There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

National Institute of Standards and Technology における超伝導研究及び生活

With the establishment of the AES the need for new block ciphers has been greatly diminished; for almost all block cipher applications the AES is an excellent and preferred choice. However, despite recent implementation advances, the AES is not suitable for extremely constrained environments such as RFID tags and sensor networks. In this paper we describe an ultra-lightweight block cipher, present . Both security and hardware efficiency have been equally important during the design of the cipher and at 1570 GE, the hardware requirements for present are competitive with today's leading compact stream ciphers.

/pdf/present-an-ultra-lightweight-block-cipher-33ym33ql7r.pdf

PRESENT: An Ultra-Lightweight Block Cipher

The objective of this paper is to give a comprehensive introduction to applied cryptography with an engineer or computer scientist in mind. The emphasis is on the knowledge needed to create practical systems which supports integrity, confidentiality, or authenticity. Topics covered includes an introduction to the concepts in cryptography, attacks against cryptographic systems, key use and handling, random bit generation, encryption modes, and message authentication codes. Recommendations on algorithms and further reading is given in the end of the paper. This paper should make the reader able to build, understand and evaluate system descriptions and designs based on the cryptographic components described in the paper.

[서평]「Applied Cryptography」

This paper proposes a systematic security evaluation of cryptographic hardware against C safe-error attacks. Using the graph representation of a design, we provide a simple and efficient method to detect possible C safe-errors. Exposing possible vulnerabilities at an early stage of a design process, this method avoids costly design re-spins and reduces time-to-market. As a proof of concept, we apply the method to two well-known exponentiation algorithms: square-and-multiply-always and the Montgomery ladder.

Systematic security evaluation method against C safe-error attacks

Wireless sensor networks consist of tiny senor nodes with limited computing and communicating capabilities and, more importantly, with limited energy resources. In this chapter we evaluate the power consumption of Public-key algorithms and investigate whether these algorithms can be used within the power constrained sensor nodes. We evaluate conventional digital signature schemes and encryption schemes, one-time signature schemes and Public-key authentication schemes.

Public-key Primitives

True Random Number Generators (TRNGs) are indispensable in modern cryptosystems. Unfortunately, to guarantee high entropy of the generated numbers, many TRNG designs require a complex implementation procedure, often involving manual placement and routing. In this work, we introduce, analyse, and compare three dynamic calibration mechanisms for the COherent Sampling ring Oscillator based TRNG: GateVar, WireVar, and LUTVar, enabling easy integration of the entropy source into complex systems. The TRNG setup procedure automatically selects a configuration that guarantees the security requirements. In the experiments, we show that two out of the three proposed mechanisms are capable of assuring correct TRNG operation even when an automatic placement is carried out and when the design is ported to another Field-Programmable Gate Array (FPGA) family. We generated random bits on both a Xilinx Spartan 7 and a Microsemi SmartFusion2 implementation that, without post processing, passed the AIS-31 statistical tests at a throughput of 4.65 Mbit/s and 1.47 Mbit/s, respectively.

Design and Analysis of Configurable Ring Oscillators for True Random Number Generation Based on Coherent Sampling

The rapid and widespread deployment of electronic devices operating in the field is bringing security issues into the spotlight. Fault injection, for instance, is a class of attacks that allows adversaries to bypass security-related capabilities by tampering with the normal functioning of a device. In this paper we describe a setup capable of faulting integrated circuits by exposing them to a pulsed magnetic field. The magnetic field is generated by discharging a pulse forming network made from a transmission line over an injection probe. The discharge is triggered by a spark gap based switch. We describe the mechanisms behind the different circuit components and evaluate the performance of the setup in practice. To the best of our knowledge, this is the first time a spark gap switch is used to build an electromagnetic (EM) pulse fault injection setup.

/pdf/design-and-evaluation-of-a-spark-gap-based-em-fault-129hnpc3rt.pdf

Design and Evaluation of a Spark Gap Based EM-fault Injection Setup

Physically Unclonable Functions (PUFs) provide a unique signature for integrated circuits (ICs), similar to a fingerprint for humans. They are primarily used to generate secret keys, hereby exploiting the unique manufacturing variations of an IC. Unfortunately, PUF output bits are not perfectly reproducible and non-uniformly distributed. To obtain a high-quality key, one needs to implement additional post-processing logic on the same IC. Fuzzy extractors are the well-established standard solution. Pattern Matching Key Generators (PMKGs) have been proposed as an alternative. In this work, we demonstrate the latter construction to be vulnerable against manipulation of its public helper data. Full key recovery is possible, although depending on system design choices. We demonstrate our attacks using a 4-XOR arbiter PUF, manufactured in 65nm CMOS technology. We also propose a simple but effective countermeasure.

/pdf/attacking-puf-based-pattern-matching-key-generators-via-4x01szgwj7.pdf

Ingrid Verbauwhede

Papers

Systematic security evaluation method against C safe-error attacks

Public-key Primitives

Design and Analysis of Configurable Ring Oscillators for True Random Number Generation Based on Coherent Sampling

Design and Evaluation of a Spark Gap Based EM-fault Injection Setup

Attacking PUF-Based Pattern Matching Key Generators via Helper Data Manipulation.