Biblio

Distributed Denial of Service (DDoS) attacks serve to diminish the ability of the network to perform its intended function over time. The paper presents the design, implementation and analysis of a protocol based upon a technique for address agility called DDoS Resistant Multicast (DRM). After describing the our architecture and implementation we show an analysis that quantifies the overhead on network performance. We then present the Simple Agile RPL multiCAST (SARCAST), an Internet-of-Things routing protocol for DDoS protection. We have implemented and evaluated SARCAST in a working IoT operating system and testbed. Our results show that SARCAST provides very high levels of protection against DDoS attacks with virtually no impact on overall performance.

We propose $μ$Leech, a new embedded trusted platform module for next generation power scavenging devices. Such power scavenging devices are already widely deployed. For instance, the Square point-of-sale reader uses the microphone/speaker interface of a smartphone for communications and as power supply. While such devices are used as trusted devices in security critical applications in the wild, they have not been properly evaluated yet. $μ$Leech can securely store keys and provide cryptographic services to any connected smart phone. Our design also facilitates physical security analysis by providing interfaces to facilitate acquisition of power traces and clock manipulation attacks. Thus $μ$Leech empowers security researchers to analyze leakage in next generation embedded and IoT devices and to evaluate countermeasures before deployment.

Building lightweight security for low-cost pervasive devices is a major challenge considering the design requirements of a small footprint and low power consumption. Physical Unclonable Functions (PUFs) have emerged as a promising technology to provide a low-cost authentication for such devices. By exploiting intrinsic manufacturing process variations, PUFs are able to generate unique and apparently random chip identifiers. Strong-PUFs represent a variant of PUFs that have been suggested for lightweight authentication applications. Unfortunately, many of the Strong-PUFs have been shown to be susceptible to modelling attacks (i.e., using machine learning techniques) in which an adversary has access to challenge and response pairs. In this study, we propose an obfuscation technique during post-processing of Strong-PUF responses to increase the resilience against machine learning attacks. We conduct machine learning experiments using Support Vector Machines and Artificial Neural Networks on two Strong-PUFs: a 32-bit Arbiter-PUF and a 2-XOR 32-bit Arbiter-PUF. The predictability of the 32-bit Arbiter-PUF is reduced to $\approx$ 70% by using an obfuscation technique. Combining the obfuscation technique with 2-XOR 32-bit Arbiter-PUF helps to reduce the predictability to $\approx$ 64%. More reduction in predictability has been observed in an XOR Arbiter-PUF because this PUF architecture has a good uniformity. The area overhead with an obfuscation technique consumes only 788 and 1080 gate equivalents for the 32-bit Arbiter-PUF and 2-XOR 32-bit Arbiter-PUF, respectively.

Many modern defenses rely on address space layout randomization (ASLR) to efficiently hide security-sensitive metadata in the address space. Absent implementation flaws, an attacker can only bypass such defenses by repeatedly probing the address space for mapped (security-sensitive) regions, incurring a noisy application crash on any wrong guess. Recent work shows that modern applications contain idioms that allow the construction of crash-resistant code primitives, allowing an attacker to efficiently probe the address space without causing any visible crash. In this paper, we classify different crash-resistant primitives and show that this problem is much more prominent than previously assumed. More specifically, we show that rather than relying on labor-intensive source code inspection to find a few "hidden" application-specific primitives, an attacker can find such primitives semi-automatically, on many classes of real-world programs, at the binary level. To support our claims, we develop methods to locate such primitives in real-world binaries. We successfully identified 29 new potential primitives and constructed proof-of-concept exploits for four of them.

Implementation attacks and more specifically Power Analysis (PA) (the dominant type of side channel attack) and fault injection (FA) attacks constitute a pragmatic hazard for scalar multiplication, the main operation behind Elliptic Curve Cryptography. There exists a wide variety of countermeasures attempting to thwart such attacks that, however, few of them explore the potential of alternative number systems like the Residue Number System (RNS). In this paper, we explore the potential of RNS as an PA-FA countermeasure and propose an PA-FA resistant scalar multiplication algorithm and provide an extensive security analysis against the most effective PA-FA techniques. We argue through a security analysis that combining traditional PA-FA countermeasures with lightweight RNS countermeasures can provide strong PA-FA resistance.

The damage caused by counterfeits of semiconductors has become a serious problem. Recently, a physical unclonable function (PUF) has attracted attention as a technique to prevent counterfeiting. The present study investigates an arbiter PUF, which is a typical PUF. The vulnerability of a PUF against machine-learning attacks has been revealed. It has also been indicated that the output of a PUF is inverted from its normal output owing to the difference in environmental variations, such as the changes in power supply voltage and temperature. The resistance of a PUF against machine-learning attacks due to the difference in environmental variation has seldom been evaluated. The present study evaluated the resistance of an arbiter PUF against machine-learning attacks due to the difference in environmental variation. By performing an evaluation experiment using a simulation, the present study revealed that the resistance of an arbiter PUF against machine-learning attacks due to environmental variation was slightly improved. However, the present study also successfully predicted more than 95% of the outputs by increasing the number of learning cycles. Therefore, an arbiter PUF was revealed to be vulnerable to machine-learning attacks even after environmental variation.

Modern smart surveillance systems can not only record the monitored environment but also identify the targeted objects and detect anomaly activities. These advanced functions are often facilitated by deep neural networks, achieving very high accuracy and large data processing throughput. However, inappropriate design of the neural network may expose such smart systems to the risks of leaking the target being searched or even the adopted learning model itself to attackers. In this talk, we will present the security challenges in the design of smart surveillance systems. We will also discuss some possible solutions that leverage the unique properties of emerging nano-devices, including the incurred design and performance cost and optimization methods for minimizing these overheads.

There are more and more systems using mobile devices to perform sensing tasks, but these increase the risk of leakage of personal privacy and data. Data hiding is one of the important ways for information security. Even though many data hiding algorithms have worked on providing more hiding capacity or higher PSNR, there are few algorithms that can control PSNR effectively while ensuring hiding capacity. In this paper, with controllable PSNR based on LSBs substitution- PSNR-Controllable Data Hiding (PCDH), we first propose a novel encoding plan for data hiding. In PCDH, we use the remainder algorithm to calculate the hidden information, and hide the secret information in the last x LSBs of every pixel. Theoretical proof shows that this method can control the variation of stego image from cover image, and control PSNR by adjusting parameters in the remainder calculation. Then, we design the encoding and decoding algorithms with low computation complexity. Experimental results show that PCDH can control the PSNR in a given range while ensuring high hiding capacity. In addition, it can resist well some steganalysis. Compared to other algorithms, PCDH achieves better tradeoff among PSNR, hiding capacity, and computation complexity.

The precise measurement of temperature is very important to the security and stability of the operation for a superconducting magnet. A slight fluctuation in the operating temperature may cause a superconducting magnet unstable. This paper presents a low-temperature measurement system based on C8051 Micro Controller Unit and Platinum resistance thermometer. In the process of data acquisition, a modified weighted average algorithm is applied to the digital filter program of the micro controller unit. The noise can be effectively reduced and can measure temperature of three different location points simultaneously, and there is no the interference among the three channels. The designed system could measure the temperature from 400 K to 4.0 K with a resolution of 1 mK. This system will be applied in a conduction cooling Nb3Al superconducting magnet. In order to certify the feasibility of the system, tests are performed in a small NbTi non-insulation superconducting magnet model. The results show that the measurement system is reliable and the measured temperature is accurate.

Memristors are an attractive option for use in future memory architectures due to their non-volatility, high density and low power operation. Notwithstanding these advantages, memristors and memristor-based memories are prone to high defect densities due to the non-deterministic nature of nanoscale fabrication. The typical approach to fault detection and diagnosis in memories entails testing one memory cell at a time. This is time consuming and does not scale for the dense, memristor-based memories. In this paper, we integrate solutions for detecting and locating faults in memristors, and ensure post-silicon recovery from memristor failures. We propose a hybrid diagnosis scheme that exploits sneak-paths inherent in crossbar memories, and uses March testing to test and diagnose multiple memory cells simultaneously, thereby reducing test time. We also provide a repair mechanism that prevents faults in the memory from being activated. The proposed schemes enable and leverage sneak paths during fault detection and diagnosis modes, while still maintaining a sneak-path free crossbar during normal operation. The proposed hybrid scheme reduces fault detection and diagnosis time by ~44%, compared to traditional March tests, and repairs the faulty cell with minimal overhead.
 

Hash functions, such as SHA (secure hash algorithm) and MD (message digest) families that are built upon Merkle-Damgard construction, suffer many attacks due to the iterative nature of block-by-block message processing. Chum and Zhang [4] proposed a new hash function construction that takes advantage of the randomize-then-combine technique, which was used in the incremental hash functions, to the iterative hash function. In this paper, we implement such hash construction in three ways distinguished by their corresponding padding methods. We conduct the experiment in parallel multi-threaded programming settings. The results show that the speed of proposed hash function is no worse than SHA1.
 

The Philips audio fingerprint[1] has been used for years, but its robustness against external noise has not been studied accurately. This paper shows the Philips fingerprint is noise resistant, and is capable of recognizing music that is corrupted by noise at a -4 to -7 dB signal to noise ratio. In addition, the drawbacks of the Philips fingerprint are addressed by utilizing a “Power Mask” in conjunction with the Philips fingerprint during the matching process. This Power Mask is a weight matrix given to the fingerprint bits, which allows mismatched bits to be penalized according to their relevance in the fingerprint. The effectiveness of the proposed fingerprint was evaluated by experiments using a database of 1030 songs and 1184 query files that were heavily corrupted by two types of noise at varying levels. Our experiments show the proposed method has significantly improved the noise resistance of the standard Philips fingerprint.

IT industry loses tens of billions of dollars annually from security attacks such as tampering and malicious reverse engineering. Code obfuscation techniques counter such attacks by transforming code into patterns that resist the attacks. None of the current code obfuscation techniques satisfy all the obfuscation effectiveness criteria such as resistance to reverse engineering attacks and state space increase. To address this, we introduce new code patterns that we call nontrivial code clones and propose a new obfuscation scheme that combines nontrivial clones with existing obfuscation techniques to satisfy all the effectiveness criteria. The nontrivial code clones need to be constructed manually, thus adding to the development cost. This cost can be limited by cloning only the code fragments that need protection and by reusing the clones across projects. This makes it worthwhile considering the security risks. In this paper, we present our scheme and illustrate it with a toy example.

Large number of digital images and videos are acquired, stored, processed and shared nowadays. High quality imaging hardware and low cost, user friendly image editing software make digital mediums vulnerable to modifications. One of the most popular image modification techniques is copy move forgery. This tampering technique copies part of an image and pastes it into another part on the same image to conceal or to replicate some part of the image. Researchers proposed many techniques to detect copy move forged regions of images recently. These methods divide image into overlapping blocks and extract features to determine similarity among group of blocks. Selection of the feature extraction algorithm plays an important role on the accuracy of detection methods. Column averages of 1D-FT of rows is used to extract features from overlapping blocks on the image. Blocks are transformed into frequency domain using 1D-FT of the rows and average values of the transformed columns form feature vectors. Similarity of feature vectors indicates possible forged regions. Results show that the proposed method can detect copy pasted regions with higher accuracy compared to similar works reported in the literature. The method is also more resistant against the Gaussian blurring or JPEG compression attacks as shown in the results.

This paper applies a con-resistant trust mechanism to improve the performance of a communications-based special protection system to enhance its effectiveness and resiliency. Smart grids incorporate modern information technologies to increase reliability and efficiency through better situational awareness. However, with the benefits of this new technology come the added risks associated with threats and vulnerabilities to the technology and to the critical infrastructure it supports. The research in this paper uses con-resistant trust to quickly identify malicious or malfunctioning (untrustworthy) protection system nodes to mitigate instabilities. The con-resistant trust mechanism allows protection system nodes to make trust assessments based on the node's cooperative and defective behaviors. These behaviors are observed via frequency readings which are prediodically reported. The trust architecture is tested in experiments by comparing a simulated special protection system with a con-resistant trust mechanism to one without the mechanism via an analysis of the variance statistical model. Simulation results show promise for the proposed con-resistant trust mechanism.

Anonymous communications networks, such as Tor, help to solve the real and important problem of enabling users to communicate privately over the Internet. However, in doing so, anonymous communications networks introduce an entirely new problem for the service providers - such as websites, IRC networks or mail servers - with which these users interact, in particular, since all anonymous users look alike, there is no way for the service providers to hold individual misbehaving anonymous users accountable for their actions. Recent research efforts have focused on using anonymous blacklisting systems (which are sometimes called anonymous revocation systems) to empower service providers with the ability to revoke access from abusive anonymous users. In contrast to revocable anonymity systems, which enable some trusted third party to deanonymize users, anonymous blacklisting systems provide users with a way to authenticate anonymously with a service provider, while enabling the service provider to revoke access from any users that misbehave, without revealing their identities. In this paper, we introduce the anonymous blacklisting problem and survey the literature on anonymous blacklisting systems, comparing and contrasting the architecture of various existing schemes, and discussing the tradeoffs inherent with each design. The literature on anonymous blacklisting systems lacks a unified set of definitions, each scheme operates under different trust assumptions and provides different security and privacy guarantees. Therefore, before we discuss the existing approaches in detail, we first propose a formal definition for anonymous blacklisting systems, and a set of security and privacy properties that these systems should possess. We also outline a set of new performance requirements that anonymous blacklisting systems should satisfy to maximize their potential for real-world adoption, and give formal definitions for several optional features already supported by some sche- - mes in the literature.