Biblio

The use of biometrics in security applications may be vulnerable to several challenges of hacking. Thus, the emergence of cancellable biometrics becomes a suitable solution to this problem. This paper presents a one-way cancellable biometric transform that depends on 3D chaotic maps for face and fingerprint encryption. It aims to avoid cloning of original biometrics and allow the templates used by each user in different applications to be variable. The permutations achieved with the chaotic maps guarantee high security of the biometric templates, especially with the 3D implementation of the encryption algorithm. In addition, the paper presents a hardware implementation for this framework. The proposed algorithm also achieves good performance in the presence of low and moderate levels of noise. An experimental version of the proposed cancellable biometric system has been applied on FPGA model. The obtained results achieve a powerful performance of the proposed cancellable biometric system.

The deployment of smart devices in IoT applications are increasing with tremendous pace causing severe security concerns, as it trade most of private information. To counter that security issues in low resource applications, lightweight cryptographic algorithms have been introduced in recent past. In this paper we propose efficient hardware architecture of piccolo lightweight algorithm uses 64 bits block size with variable key size of length 80 and 128 bits. This paper introduces novel hardware architecture of piccolo-80, to supports high speed RFID security applications. Different design strategies are there to optimize the hardware metrics trade-off for particular application. The algorithm is implemented on different family of FPGAs with different devices to analyze the performance of design in 4 input LUTs and 6 input LUTs implementations. In addition, the results of hardware design are evaluated and compared with the most relevant lightweight block ciphers, shows the proposed architecture finds its utilization in terms of speed and area optimization from the hardware resources. The increment in throughput with optimized area of this architecture suggests that piccolo can applicable to implement for ultra-lightweight applications also.

Physical Unclonable Functions (PUFs) have been designed for many security applications such as identification, authentication of devices and key generation, especially for lightweight electronics. Traditional approaches to enhancing security, such as hash functions, may be expensive and resource dependent. However, modelling attacks using machine learning (ML) show the vulnerability of most PUFs. In this paper, a combination of a 32-bit current mirror and 16-bit arbiter PUFs in 65nm CMOS technology is proposed to improve resilience against modelling attacks. Both PUFs are vulnerable to machine learning attacks and we reduce the output prediction rate from 99.2% and 98.8% individually, to 60%.

Monitoring systems are essential to understand and control the behaviour of systems and networks. Cyber-physical systems (CPS) are particularly delicate under that perspective since they involve real-time constraints and physical phenomena that are not usually considered in common IT solutions. Therefore, there is a need for publicly available monitoring tools able to contemplate these aspects. In this poster/demo, we present our initiative, called CPS-MT, towards a versatile, real-time CPS monitoring tool, with a particular focus on security research. We first present its architecture and main components, followed by a MiniCPS-based case study. We also describe a performance analysis and preliminary results. During the demo, we will discuss CPS-MT's capabilities and limitations for security applications.

Cryptographic applications need high-quality random number generator (RNG) for strong security and privacy measures. This paper presents RNG based on a hydrogen gas sensor that is fabricated by using microfabrication techniques. The proposed approach extracts the thermal noise information as an entropy source from the gas sensor that is non-deterministic during its operation and using hash function SHA-256 as post processing. This non-deterministic noise is then processed to acquire a random number set fulfilling the NIST 800-22 statistical randomness test suite and it demonstrates that a gas sensor based RNG can provide high-quality random numbers. Secure data transfer is possible by having this method directly without any other hardware where hydrogen gas sensor needs to be used such as petrochemical field, fuel cells, and nuclear reactors.

This paper introduces a newly developed Object-Oriented Open Software Architecture designed for supporting security applications, while leveraging on the capabilities offered by dedicated Open Hardware devices. Specifically, we target the SEcube™ platform, an Open Hardware security platform based on a 3D SiP (System on Package) designed and produced by Blu5 Group. The platform integrates three components employed for security in a single package: a Cortex-M4 CPU, a FPGA and an EAL5+ certified Smart Card. The Open Software Architecture targets both the host machine and the security device, together with the secure communication among them. To maximize its usability, this architecture is organized in several abstraction layers, ranging from hardware interfaces to device drivers, from security APIs to advanced applications, like secure messaging and data protection. We aim at releasing a multi-platform Open Source security framework, where software and hardware cooperate to hide to both the developer and the final users classical security concepts like cryptographic algorithms and keys, focusing, instead, on common operational security concepts like groups and policies.

Security in smartphones has become one of the major concerns, with prolific growth in its usage scenario. Many applications are available for Android users to protect their applications and data. But all these security applications are not easily accessible for persons with disabilities. For persons with color blindness, authentication mechanisms pose user interface related issues. Color blind users find the inaccessible and complex design in the interface difficult to access and interpret mobile locks. This paper focuses on a novel method for providing color and touch sensitivity based dot pattern lock. This Model automatically replaces the existing display style of a pattern lock with a new user preferred color combination. In addition Pressure Gradient Input (PGI) has been incorporated to enhance authentication strength. The feedback collected from users shows that this accessible security application is easy to use without any major access barrier.

Transform based image steganography methods are commonly used in security applications. However, the application of several recent transforms for image steganography remains unexplored. This paper presents bit-plane based steganography method using different transforms. In this work, the bit-plane of the transform coefficients is selected to embed the secret message. The characteristics of four transforms used in the steganography have been analyzed and the results of the four transforms are compared. This has been proven in the experimental results.

This paper presents an entirely new RFID tag antenna design that incorporates the QR (Quick Response) code for security purposes. The tag antenna is designed to work at 2.45 GHz frequency. The RFID integrated QR code tag antenna is printed with an additive material deposition system that enables to produce a low cost tag antenna with extended security.

In order to enhance the supply chain security at airports, the German federal ministry of education and research has initiated the project ESECLOG (enhanced security in the air cargo chain) which has the goal to improve the threat detection accuracy using one-sided access methods. In this paper, we present a new X-ray backscatter technology for non-intrusive imaging of suspicious objects (mainly low-Z explosives) in luggage's and parcels with only a single-sided access. A key element in this technology is the X-ray backscatter camera embedded with a special twisted-slit collimator. The developed technology has efficiently resolved the problem related to the imaging of complex interior of the object by fixing source and object positions and changing only the scanning direction of the X-ray backscatter camera. Experiments were carried out on luggages and parcels packed with mock-up dangerous materials including liquid and solid explosive simulants. In addition, the quality of the X-ray backscatter image was enhanced by employing high-resolution digital detector arrays. Experimental results are discussed and the efficiency of the present technique to detect suspicious objects in luggages and parcels is demonstrated. At the end, important applications of the proposed backscatter imaging technology to the aviation security are presented.

Machine learning (ML) plays a central role in the solution of many security problems, for example enabling malicious and innocent activities to be rapidly and accurately distinguished and appropriate actions to be taken. Unfortunately, a standard assumption in ML - that the training and test data are identically distributed - is typically violated in security applications, leading to degraded algorithm performance and reduced security. Previous research has attempted to address this challenge by developing ML algorithms which are either robust to differences between training and test data or are able to predict and account for these differences. This paper adopts a different approach, developing a class of moving target (MT) defenses that are difficult for adversaries to reverse-engineer, which in turn decreases the adversaries' ability to generate training/test data differences that benefit them. We leverage the coevolutionary relationship between attackers and defenders to derive a simple, flexible MT defense strategy which is optimal or nearly optimal for a broad range of security problems. Case studies involving two distinct cyber defense applications demonstrate that the proposed MT algorithm outperforms standard static methods, offering effective defense against intelligent, adaptive adversaries.