An analysis and a comparative study of cryptographic algorithms used on the internet of things (IoT) based on avalanche effect

Ubiquitous computing is already weaving itself around us and it is connecting everything to the network of networks. This interconnection of objects to the internet is new computing paradigm called the Internet of Things (IoT) networks. Many capacity and non-capacity constrained devices, such as sensors are connecting to the Internet. These devices interact with each other through the network and provide a new experience to its users. In order to make full use of this ubiquitous paradigm, security on IoT is important. There are problems with privacy concerns regarding certain algorithms that are on IoT, particularly in the area that relates to their avalanche effect means that a small change in the plaintext or key should create a significant change in the ciphertext. The higher the significant change, the higher the security if that algorithm. If the avalanche effect of an algorithm is less than 50% then that algorithm is weak and can create security undesirability in any network. In this, case IoT. In this study, we propose to do the following: (1) Search and select existing block cryptographic algorithms (maximum of ten) used for authentication and encryption from different devices used on IoT. (2) Analyse the avalanche effect of select cryptographic algorithms and determine if they give efficient authentication on IoT. (3) Improve their avalanche effect by designing a mathematical model that improves their robustness against attacks. This is done through the usage of the initial vector XORed with plaintext and final vector XORed with cipher tect. (4) Test the new mathematical model for any enhancement on the avalanche effect of each algorithm as stated in the preceding sentences. (5) Propose future work on how to enhance security on IoT. Results show that when using the proposed method with variation of key, the avalanche effect significantly improved for seven out of ten algorithms. This means that we have managed to improve 70% of algorithms tested. Therefore indicating a substantial success rate for the proposed method as far as the avalanche effect is concerned. We propose that the seven algorithms be replaced by our improved versions in each of their implementation on IoT whenever the plaintext is varied.Electrical and Mining EngineeringM. Tech. (Electrical Engineering

Preventing cryptographic attacks used on the Internet of Things

Cryptographic attacks on Internet of Things (IoT) devices are not highly con sidered by the users of IoT. Most cryptographic algorithms commonly used on IoT devices are vulnerable to cryptographic attacks. Cryptography attacks refer to mathematical procedures to crack the secret key of the algorithm used on IoT devices. More needs to be done to prevent attacks on cryptographic algorithms used on IoT devices. The objectives of this study are: (i)To use the Khumbelo Difference Muthavhine (KDM) function to prevent Differen tial Cryptanalysis (DC) attacks in the AES algorithm used on IoT devices. (ii) To apply the Blocker function to prevent Differential-Linear Cryptanal ysis (DL) attacks in the Serpent algorithm used on IoT devices. (iii) To use the Khumbelo function to prevent Linear Cryptanalysis (LC), DC, DL, boomerang, truncated differential, meet-in-the-middle, and zero-correlation linear-distinguisher attacks in the Camellia algorithm used on IoT devices. (iv) Applying the Khumbelo function to protect IoT against LC, DC, DL, boomerang, truncated differential, meet-in-the-middle, and zero-correlation linear-distinguisher attacks. The KDM, Khumbelo, and Blocker functions prevented cryptographic attacks since all 8 x 8 S-Boxes were changed to 8 x 32 S-Box depending on the particular chapter. The analysis produced re markable results in preventing cryptographic attacks from IoT devices using the KDM, Khumbelo, and Blocker functions. The objectives of the study was to block the construction of distiguishers. Distinguishers are used by intrud ers as first step to conduct any cryptographic attack. Once the construction of distiguishers are blocked, therefore no attacks would be established. The study managed to block construction of distiguishers to 0% probability com pared to (i)50% of LC attacks, (ii) 50% of DL attacks, and (iii) 50% of DC attacks. The 8 x 32 S-Box was expected to build distinguishers from 2 8 x 232 = 256 x 4, 294, 967, 296 matrices with 1, 099, 511, 627, 776 elements. Due to computational space, an ordinary computer could not compute 256 x 4, 294, 967, 296 matrices.Electrical and Mining EngineeringD. Eng. (Electrical Engineering

Blocking Linear Cryptanalysis Attacks Found on Cryptographic Algorithms Used on Internet of Thing Based on the Novel Approaches of Using Galois Field (GF (<b>2³²</b>)) and High Irreducible Polynomials

Attacks on the Internet of Things (IoT) are not highly considered during the design and implementation. The prioritization is making profits and supplying services to clients. Most cryptographic algorithms that are commonly used on the IoT are vulnerable to attacks such as linear, differential, differential–linear cryptanalysis attacks, and many more. In this study, we focus only on linear cryptanalysis attacks. Little has been achieved (by other researchers) to prevent or block linear cryptanalysis attacks on cryptographic algorithms used on the IoT. In this study, we managed to block the linear cryptanalysis attack using a mathematically novel approach called Galois Field of the order (232), denoted by GF (232), and high irreducible polynomials were used to re-construct weak substitution boxes (S-Box) of mostly cryptographic algorithms used on IoT. It is a novel approach because no one has ever used GF (232) and highly irreducible polynomials to block linear cryptanalysis attacks on the most commonly used cryptographic algorithms. The most commonly used cryptographic algorithms on the IoT are Advanced Encryption Standard (AES), BLOWFISH, CAMELLIA, CAST, CLEFIA, Data Encryption Standard (DES), Modular Multiplication-based Block (MMB), RC5, SERPENT, and SKIPJACK. We assume that the reader of this paper has basic knowledge of the above algorithms

Preventing Differential Cryptanalysis Attacks Using a KDM Function and the 32-Bit Output S-Boxes on AES Algorithm Found on the Internet of Things Devices

Many Internet of Things (IoT) devices use an Advanced Encryption Standard (AES) algorithm to secure data stored and transmitted during the communication process. The AES algorithm often suffers DC (DC) attacks. Little has been done to prevent DC attacks, particularly on an AES algorithm. This study focuses on preventing Differential Cryptanalysis attacks. DC attacks are practiced on an AES algorithm that is found on IoT devices. The novel approach of using a Khumbelo Difference Muthavine (KDM) function and changing the 8 × 8 S-Boxes to be the 8 × 32 S-Boxes successfully prevents DC attacks on an AES algorithm. A KDM function is a newly mathematically developed function, coined and used purposely in this study. A KDM function was never produced, defined, or utilized before by any researcher except for in this study. A KDM function makes a new 32-Bit S-Box suitable for the new Modified AES algorithm and confuses the attacker since it comprises many mathematical modulo operators. Additionally, these mathematical modulo operators are irreversible. The study managed to prevent the DC attack of a minimum of 70% on AES and a maximum of 100% on a Simplified DES. The attack on the new Modified AES Algorithm is 0% since no S-Box is used as a building block