# Securing IoT Devices against Differential-Linear (DL) Attack Used on Serpent Algorithm
## 3 Literature Review  
[PERSON] et al. [14] developed a Serpent as an algorithm to replace the Data Encryption Standard (DES) algorithm. The main purpose of developing Serpent was to increase the Avalanche Effect (AE) in order to confuse and frustrate attackers [15]. [PERSON] and [PERSON] [16] indicated that Serpent had been one of the most cryptographic algorithms used on IoT devices. In addition, [PERSON] et al. [12] demonstrated that Serpent had been used on IoT devices such as smart cards, Intel Pentium, and other 8-bit processors. [PERSON] and [PERSON] [17] indicated that nowadays, IoT devices such as smart cards play a critical function in everyone's life by delivering excellent services to the facet of the cyber world. IoT devices could also provide services such as intelligent management and monitoring. Additionally, [PERSON] and [PERSON] [17] indicated that there has been an increase in 5G network dependence for seamless services; IoT devices had been attracting much attention to researchers. However, IoT devices in this miscellaneous 5G network are vulnerable to many attacks. It was already stated that most IoT devices encrypt data using Serpent. [PERSON] and [PERSON] [18] tried to reduce attacks found on Serpent by using differential factors. The differential factor attacked the key size using differential cryptanalysis and time complexity. [PERSON] et al. [19] developed more accurate results of the DL attack on round number 11 of Serpent found on IoT devices. The results were found using statistical analysis, mathematical theory, and experimental criticisms, which showed and declared that early attacks had exaggerated effects. [PERSON] et al. [13] developed a Simple Power Analysis attack (SPA) to attack an 8-bit smart card encrypted by Serpent. The results showed that Serpent key generation was convincing to a side-channel attack because of a linear feedback shift register (LFSR). LFSRs are very common in most cryptographic algorithms; suggestions were given that Serpent's LFSRs should be carefully modified and guesstimated in order to reduce attacks. [PERSON] et al. [20] presented a linear approximation on round number nine of Serpent using a statistical theory of \(1/2+2^{-52}\) probability. Furthermore, [PERSON] et al. [20] continued using the theory to attack round number 10 of Serpent using all key sizes: data complexity of \(2^{118}\) and time taken of \(2^{89}\) seconds. A random variable of the probability was also applied on the first attack against round number 11 of Serpent using 192-bit and 256-bit key lengths, which needed an equal quantity of data and \(2^{187}\) seconds taken [20]. [PERSON] et al. [6] developed a _DLCT_ used to attack Serpent's secret keys. [PERSON] et al. [7] analyzed and observed a _DLCT_ to obtain absolute indicators of Serpent weaknesses. [PERSON] et al. [7] expanded the analytic results found on the _DLCT_ and DL attacks. [PERSON] et al. [7] improved the observations about the notion of _DLCT_ and DL attack. According to the results found by [PERSON] et al. [7], the _DLCT_ approach method was found to be similar with respect to auto-correlation spectrum entities, and a conclusion was drawn that _DLCT_ was nothing else but an Auto Correlation Table (ACT). Furthermore, [PERSON] et al. [7] indicated that the ACT spectrum was invariable under any equivalence similarities and was not invariant under changes. [PERSON] et al. [6] indicated that the _DLCT_ was formulated expeditiously using the fast Fourier transform. Additionally, [PERSON] et al. [6] applied the _DLCT_'s strength to enhance DL attacks on ICEPOLE and DES in order to justify published experimental findings on CAESAR and Serpent. The results showed that _DLCT_ was not abided by the DL attack model. Little has been conducted with respect to securing Serpent against DL attacks.