Power analysis on smartcard algorithms using simulation

This paper presents the results from a power analysis of the AES and RSA algorithms by\ud simulation using the PINPAS tool. The PINPAS tool is capable of simulating the power\ud consumption of assembler programs implemented in, amongst others, Hitachi H8/300\ud assembler. The Hitachi H8/300 is a popular CPU for smartcards. Using the PINPAS tool, the\ud vulnerability for power analysis attacks of straightforward AES and RSA implementations is\ud examined. In case a vulnerability is found countermeasures are added to the implementation\ud that attempt to counter power analysis attacks. After these modifications the analysis is\ud performed again and the new results are compared to the original results

Attacking AES-Masking Encryption Device with Correlation Power Analysis

Modern communication system use cryptography algorithm to ensure data still confidentiality, integrity, and authentic. There is a new vulnerability in a cryptographic algorithm when implemented on a hardware device. This vulnerability is considered capable of uncovering a secret key used in a cryptographic algorithm. This technique is known as a power analysis attack. Previous and other research introduces countermeasure to countering this new vulnerability. Some researchers suggest using logic level with encoding the AES. The countermeasure using logic is meager cost and efficient. The contribution of this paper is to analyze CPA on encryption device that has been given logic level countermeasure. Our finding of this paper is the use of encoding with one-hot masking technique does not provide the maximum countermeasure effect against CPA-based attacks. In this research, CPA attack can be successfully revealing the AES secret-ke

FPGA based remote code integrity verification of programs in distributed embedded systems

The explosive growth of networked embedded systems has made ubiquitous and pervasive computing a reality. However, there are still a number of new challenges to its widespread adoption that include scalability, availability, and, especially, security of software. Among the different challenges in software security, the problem of remote-code integrity verification is still waiting for efficient solutions. This paper proposes the use of reconfigurable computing to build a consistent architecture for generation of attestations (proofs) of code integrity for an executing program as well as to deliver them to the designated verification entity. Remote dynamic update of reconfigurable devices is also exploited to increase the complexity of mounting attacks in a real-word environment. The proposed solution perfectly fits embedded devices that are nowadays commonly equipped with reconfigurable hardware components that are exploited to solve different computational problems

Static and Dynamic Component Obfuscation on Reconfigurable Devices

Computing systems are used in virtually every aspect of our lives. Technology such as smart phones and electronically controlled subsystems in cars is becoming so commonly used that it is virtually ubiquitous. Sometimes, this technology can be exploited to perform functions that it was never intended to perform, or fail to provide information that it is supposed to protect. X-HIA was shown to be effective at identifying several circuit components in a significantly shorter time than previous identification methods. Instead of requiring a number of input/output pairings that grows factorially or exponentially as the circuit size grows, it requires only a number that grows polynomially with the size of the circuit. This allows for the identification of significantly larger circuits. Static protection techniques that are applied to the circuits do not increase the amount of time required to identify the circuit to the point that it is not feasible to perform that identification. DPR is implemented, and it is shown both that the overhead is not prohibitive and that it is effective at causing an identification algorithm to fail

Applications of Machine Learning in Cryptography: A Survey

Machine learning techniques have had a long list of applications in recent years. However, the use of machine learning in information and network security is not new. Machine learning and cryptography have many things in common. The most apparent is the processing of large amounts of data and large search spaces. In its varying techniques, machine learning has been an interesting field of study with massive potential for application. In the past three decades, machine learning techniques, whether supervised or unsupervised, have been applied in cryptographic algorithms, cryptanalysis, steganography, among other data-security-related applications. This paper presents an updated survey of applications of machine learning techniques in cryptography and cryptanalysis. The paper summarizes the research done in these areas and provides suggestions for future directions in research