Efficient Stack Smashing Attack Detection Method Using DSLR


KIPS Transactions on Computer and Communication Systems, Vol. 12, No. 9, pp. 283-290, Sep. 2023
https://doi.org/10.3745/KTCCS.2023.12.9.283,   PDF Download:
Keywords: Stack Smashing, Buffer Overflow, Canary, Random Value, XOR Bit
Abstract

With the recent steady development of IoT technology, it is widely used in medical systems and smart TV watches. 66% of software development is developed through language C, which is vulnerable to memory attacks, and acts as a threat to IoT devices using language C. A stack-smashing overflow attack inserts a value larger than the user-defined buffer size, overwriting the area where the return address is stored, preventing the program from operating normally. IoT devices with low memory capacity are vulnerable to stack smashing overflow attacks. In addition, if the existing vaccine program is applied as it is, the IoT device will not operate normally. In order to defend against stack smashing overflow attacks on IoT devices, we used canaries among several detection methods to set conditions with random values, checksum, and DSLR (random storage locations), respectively. Two canaries were placed within the buffer, one in front of the return address, which is the end of the buffer, and the other was stored in a random location in-buffer. This makes it difficult for an attacker to guess the location of a canary stored in a fixed location by storing the canary in a random location because it is easy for an attacker to predict its location. After executing the detection program, after a stack smashing overflow attack occurs, if each condition is satisfied, the program is terminated. The set conditions were combined to create a number of eight cases and tested. Through this, it was found that it is more efficient to use a detection method using DSLR than a detection method using multiple conditions for IoT devices.


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Cite this article
[IEEE Style]
D. Y. Hwang and D. Yoo, "Efficient Stack Smashing Attack Detection Method Using DSLR," KIPS Transactions on Computer and Communication Systems, vol. 12, no. 9, pp. 283-290, 2023. DOI: https://doi.org/10.3745/KTCCS.2023.12.9.283.

[ACM Style]
Do Yeong Hwang and Dong-Young Yoo. 2023. Efficient Stack Smashing Attack Detection Method Using DSLR. KIPS Transactions on Computer and Communication Systems, 12, 9, (2023), 283-290. DOI: https://doi.org/10.3745/KTCCS.2023.12.9.283.