
?????????? ???? ??????-?????????????? ?? ???????????????? ?????????? ?????????? ??????????????. ???????? ?? ??????-?????????????? ???????????????? ?????????? ???? ?????????????????????? ???????????????????? ?????????????? ????????????????????, ???? ?????????????????????? ???????????? ???????????????? ???????????????????? ???????????????????????????? ???????? ??????????, ???????????????????????????? ?????????? ???? ??????-???????????????? ?????? ???????????????? ???????? ???????????? ????????. ?????????????????????? ?????????????????????? ???? ?????????????????????????? ?????????????????????????? ????????????????? ???????????????????? ?????????????? ???????? ?????? ?????????????????? ?????????? ?????????????????????????? ??????????????. ???????????? ?? ?????????? ?????????? ?? ?????????????????? ???????? ???? ??????-?????????????? ?????? ???????????????????? ??????????????. ??????????????????????, ???????????????????? ???????????????? ?????????????????? ???????? ?????? ???????????????????? ??????????????, ???????????????? ?????????????????? ??????????????. ?????????? ?????????? ???????????? ???????????????????? ?? ?????????? ????????????????. ?????????? ???????? SQL-????????????????? ??? ?????????????????? ???????????? ?????????? ??????-????????????????, ?????? ?????????? ???????? ??????????. ?????????????????????????? ???????????????????????? ???????????? ?????????????????????????? ???????? SQL-????????????????? ???? ?????????????????? ?????????????????? ?????????? ??????????????, ???? ???????????????? ?????? ???????????????? ??????????. ?????? ???????????????? ?????????? ?????????????? ?????????????????????? ?????????????? ?? ?????????????? ??????????, ?????? ?????????? ?????????????????????????? ?? ???????????????? ???????? ????????????????????????. ???? ?????????????????? ?????????????????????????????? ???????????? ?????????? ???????????????? ?????????? ????????????????? SQL, ???????????????????????????? ???????? ????????????. ?????????? ???????????????????????????????? ????????????????, ???? ?????????? ?????????? ?????????????????? ?? ?????????????????????????? ???????????? ?????????????????? ???????????????? ???????????????? ?????????? ?????????? ?????????????????? ?????????????????????? ???????? SQL-?????????????????. ?? ?????????????????????????????? ???????????? ???????????????? ?????????? ????????????????, ???? ?????????????????????? ???? ?? ????????????, ?????? ?? ?? ?????????????????????? ????????????????, ?? ???????????? ?????????????? ??????????????, ???????????????????????????? ?????????????????? ???????? ???????? ???? ???????????????????? ??????????????. ???????????? ?? ???????????????????????????? ?? ???????????????? ???????????? ?????????? ?????????????? ????????????, ???????????? ?? ?????????? ?? ?????????? ??????????, ???? ?????????????????? ?????????????????? ?????? ?????????????????? ?????????? ???? ?????????????????????? ????????????.
Attacks to web applications are a relatively new type of attack. If the web application does not filter incoming parameters properly, then attackers can get the opportunity to falsify the database using the form on the web page or by changing other incoming data. Mathematical modeling and identification of information objects play an important role in solving problems of pattern recognition. One of these tasks is to detect attacks or normal requests for web applications. Studies on the detection of attacks or normal requests for web applications began relatively recently. Nevertheless, there is a lot of research in this direction. Attack of the form of SQL-injection is a common way of hacking web applications that have a database. Our paper proposes a mathematical method for identifying SQL-injection attacks using a function bounded below that depends on the input string. To build such a function, we used special characters and keywords that are often found in the construction of attacks by intruders. In our proposed method, we can detect SQL-injection attacks using a single character. Nevertheless, we experimentally show that the proposed detection method using a set of numerous symbols allows us to determine the vulnerability of the form of SQL-injection more accurately. In the proposed method, we created a character set that combines both attack and normal detections, and the previously known threshold, using the approximate data of the attackers and normal strings. According to our experiments with artificial data, the set contains a space, a semicolon, and the right bracket has worked well for a larger weight range for the attack and the normal string.
?? ???????????? ???????????? ?????????????????? ???????????????? ?????????????????????? ?????????? ???????????????? SQL ?? ?????????????? ?????????????? ?????????????????????????? ?? ???????? ???????????? ?????????????????????????? ?????????????????????????? ?????????????????? ?? ?????????????? ?????????????????????????? ????????????. ?? ???????????????????????? ???????????? ???????????? ?? ?????????????? ?????????????????? ???????????? ?????????????????? ?? ???????????????????? ?????????? ?????????? ???????????????? ?????? ?????????????????????????? ?????? ??????????, ?????? ?? ?????????????????????? ?????????????? ?? ?????????? ?????????????????? ??????????????. ???????????????? ?????????????????????????? ?? ???????????????????????????? ?????????????? ?????????? ???????????????? ????????????, ?????????? ?? ?????????????? ?? ???????????? ????????????, ?????????????? ???????????????? ???????????????? ?????? ?????????????????????????? ?????????? ?? ???????????????????? ????????????. ???????????? ?????????? ???????????? ?????????? ???????????? ?? ?????????????????????? ???? ?????????????????????? ????????????.
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| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
