.41 0.520 1 application_overprivileged_check 0.26 0.611 1 sqlcipher_key_leakage_check 0.26 0.612 1 decompile_apk_check 0.22 0.640 1 get_reflection_code 0.07 0.787 1 obfuscation_check 0.07 0.787 1 okhttp_vuln_check 0.01 0.905 1 dynamic_code_loading_check 0.00 0.979 1 certificate_validity_check 0.00 1.000 0 change_cipher_spec_check 0.00 1.000 0 debug_flag_check 0.00 1.000 0 extract_lib_info 0.00 1.000 0 get_native_methods 0.00 1.000 0 heartbleed_check 0.00 1.000 0 master_key_check 0.00 1.000 0
  9. STEPS looked at the odds ratios of the vulnerabilities in the independent variables to quantify the size of the relationship between the independent vulnerabilities and the key size vulnerability Is vulnerability in one independent variable significantly more likely to experience a key size vulnerability? The odds ratios will allow us to quantify the size of that likelihood examined the output of the model to determine which independent variables are significant in relation to the dependent variable, and which, if any, are not created a logistic regression model to examine relationship between our dependent and independent variables
  10. Results from Experiment 1
  11. Results from Experiment 2
  12. ODDS RATIOS COMPARISON MODEL 1 Variable Name OR (CI) secure_random_check 1.68 (1.07, 2.66) * sqlcipher_key_leakage_check 0.68 (0.23, 1.98) * = odds ratio significantly different from 1 MODEL 2 Variable Name OR (CI) dirtycow_check 1.50 (0.90, 2.51) sends_sms_check 1.64 (0.87, 3.10) decode_apk_check 0.30 (0.06, 1.50) allow_backup_check 0.78 (0.57, 1.07) secure_random_check 1.82 (1.14, 2.90) * javascript_interface_check 1.34 (0.89, 2.03) * = odds ratio significantly different from 1
  13. WHAT DID YOU FIND? DO YOU HAVE A LIST OF INSECURE APPS? OR PATTERNS OF INSECURE LIBRARIES OR OTHER ASPECTS OF APPS?
  14. FINDINGS • Only secure_random_check was significant • Model1 • odds ratio of 1.68 (95% CI 1.07 - 2.66) • Model 2 • odds ratio of 1.82 (95% CI 1.14 - 2.90) • Country Association • 1007 applications – representing 48 categories and developed across 79 distinct countries • 57 apps were unable to be logically associated to a country of origin • A unique trend by location was found to be insignificant
  15. Total Apps Vulnerable Keysize % Africa 15 1 6.7% Americas 333 63 18.9% Asia 386 85 22.0% Europe 163 48 29.4% Oceania 13 3 23.1%
  16. DO YOU THINK THERE ARE ANY IMPLICATIONS OF WHAT YOU’VE FOUND? IS IT NOVEL? IS IT WORTH OTHERS CHECKING ON THE APPS OR OTHER FINDINGS YOU HAVE?
  17. IMPLICATIONS • Model1:odds ratio of 1.68 (95% CI 1.07 - 2.66) • apps that have a secure_random_check vulnerability are 1.68 times as likely to also have a keysize_check vulnerability than those apps that do not have a secure_random_check vulnerability • Model 2 : odds ratio of 1.80 (95% CI 1.14 - 2.90) • apps that have a secure_random_check vulnerability are 1.80 times as likely to also have a keysize_check vulnerability than those apps that do not have a secure_random_check vulnerability • Country Attribution • Associating Apps to Country of Origin • Global Associations
  18. THE END

Notas do Editor

  1. Endale
  2. Endale
  3. Rhiannon
  4. Joseph 
  5. Stacy
  6. Endale
  7. Stacy
  8. Stacy
  9. Rhiannon
  10. Stacy
  11. Stacy
  12. Stacy
  13. Endale
  14. Stacy & Joseph 
  15. Joseph 
  16. Endale 
  17. Stacy and Joseph 
  18. Joseph
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