Lightweight Cryptography Algorithms for Embedded Systems Online Tutoring
1.0 Introduction to Embedded Systems:
Embedded systems prevail in this digitalized world in various forms extending from automobiles, home automation, wireless avionics or sensor nodes. These embedded systems (ESs) are being deployed in form of miniature wearable nodes as well as large industrial installation (Manifavas, et al., 2013). The ESs can be found in form of tiny chips installed in video cables, keyboards, controllers, mobile phones. ESs also differ vastly from being used as normal desktop and server systems although these do not directly perform any computing process, but tare adapted to perform under domain-specific areas for carrying on special dedicated tasks (Rana, et al., 2018). The embedded system usually contains the input receptor and an actuator for converting the input data into an output as per the embedded code in the device.
[hbupro_banner id=”6299″]2.0 Need for Lightweight Cryptography Algorithms
Embedded devices have the inherited limitation when it comes to processing power, memory, storage and energy. In this instance, cryptography has been widely applied for directly impacting the overall ESs in terms of size, cost, speed and power consumption (Buchanan, 2017). Cryptography means “secret writing” (Rana et al., 2018). It allows the users to communicate the information electronically using the encrypted codes that can only be deciphered by the expected user. The traditional cryptography solutions do provide the high levels of security, while ignoring the requirements of constrained devices (Manifavas, et al., 2013). The limitation of memory and space has given rise to extensive application of lightweight cryptography (LWC) and pushed the researchers in designing cryptography for devices with constrained capabilities in terms of hardware design, software, connectivity and power supply (Rana, et al., 2018).
In 2007, extensive research had been made on LWC for evaluating the hardware and software implementation in lightweight symmetric as well as lightweight asymmetric cryptography (Manifavas et al., 2013). Today, with widespread utilization of the small electronic devices including smart cards, Radio-Frequency Identification (RFID) tags, sensory nodes and industrial controllers, concerns with security and privacy have risen extensively. Hence, the cryptography mechanisms are challenging to be implemented due to rising concerns of security, memory safety and storage capacity of small devices (Rana, et al., 2018). Optimization of security, performance, memory safety and resource requirements make it difficult for conventional cryptography algorithms to be implemented in resource-constrained devices. The table below shows the device spectrums that utilize conventional cryptography and LWC (Rana, et al., 2018).
The increased complexity of LWC has mandated the need for designing of such algorithms that can save memory, save communication bandwidth and are fast to execute without affecting the overall performance of the ESs (Rana, et al., 2018). With rapid growth of the ESs and the increased connections requirements across different devices over Wi-Fi or Bluetooth, the devices have become susceptible of physical attacks (Xia, 2019). In recent times, the new attacks targeting memory safety have been recognized that could expose the vulnerability of specific device easily. According to Xia (2019), the Broadcom Wi-Fi’s vulnerability disclosure in 2017 have already allowed the execution of several arbitrary codes installed on Wi-Fi chips of many mobile phones.
For LWCs, the designers have to trade-off between cost, performance and the security as it is generally easy to optimize any two of the given three dimensions i.e. cost and performance, security and cost and security and performance (Buchanan, 2017). For securing a high-performance hardware implementation of LWC, a pipelined side-channel-resistant architecture can be utilized. However, this would result in high memory storage and high costs. While, on the other side, it is possible to design a low-cost hardware with the limitation of low performance. The high demand of saving energy, memory and energy used via RSA, AES, MD and SHA has posed need for designing LWC that is specially tuned for constrained devices (Buchanan, 2017).
Most of the conventional cryptographic algorithms do not support low-resource smart devices. According to Singh et al. (2017), the RSA algorithm 1204-bit cannot be used in implementation of RFID tags. Lee & Lim (2014) analyzed AES, TWINE, PRESENT and HIGHT symmetric lightweight algorithm for applying in ESs. The AES utilizes Rijndael cipher in three versions i.e. AES-128, AES-256 and AES-192 for providing a solution in CoAP. Whereas, TWINE utilizes Feistel structure on sub key by applying 4 by 4 S-Box. (Lee & Lim, 2014) TWINE is considered to be more complicated as compared to HIGHT and CLEFIA whereas HIGHT is simple and basic application for Feistel networking. HIGHT, despite of being simple, has high saturation attacking vulnerability. Similarly, PRESENT uses the lightweight algorithms for security purposes with a block length of 64 bits and keys of 80 and 128 bits (Lee & Lim, 2014).
[hbupro_banner id=”6296″]According to Singh et al. (2017), Elliptic Curve Cryptography (ECC) is a smaller key sized asymmetric LWC with fast processing speed and lesser memory space. ECC allows the algorithms to be processed at speed in small area of hardware that leads to faster computation. According to Eisenbarth & Kumar (2007), ECC can be easily applied to constrained devices. RSA is another algorithm of cryptography that is usually not considered to be LWC due to its large key size. However, RSA provides higher security and maintains the privacy of data of the users (Eisenbarth, et al., 2007). The table below shows the symmetric and asymmetric LWCs that can be applied in constrained devices along with their code lengths, structure, key sizes and possible security attacks (Singh et al., 2017).
LWCs do provide integrity and confidentiality while consuming less memory space and computation power. Although asymmetric LWCs do have bigger key sizes, yet they provide strong security than the symmetric LWCs algorithms. Therefore, by considering all different aspects of both symmetric and asymmetric LWCs, a hybrid LWC algorithm has been proposed that would combine the features of both symmetric and asymmetric algorithms in such a way that the proposed algorithm will minimize computation time, be fast efficient, have higher security and consume less power.
3.0 Proposed Hybrid Lightweight Algorithm
The hybrid lightweight algorithm (HLA) has already been proposed by Singh et al. (2017) to be applied in smart spaces i.e. smart city, smart homes, smart factory and smart hospitals. The figure 1 below shows the flowchart of HLA with four inputs i.e. data size, memory space, battery power and computation power. Each of the parameters’ threshold level can be checked and calculated by using specific algorithms. In order to design a faster and reliable file system, the HLA uses the memory technologies that can combine non-volatility of flash and RAM’s speed (Singh, et al., 2017). Due to continuous writing of data in flash memory, the overall writing speed decreases in HLA.
Near-Threshold-Computing (NTC) method is used in HLA due to which the electronic devices can operate at lower voltage levels thus reducing the overall energy consumption (Singh, et al., 2017). According to Singh et al. (2017), NTC can allow future computer systems to operate at much lower voltages that can likely reduce the overall energy consumption by 10 to 100 times. In the proposed HLA system, two encryption codes can be deployed i.e. LWC asymmetric encryption algorithms and LWC symmetric encryption algorithms. By combining these two algorithms, the ESs will be improved as it will reduce key size, key length, code size and block size altogether.
In the proposed HLA system, Wireless Sensor Network (WSN), Internet of Things (IoT) and RFID will be deployed. All of these systems are focused towards low-constrained devices hence this system will be suitable to be deployed in constrained area whilst increasing ES performance and processing speed. The proposed HLA system would go through four analysis phases i.e. data size, battery power, memory space and computation power.
In the first phase, the size of data to be transmitted would be analyzed using different cryptographic algorithms followed by analysis of battery power consumption requirement. At the third phase, the HLA will analyze the memory requirement for data computation. According to Zegers et al. (2015), HLA will use the memory size analysis for determining the right cryptographic encryption code to be deployed. Final phase would require HLA to combine the data size analysis, battery power analysis and memory space analysis to consider the right amount of computation power required. According to Davy (2003), algorithms with more computation power tend to execute data faster and efficiently.
After going through the phases, the HLA would see if the size of data is larger than set threshold level, it would be considered to be encrypted using LWC algorithm otherwise, it will be passed towards being further analyzed. The proposed HLA system will utilize the calculation methods like figure-of-merit (FoM), design throughput and efficiency ratios for considering the computational power and performance efficiency of the system (Masram et al., 2014; Kim et al., 2016; Xiao et al., 2016; Puthal et al., 2017). The proposed scheme is well designed for being deployed in low-constrained devices that have constricted battery power, low memory space and limited resources.
4.0 PREESNT-PERMS Cipher as HLA:
The proposed HLA system not only allow fluent implicit communication amongst different parts of the system, but it also allows the system operation in simulated as well as real entities. Through HLA, the data and communication will get substantial security in the digital world along while the overall complexity of computation will be kept at minimum. Thorat & Inamdar (2018) used HLA by deploying PRESENT and PERMS ciphers (one is symmetric and one is asymmetric) for improving the performance of the ESs. Thus, it has been proposed in the current report that for deploying HLA in ESs, PRESENT-PERMS cipher can be utilized effectively (see figure 2 below for the block diagram of proposed HLA).
After the choice has been made on whether the data be encrypted using symmetric or asymmetric LWC, it will be allocated to either PRESENT or PERMS Ciphers. PERMS has been chosen because it has superior instructions in terms of CPU cycles as compared to GRP system (Naif, et al., 2019). PERMS has been chosen because it can perform the arbitrary permutations in short time as compared to other methods as well as it takes lesser CPU cycles for performing computation. Along with its high speed, PERMS can also provide the good security property that makes it a good potential candidate for carrying on LWC (Khurana, et al., 2013). However, it has been identified that for any type of block cipher, there is a continuous need of creating linear and non-linear layers. In order to do the layering, PRESENT Cipher has been selected for HLA due to it being an ISO standard proven cipher (Bansod, et al., 2014). In the current proposed HLA, all the permutation layering will be done by PERMS and non-linear layering will be performed by the PRESENT S-Box. It has been reported by Thorat & Inamdar (2018) that PERMS have high cryptanalysis properties, so it will be able to prevent any brute-force attack on system.
The PRESENT-PERMS Cipher has been found out to be resistant against the differential and linear cyrptanalysis as discussed by Thorat & Inamdar (2018). As it was found by Thorat & Inamdar (2018), the PRESENT-PERMS Ciphers required lowest GEs and energy for carrying on with the encryption as compared to other ciphers (see table 3 below for comparison of Ciphers against PRESENT-PERMS). Moreover, PRESENT-PERMS Ciphers also require least CPU cycles against the CLEFIA requirements (see figure 3 below for comparison of different Ciphers in terms of CPU cycles).
In light of the above research analysis on LWCs related to PERMS and PRESENT S-Box, it can be concluded that the proposed HLA system will strike the perfect balance in providing higher security, throughput, productiveness and compactness when applied to ESs. Moreover, it can also be concluded that the use of PRESENT S-Box as LWC can reduce the power consumption and allow communication and calculations to be carried out at faster rate. Hence, based on analysis, it can be said that the proposed novel approach will have a positive effect on the LWCs application in ESs.
5.0 REFERENCES
Bansod, G., Raval, N. & Pisharoty, N., 2014. Implementation of a New Lightweight Encryption Design for Embedded Security. IEEE Transactions on Information Forensics and Security , 10(1), pp. 142-151.
Buchanan, W. J., 2017. Lightweight cryptography methods. Journal of Cyber Security Technology , 1(3-4).
Davy, A., 2003. Components of a smart device and smart device interactions. Telecommunications Software and Systems Group , 1(4), pp. 1-18.
Eisenbarth, T. et al., 2007. A survey of lightweight-cryptography implementations. IEEE Design & Test of Computers, 24(6), pp. 522-533.
Khurana, S., Kolay, S., Rebeiro, C. & Mukhopadhyay, D., 2013. Lightweight cipher implementations on embedded processors. 8th International Conference on Design & Technology of Integrated Systems in Nanoscale Era (DTIS), pp. 82-87.
Kim, J. M., Lee, H. S., Yi, J. & Park, M., 2016. Power adaptive data encryption for energy-efficient and secure communication in solar-powered wireless sensor networks. Journal of Sensors, pp. 1-10.
Lee, J.-H. & Lim, D.-G., 2014. Parallel architecture for high-speed block cipher, HIGHT. International Journal of Security and Its Applications , 8(2), pp. 59-66.
Manifavas, C., Hatzivasilis, G., Fysarakis, K. & Rantos, K., 2013. Lightweight Cryptography for Embedded Systems – A Comparative Analysis. Data Privacy Management and Autonomous Spontaneous Security, pp. 333-349.
Masram, R., Shahare, V., Abraham, J. & Moona, R., 2014. Analysis and comparison of symmetric key cryptographic algorithms based on various file features. International Journal of Network Security & Its Applications, 6(4), p. 43.
Naif, J. R., Abdul-Majeed, G., -Majeed & Farhan, A. K., 2019. Secure IOT System Based on Chaos-Modified Lightweight AES. International Conference on Advanced Science and Engineering (ICOASE), pp. 1-6.
Puthal, D., Nepal, S., Ranjan, R. & Chen, J., 2017. A dynamic prime number based efficient security mechanism for big sensing data streams. Journal of Computer and System Sciences , 33(1), pp. 22-42.
Rana, S., Hossain, S., Shoun, H. I. & Kashem, M. A., 2018. An effective lightweight cryptographic algorithm to secure resource-constrained devices. Spectrum, 9(11).
Singh, S., Sharma, P. K., Moon, S. Y. & Park, J. H., 2017. Advanced lightweight encryption algorithms for IoT devices: survey, challenges and solutions. Journal of Ambient Intelligence and Humanized Computing, pp. 1-18.
Thorat, C. G. & Inamdar, V. S., 2018. Implementation of new hybrid lightweight cryptosystem. Applied Computing and Informatics, 15(2).
Xia, H., 2019. Capability Memory Protection for Embedded Systems. Diss. University of Cambridge.
Xiao, C., Wang, L., Zhu, M. & Wang, W., 2016. A resource-efficient multimedia encryption scheme for embedded video sensing system based on unmanned aircraft. Journal of Network and Computer Applications, Volume 59, pp. 117-125.
Zegers, W., Chang, S.-Y., Park, Y. & Gao, J., 2015. A lightweight encryption and secure protocol for smartphone cloud. IEEE Symposium on Service-Oriented System Engineering, pp. 259-266.
