Identification of Bugs and Vulnerabilities in TLS Implementation for Windows Operating System Using State Machine Learning

TLS protocol is an essential part of secure Internet communication. In past, many attacks have been identified on the protocol. Most of these attacks are due to flaws in protocol implementation. The flaws are due to improper design and implementation of program logic by programmers. One of the widely used implementation of TLS is SChannel which is used in Windows operating system since its inception. We have used protocol state fuzzing to identify vulnerable and undesired state transitions in the state machine of the protocol for various versions of SChannel. The client as well as server components have been analyzed thoroughly using this technique and various flaws have been discovered in the implementation. Exploitation of these flaws under specific circumstances may lead to serious attacks which could disrupt secure communication. In this paper, we analyze state machine models of TLS protocol implementation of SChannel library and describe weaknesses and design flaws in these models, found using protocol state fuzzing.Comment: 9 pages, 8 figures, 1 tabl

AN AYURVEDIC REVIEW ON JANAPADODHWAMSA

Ayurveda the eternal life science from many centuries proved to be the most efficient tool in the health management system. It gives more weightage to the prevention than the cure. Janapadodhwamsa is one among the unique concept described in Ayurveda treatises which literally means demolition or annihilation of people or community. Acharya Charaka called it Janapadodhwamsa, Acharya Sushruta called it Maraka, and Acharya Bhela called it Janamaar. There are four factors that have been described which are common and essential for every living being, that is, Vayu (air), Jala (water), Desha (land), and Kaala (season). Among these four factors, Kaala is mainly main factor. Any abnormal alteration in these four factors can significantly influence individual or community or environment or all of them together. Vitiation of these four common factors is the cause for Janapadodhwamsa. Foremost reason for Janapadodhwamsa has been described as Adharma (immorality) and the root cause of Adharma is said to be Pragyaparadha (delinquency of wisdom). Considering the note worthiness of Janapadodhwamsa, a whole chapter has been depicted in CharakaSamhita illustrating its onset, causes, peculiar features, and management. Its causative agents, method of prevention has been clearly explained. To manage Janapadodhvamsa, it is advised to include the usage of Rasayana therapy, Panchkarama procedures, SadvritPaalan (code of right conducts), and Aachara Rasayana, that is, behavioral therapy

RAZOR A Lightweight Block Cipher for Security in IoT

Rapid technological developments prompted a need to do everything from anywhere and that is growing due to modern lifestyle. The Internet of Things (IoT) technology is helping to provide the solutions by inter-connecting the smart devices. Lightweight block ciphers are deployed to enable the security in such devices. In this paper, a new lightweight block cipher RAZOR is proposed that is based on a hybrid design technique. The round function of RAZOR is designed by mixing the Feistel and substitution permutation network techniques. The rotation and XOR based diffusion function is applied on 32-bit input with 8 branches and branch number 7 to optimize the security. The strength of RAZOR is proved against differential, linear, and impossible differential attacks. The number of active S-boxes in any 5-round differential characteristic of RAZOR is 21 in comparison to the 10, 6, 4, 7, and 6 for PRESENT, Rectangle, LBlock, GIFT, and SCENERY respectively. RAZOR provides better security than the existing lightweight designs. The average throughput of 1.47 mega bytes per second to encrypt the large files makes it a better choice for software oriented IoT applications

Energy Efficiency Comparative Analysis of Different Routing Protocol In MANET for Healthcare Environment

Now a day ad hoc mobile networks have several routing protocols, but every protocol has its own advantages and limitations therefore our main aim is to meet maximum performance using advance algorithm. Some are good in a small network; some are suitable in large networks, and some give better performance in location or global networks. Today advance and innovative applications for health care environments which are based on a wireless network are being developed in the commercial sectors. In our research work ECHERP framework gives a better performance as compared to other routing protocol. Designing WSN with this architecture in mind will enable designers to balance the energy dissipation and optimize the energy consumption among all network constituents because energy is one of the most crucial factor and sustain the network lifetime for the intended application. By categorizing the overall WSN system into sub region, components of each region were extracted in terms of their dominant factors, followed by a mathematical formula as a total energy cost function in terms of their constituents. As in our base paper three protocols are used which are DSR, DSDV and AODV and out of these DSR has best parameters on comparing diverse parameters has maximum remaining energy. But in our research a new protocol ECHERP is integrated in NS2 and then we compared these four protocols and we found that ECHERP have optimized values of parameters

Modeling Large S-box in MILP and a (Related-key) Differential Attack on Full Round PIPO-64/128

Mixed integer linear programming (MILP) based tools are used to estimate the strength of block ciphers against the cryptanalytic attacks. The existing tools use partial difference distribution table (p-DDT) approach to optimize the probability of differential characteristics for large (≥8-bit) S-box based ciphers. We propose to use the full difference distribution table (DDT) with the probability of each possible propagation for MILP modeling of large S-boxes. This requires more than 16 variables to represent the linear inequalities of each propagation and corresponding probabilities. The existing tools (viz. Logic Friday) cannot handle the linear inequalities in more than 16 variables. In this paper, we present a new tool (namely MILES) to minimize the linear inequalities in more than 16 variables. This tool reduces the number of inequalities by minimizing the truth table corresponding to the DDT of S-box. We use our tool to minimize the linear inequalities for 8-bit S-boxes (AES and SKINNY) and get better results than existing tools. We show the application of MILES on 8-bit S-box based lightweight block cipher PIPO. There are 20621 inequalities in 23 variables corresponding to the possible propagations in DDT and these are minimized to 6035 inequalities using MILES. MILP model based on these linear inequalities is used to optimizethe probability of differential characteristics for round-reduced PIPO. MILP model based on these inequalities is used to optimize the probability of differential and impossible differential characteristics for PIPO-64/128 reduced to 9 and 4 rounds respectively. We present an iterative 2-round related-key differential characteristic with the probability of 2^{-4} and that is used to construct a full round related-key differential distinguisher with the probability of 2^{-24}. We present a major collision in PIPO-64/128 which produces the same ciphertext (C) by encrypting the plaintext (P) under two different keys

Differential-ML Distinguisher: Machine Learning based Generic Extension for Differential Cryptanalysis

Differential cryptanalysis is an important technique to evaluate the security of block ciphers. There exists several generalisations of differential cryptanalysis and it is also used in combination with other cryptanalysis techniques to improve the attack complexity. In 2019, usefulness of machine learning in differential cryptanalysis is introduced by Gohr to attack the lightweight block cipher SPECK. In this paper, we present a framework to extend the classical differential distinguisher using machine learning (ML) based differential distinguisher. We propose a novel technique to construct differential-ML distinguisher for Feistel, SPN and ARX structure based block ciphers. We demonstrate our technique on lightweight block ciphers SPECK, SIMON & GIFT64 and construct differential-ML distinguishers for these ciphers. Data complexity for 9-round SPECK, 12-round SIMON & 8-round GIFT64 is reduced from 2^31 to 2^21, 2^34 to 2^22 and 2^28 to 2^22 respectively. The 12-round differential-ML distinguisher for SIMON is first distinguisher with data complexity less than 2^32