Добавил:
Опубликованный материал нарушает ваши авторские права? Сообщите нам.
Вуз: Предмет: Файл:

Проектирование экзоскелета. Монография

.pdf
Скачиваний:
0
Добавлен:
08.09.2026
Размер:
2 Мб
Скачать
☆
131
СПИСОК ИСПОЛЬЗОВАННОЙ ЛИТЕРАТУРЫ
1. Laimek, Roongtawan, Natsuda Kaothanthong, and Thepchai
Supnithi. 2018. “ATM Fraud Detection Using Outlier Detection.” In
Intelligent Data Engineering and Automated Learning -- IDEAL 2018, eds. Hujun Yin, David Camacho, Paulo Novais, and Antonio J Tallón-Ballesteros. Cham: Springer International Publishing, 539–47.
2. Kim, Min-Jung, and Taek-Soo Kim. 2002. “A Neural Classifier with
Fraud Density Map for Effective Credit Card Fraud Detection.” In Intelligent
Data Engineering and Automated Learning --- IDEAL 2002, eds. Hujun Yin et al. Berlin, Heidelberg: Springer Berlin Heidelberg, 378–83.
3. Yang, Qinghong et al. 2015. “Based Big Data Analysis of Fraud
Detection for Online Transaction Orders.” In Cloud Computing, eds. Victor
C M Leung, Roy Xiaorong Lai, Min Chen, and Jiafu Wan. Cham: Springer International Publishing, 98–106.
4. Hartl, Verena M I A, and Ulrike Schmuntzsch. 2016. “Fraud
Protection for Online Banking.” In Human Aspects of Information Security,
Privacy, and Trust, ed. Theo Tryfonas. Cham: Springer International Publishing, 37–47.
5. Vishwakarma, Pinki Prakash, Amiya Kumar Tripathy, and
Srikanth Vemuru. 2018. “A Layered Approach to Fraud Analytics for NFC- Enabled Mobile Payment System.” In Distributed Computing and Internet
Technology, eds. Atul Negi, Raj Bhatnagar, and Laxmi Parida. Cham: Springer International Publishing, 127–31.
6. Lebichot, Bertrand, Fabian Braun, Olivier Caelen, and Marco
Saerens. 2017. “A Graph-Based, Semi-Supervised, Credit Card Fraud Detection System.” In Complex Networks & Their Applications V, eds.
Hocine Cherifi, Sabrina Gaito, Walter Quattrociocchi, and Alessandra Sala. Cham: Springer International Publishing, 721–33.
7. Yildirim, Mehmet Yigit, Mert Ozer, and Hasan Davulcu. 2018.
“Cost-Sensitive Decision Making for Online Fraud Management.” In Artificial Intelligence Applications and Innovations, eds. Lazaros Iliadis, Ilias
132
Maglogiannis, and Vassilis Plagianakos. Cham: Springer International Publishing, 323–36.
8. El-kaime, Hafsa, Mostafa Hanoune, and Ahmed Eddaoui. 2019.
“The Data Mining: A Solution for Credit Card Fraud Detection in Banking.”
In Lecture Notes in Real-Time Intelligent Systems, eds. Jolanta Mizera­Pietraszko, Pit Pichappan, and Lahby Mohamed. Cham: Springer International Publishing, 332–41.
9. Lee, Namsup, Hyunsoo Yoon, and Daeseon Choi. 2018.
“Detecting Online Game Chargeback Fraud Based on Transaction
Sequence Modeling Using Recurrent Neural Network.” In Information Security Applications, eds. Brent ByungHoon Kang and Taesoo Kim. Cham: Springer International Publishing, 297–309.
10. Wiese, Bénard, and Christian Omlin. 2009. “Credit Card
Transactions, Fraud Detection, and Machine Learning: Modelling Time with
LSTM Recurrent Neural Networks.” In Innovations in Neural Information
Paradigms and Applications, eds. Monica Bianchini, Marco Maggini, Franco Scarselli, and Lakhmi C Jain. Berlin, Heidelberg: Springer Berlin Heidelberg, 231–68. https://doi.org/10.1007/978-3-642-04003-0_10.
11. Chen, Rong-Chang, Ming-Li Chiu, Ya-Li Huang, and Lin-Ti Chen.
2004. “Detecting Credit Card Fraud by Using Questionnaire-Responded Transaction Model Based on Support Vector Machines.” In Intelligent Data
Engineering and Automated Learning -- IDEAL 2004, eds. Zheng Rong Yang, Hujun Yin, and Richard M Everson. Berlin, Heidelberg: Springer Berlin Heidelberg, 800–806.
12. Blackwell, Clive. 2008. “A Reasoning Agent for Credit Card Fraud
on the Internet Using the Event Calculus.” In Global E-Security, eds. Hamid Jahankhani, Kenneth Revett, and Dominic Palmer-Brown. Berlin, Heidelberg: Springer Berlin Heidelberg, 26–39.
13. Chen, Rongchang, Tungshou Chen, Yuer Chien, and Yuru Yang.
2005. “Novel Questionnaire-Responded Transaction Approach with SVM for Credit Card Fraud Detection.” In Advances in Neural Networks -- ISNN
2005, eds. Jun Wang, Xiao-Feng Liao, and Zhang Yi. Berlin, Heidelberg: Springer Berlin Heidelberg, 916–21.
133
14. Ding, Xuhua. 2010. “A Hybrid Method to Detect Deflation Fraud
in Cost-Per-Action Online Advertising.” In Applied Cryptography and Network Security, eds. Jianying Zhou and Moti Yung. Berlin, Heidelberg: Springer Berlin Heidelberg, 545–62.
15. Peng, Yanlin, Linfeng Zhang, and Yong Guan. 2009. “Detecting
Fraud in Internet Auction Systems.” In Advances in Digital Forensics V, eds.
Gilbert Peterson and Sujeet Shenoi. Berlin, Heidelberg: Springer Berlin Heidelberg, 187–98.
16. Kumar, Akshi, and Garima Gupta. 2018. “Fraud Detection in
Online Transactions Using Supervised Learning Techniques.” In Towards Extensible and Adaptable Methods in Computing, eds. Shampa Chakraverty, Anil Goel, and Sanjay Misra. Singapore: Springer Singapore, 309–21. https://doi.org/10.1007/978-981-13-2348-5_23.
17. Akhilomen, John. 2013. “Data Mining Application for Cyber
Credit-Card Fraud Detection System.” In Advances in Data Mining. Applications and Theoretical Aspects, ed. Petra Perner. Berlin, Heidelberg: Springer Berlin Heidelberg, 218–28.
18. Whitrow, C et al. 2009. “Transaction Aggregation as a Strategy
for Credit Card Fraud Detection.” Data Mining and Knowledge Discovery
18(1): 30–55. https://doi.org/10.1007/s10618-008-0116-z.
19. Islam, Asadul Khandoker et al. 2010. “Fraud Detection in ERP
Systems Using Scenario Matching.” In Security and Privacy -- Silver Linings in the Cloud, eds. Kai Rannenberg, Vijay Varadharajan, and Christian Weber. Berlin, Heidelberg: Springer Berlin Heidelberg, 112–23.
20. Kundu, Amlan, Shamik Sural, and A K Majumdar. 2006. “Two-
Stage Credit Card Fraud Detection Using Sequence Alignment.” In Information Systems Security, eds. Aditya Bagchi and Vijayalakshmi Atluri. Berlin, Heidelberg: Springer Berlin Heidelberg, 260–75.
21. Lim, Wee-Yong, Amit Sachan, and Vrizlynn Thing. 2014.
“Conditional Weighted Transaction Aggregation for Credit Card Fraud Detection.” In Advances in Digital Forensics X, eds. Gilbert Peterson and Sujeet Shenoi. Berlin, Heidelberg: Springer Berlin Heidelberg, 3–16.
134
22. Wei, Wei et al. 2013. “Effective Detection of Sophisticated Online
Banking Fraud on Extremely Imbalanced Data.” World Wide Web 16(4): 449–75. https://doi.org/10.1007/s11280-012-0178-0.
23. Kim, Ae Chan, Seongkon Kim, Won Hyung Park, and Dong Hoon
Lee. 2014. “Fraud and Financial Crime Detection Model Using Malware Forensics.” Multimedia Tools and Applications 68(2): 479–96.
https://doi.org/10.1007/s11042-013-1410-3.
24. Carminati, Michele et al. 2014. “BankSealer: An Online Banking
Fraud Analysis and Decision Support System.” In ICT Systems Security and
Privacy Protection, eds. Nora Cuppens-Boulahia et al. Berlin, Heidelberg: Springer Berlin Heidelberg, 380–94.
25. Hand, D J et al. 2008. “Performance Criteria for Plastic Card
Fraud Detection Tools.” Journal of the Operational Research Society 59(7):
956–62. https://doi.org/10.1057/palgrave.jors.2602418.
26. Molloy, Ian et al. 2017. “Graph Analytics for Real-Time Scoring of
Cross-Channel Transactional Fraud.” In Financial Cryptography and Data Security, eds. Jens Grossklags and Bart Preneel. Berlin, Heidelberg: Springer Berlin Heidelberg, 22–40.
27. Adewumi, Aderemi O, and Andronicus A Akinyelu. 2017. “A
Survey of Machine-Learning and Nature-Inspired Based Credit Card Fraud
Detection Techniques.” International Journal of System Assurance
Engineering and Management 8(2): 937–53. https://doi.org/10.1007/s13198-016-0551-y.
28. Jog, Anita, and Anjali A Chandavale. 2018. “Implementation of
Credit Card Fraud Detection System with Concept Drifts Adaptation.” In
Intelligent Computing and Information and Communication, eds. Subhash Bhalla et al. Singapore: Springer Singapore, 467–77.
29. Khattri, Vipin, and Deepak Kumar Singh. 2018. “A Novel Distance
Authentication Mechanism to Prevent the Online Transaction Fraud.” In
Advances in Fire and Process Safety, eds. N A Siddiqui, S M Tauseef, S A Abbasi, and Ali S Rangwala. Singapore: Springer Singapore, 157–69.
135
30. Blackwell, Clive. 2014. “Using Fraud Trees to Analyze Internet
Credit Card Fraud.” In Advances in Digital Forensics X, eds. Gilbert Peterson and Sujeet Shenoi. Berlin, Heidelberg: Springer Berlin Heidelberg, 17–29.
136
ЛИСТИНГ ПРОГРАММЫ (ПОЛНЫЙ КОД)
#ifndef VERSION #error "Please use ./build script." #endif /* not VERSION */ #define _GNU_SOURCE
/* Hackish hack to import kernel stat struct without much collateral
damage */
#define stat __kernel_stat #define stat64 __kernel_stat64 #define old_stat __old_kernel_stat #define new_stat __kernel_stat #include <asm/stat.h> #undef stat #undef stat64 #undef old_stat #undef new_stat
/* End of nasty hack. */
#include <sys/ptrace.h> #include <sys/user.h> #include <stdio.h> #include <unistd.h> #include <stdlib.h> #include <assert.h> #include <time.h> #include <getopt.h> #include <signal.h> #include <sys/stat.h> #include <ctype.h> #include <sched.h>
137
#include <sys/types.h> #include <sys/wait.h> #include <errno.h> #include <string.h> #include "asmstring.h" #include <fcntl.h> #include <dlfcn.h> #include <asm/unistd.h> #include <sys/mman.h> #include <malloc.h> #include <asm/types.h> #include <utime.h> #include <sys/resource.h> #include <linux/types.h> #include <linux/dirent.h> #include <sys/vfs.h> #include <sys/socket.h> #include <netdb.h> #include <grp.h> #include <pwd.h> #include <bfd.h> //#include <libiberty.h> #include "config.h" #include "fenris.h" #include "ioctls.h" #include "libdisasm/libdis.h" #include "fenris-decl.h" #include "fdebug.h" #include "hooks.h" #ifdef PROFILE #define static #define inline #endif /* PROFILE */
138
#ifdef MINIMAL #define inline #endif /* MINIMAL */
// including allocs.h will automagically turn every malloc, realloc, free // and strdup into my_malloc,my_realloc,my_free and my_strdup
respectively
// you can override this by uncommenting the following line: // #define USE_ORIGINAL_ALLOCS (but you don't want to do it for
Fenris,
// otherwise, it'll break into tiny pieces and cut you badly).
#include "allocs.h" #include "libfnprints.h" #ifndef RTLD_NODELETE // Damn damn damn. Bury me deep. #define RTLD_NODELETE 0 #define DO_NOT_DLCLOSE 1 #endif /* not RTLD_NODELETE */ #define CURPCNT(x) current->pstack[current->nest][(int)current-
>pst_top[current->nest]+x]
char verybigbuf[200000]; // output buffer extern int break_stopped; // The process is stopped. extern int break_continuing; struct user_regs_struct r; // Current process: registers unsigned char op[8]; // Current process: eip[0..8] int pid; // Current process: pid int in_libc; // Current process: eip in LIBCSEG? unsigned int caddr; // Current process: CALL dest addr unsigned int start_eip, stop_eip; static char fnm_buf[MAXDESCR]; // Local function name struct fenris_process ps[MAXCHILDREN]; // Traced process table struct fenris_process* current; // Currently traced proces
139
char T_forks, T_execs,T_nocnd,T_nosym, // Execution options T_noindent,T_nodesc,T_nomem,T_nosig,T_goaway,
T_noskip,T_addip,T_atret=2,T_wnow,T_alwaysret,*T_dostep,T_nolast;
unsigned char be_silent; #ifdef HEAVY_DEBUG unsigned int oldip; unsigned char oldop[8]; #endif /* HEAVY_DEBUG */ char nonstd; char is_static; char already_main; extern int blocking_syscall; char* running_under_ncaegir; int runasuid, runasgid; char* runasuser;
FILE* ostream; // Output stream int innest = PRETTYSMALL; int STACKSEG,CODESEG; const char* scnames[256]= { 0, #include "scnames.h" 0 };
#define MPS (MAXFNAME*2) #define RD() reset_pdescr() #define DD() dump_pdescr(0) static char pdescr[MAXPDESC+1]; #define reset_pdescr() pdescr[0]=0 struct hacking_table { unsigned int ip;
140
unsigned int ad; unsigned char va; };
struct hacking_table reptable[MAXREP]; int reptop=0;
static void nappend(char* dst,const char* src,int max) { int i; i=max-strlen(dst)+2; if (i<=0) return; strncat(dst,src,i); }
#define check_doret() if (current->doret) { debug("\n"); current-
>doret=0; }
char fatal_there; extern int sd; extern char break_shutup;
/*********************************************************
***
* This is our fatal error handling routine. We have three * * kinds of call scenarios, self-explainatory. *
************************************************************/
extern char test_leaks;
Соседние файлы в предмете [НЕСОРТИРОВАННОЕ]