Abstract. This paper presents the silicon chip ECCon 1 , an Elliptic Curve Cryptography processor for application in Radio-Frequency Identification. The circuit is fabricated on a 180 nm CMOS technology. ECCon features small silicon size (15K GE) and has low power consumption (8.57 µW). It computes 163-bit ECC point-multiplications in 296k cycles and has an ISO 18000-3 RFID interface. ECCon's very low and nearly constant power consumption makes it the first ECC chip that can be powered passively. This major breakthrough is possible because of a radical change in hardware architecture. The ECCon datapath operates on 16-bit words, which is similar to ECC instruction-set extensions. A number of innovations on the algorithmic and on the architectural level substantially increased the efficiency of 163-bit ECC. ECCon is the first demonstration that the proof of origin via electronic signatures can be realized on RFID tags in 180 nm CMOS and below.

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Increasing the efficiency of production and manufacturing processes is a key goal of initiatives like Industry 4.0. Within the context of the European research project AR-ROWHEAD, we enable and secure smart maintenance services. An overall goal is to proactively predict and optimize the Maintenance, Repair and Operations (MRO) processes carried out by a device maintainer, for industrial devices deployed at the customer. Therefore it is necessary to centrally acquire maintenance relevant equipment status data from remotely located devices over the Internet. Consequently, security and privacy issues arise from connecting devices to the Internet, and sending data from customer sites to the maintainer's back-end.In this paper we consider an exemplary automotive use case with an AVL Particle Counter (APC) as device. The APC transmits its status information by means of a fingerprint via the publish-subscribe protocol Message Queue Telemetry Transport (MQTT) to an MQTT Information Broker in the remotely located AVL back-end. In a threat analysis we focus on the MQTT routing information asset and identify two elementary security goals in regard to client authentication. Consequently we propose a system architecture incorporating a hardware security controller that processes the Transport Layer Security (TLS) client authentication step. We validate the feasibility of the concept by means of a prototype implementation. Experimental results indicate that no significant performance impact is imposed by the hardware security element. The security evaluation confirms the advanced security of our system, which we believe lays the foundation for security and privacy in future smart service infrastructures.

Recent smartphone platforms based on new operating systems, such as iOS, Android, or Windows Phone, have been a huge success in recent years and open up many new opportunities. Unfortunately, 2011 also showed us that the new technologies and the privacy‐related data on smartphones are also increasingly interesting for attackers. Especially, the Android platform has been the favorite target for malware, mainly because of the openness of the platform, the ability to install applications from other sources than the Android Market, and the significant gains in market share. Although the processes of detecting and analyzing malware are well known from the PC world, where the arms race between attackers and defenders has continued for the past 15 years, they cannot be directly applied to smartphone platforms because of differences in the hardware and software architectures. In this paper, we first give an overview of the current malware situation on smartphone platforms with a special focus on Android and explain relevant malware detection and analysis methods. It turns out that most of the current malware relies on the installation by the user, who represents the last line of defense in malware detection. With these conclusions, we then present a new malware detection method that focuses on the information that the user is able to see prior to the installation of an application—the metadata within the platform's software market. Depending on the platform, this includes the application's description, its permissions, the ratings, or information about the developer. To analyze these data, we use sophisticated knowledge discovery processes and lean statistical methods. By presenting a wide range of examples based on real application metadata extracted from the Android Market, we show the possibilities of the new method. With the possibilities, we argue that it should be an essential part of a complete malware analysis/detection chain that includes other well‐known methods such as network traffic analysis, or static, or dynamic code inspection. Copyright © 2013 John Wiley & Sons, Ltd.

Controlling a biped robot with a high degree of freedom to achieve stable movement patterns is still an open and complex problem, in particular within the RoboCup community. Thus, the development of control mechanisms for biped locomotion have become an important field of research. In this paper we introduce a model-free approach of biped motion generation, which specifies target angles for all driven joints and is based on a neural oscillator. It is potentially capable to control any servo motor driven biped robot, in particular those with a high degree of freedom, and requires only the identification of the robot's physical constants in order to provide an adequate simulation. The approach was implemented and successfully tested within a physical simulation of our target system -the 19-DoF Bioloid robot. The crucial task of identifying and optimizing appropriate parameter sets for this method was tackled using evolutionary algorithms. We could show, that the presented approach is applicable in generating walking patterns for the simulated biped robot. The work demonstrates, how the important parameters may be identified and optimized when applying evolutionary algorithms. Several so evolved controllers were capable of generating a robust biped walking behavior with relatively high walking speeds, even without using sensory information. In addition we present first results of laboratory experiments, where some of the evolved motions were tried to transfer to real hardware.