Revisiting Location Privacy from a Side-Channel Analysis Viewpoint

Side-Channel Analysis (SCA) plays a crucial role in hardware security evaluation. However, side-channel acquisitions (a.k.a. traces) usually contain noises that often impose negative effects on key-recovery efficiency. In this paper, we propose convolutional denoising autoencoder (CDAE) for noise reduction in SCA. CDAE is composed of multiple layers of convolution operators, learning an end-to-end mapping from noisy traces to clean traces by minimizing the \(\ell _2\) loss of noisy-clean trace pairs. The convolutional layers capture the abstraction of the traces while eliminating noises. We argue that CDAE is very suitable for profiled SCA especially when the attacker has a large amount of traces in the offline profiling phase. Once the network training is done, our denoising network can be applied to individual new noisy traces for the attacker to launch online attacks. To validate the effectiveness of our method, we train CDAE to denoise traces and then perform Template Attacks (TA) in three high noise jamming scenarios, including unprotected (GPU and FPGA based) and protected (MCU based) AES implementations. Our method can significantly outperform the state-of-the-art Singular Spectrum Analysis (SSA) denoising method on both information theoretic metrics and security metrics. Results show that CDAE achieves at least \(\sim 4\times \) Signal-to-Noise Ratio (SNR) gain, thus TA with denoising preprocessing requires at most 50% of the traces in the attack phase.

Latest nanometer CMOS technology nodes have highlighted new issues in security of cryptographic hardware implementations. The constant growth of the static power consumption has led to a new class of side-channel attacks. Common attacks exploiting static power use an univariate approach to recover information from cryptographic engines. In our work, a multivariate approach based on information theoretic security metrics is presented. The temperature-dependence helps to exploit more information leakage from the hardware implementation. Starting from a univariate analysis, mutual information reveals that increasing the working temperature, the information leaked through the static power side channel is increased as well. In this work a multivariate analysis exploiting static power consumption is presented in which the temperature-domain is used to extract more information. The use of information theoretic approach allows to precisely quantify the amount of information that can be leaked from a cryptographic hardware implementation. The perceived information shows taking advantage of the use of more than one temperature, the security level can be decreased. The improvement achieved using the presented approach is demonstrated on a 40 nm CMOS implementation of the Present 80 crypto core.

In order to better understand the information security performance in products, processes, technical systems or organizations as a whole, and to plan, control, and improve it, security engineers, system developers and business managers must be able to get early feedback information from the achieved security situation. Systematic security metrics provides the means for managing security-related measurements comprehensively. We reflect on the use of information security metrics by presenting the results of an interview study carried out in Finnish industrial companies and State institutions. Furthermore, we discuss the application of security measurements from the business process and technical points of view. The role of technical security metrics is analyzed using mobile ad hoc networks as a case example.

Finding relevant metrics in information security is an important but difficult problem. In this paper, we propose to empirically investigate the relevance of different security metrics that could be derived from intrusion prevention system (IPS) alert events and computer security incident data. Based on the data provided by the University of Maryland, we show that IPS metrics are linked to security incidents, and also that different types of security incidents have different significant metrics. These results can be used for identifying possible candidates for security incident indicators, developing methods to improve incident prevention and helping organizations interpret their IPS's better in the future.

This research paper presentation will feature current frameworks to addressing risk and security modeling and metrics. The paper will analyze technical level risk and security metrics of Common Criteria/ISO15408, Centre for Internet Security guidelines, NSA configuration guidelines and metrics used at this level. Information IT operational standards view on security metrics such as GMITS/ISO13335, ITIL/ITMS and architectural guidelines such as ISO7498-2 will be explained. Business process level standards such as ISO17799, COSO and CobiT will be presented with their control approach to security metrics. Top level, the maturity standards such as SSE-CMM/ISO21827, NSA Infosec Assessment and CobiT will be explored and reviewed. For each defined level of security metrics the research presentation will explore the appropriate usage of these standards. The paper will discuss standards approaches to conducting the risk and security metrics. The research findings will demonstrate the need for common baseline for both risk and security metrics. This paper will show the relation between the attribute based common baseline and corporate assets and controls for risk and security metrics. IT will be shown that such approach spans over all mentioned standards. The proposed approach 3D visual presentation and development of the Information Security Model will be analyzed and postulated. Presentation will clearly demonstrate the benefits of proposed attributes based approach and defined risk and security space for modeling and measuring.

This chapter introduces the Metrics Based Security Assessment (MBSA) as a means of measuring an organization’s information security maturity. It argues that the historical (i.e., first through third generations) approaches used to assess/ensure system security are not effective and thereby combines the strengths of two industry proven information security models, the ISO 17799 Standard and the Systems Security Engineering Capability Maturity Model (SSE-CMM), to overcome their inherent weaknesses. Furthermore, the authors trust that the use of information security metrics will enable information security practitioners to measure their information security efforts in a more consistent, reliable, and timely manner. Such a solution will allow a more reliable qualitative measurement of the return achieved through given information security investments. Ultimately, the MBSA will allow professionals an additional, more robust self-assessment tool in answering management questions similar to: “How secure are we?”

In this paper, we consider a recently introduced framework that investigates physically observable implementations from a theoretical point of view. The model allows quantifying the effect of practically relevant leakage functions with a combination of security and information theoretic metrics. More specifically, we apply our evaluation methodology to an exemplary block cipher. We first consider a Hamming weight leakage function and evaluate the efficiency of two commonly investigated countermeasures, namely noise addition and masking. Then, we show that the proposed methodology allows capturing certain non-trivial intuitions, e.g. about the respective effectiveness of these countermeasures. Finally, we justify the need of combined metrics for the evaluation, comparison and understanding of side-channel attacks.

Researchers are exploring the use of convolutional neural networks (CNNs) in side-channel attacks to understand the weaknesses in cryptographic implementation. CNNs can learn hierarchical characteristics automatically from electromagnetic radiation or power usage during cryptographic processes. Researchers train CNNs on side-channel data to extract meaningful representations and deduce secret keys. Deep learning algorithms are helpful in evaluating the security of embedded systems, and CNNs are a feasible paradigm for profiling side-channel analysis attacks. In this paper, it has been introduced a VGG (Visual Geometry Group)-Net architecture, which is a typical deep convolutional neural network design with numerous layers. It uses the ASCAD dataset to conduct experiments. They found that VGG-Net architecture Side Channel Attacks (SCA) provides better results than the previously optimized CNN model by significantly reducing the number of side-channel traces required for successful attacks on desynchronized datasets. The researchers also discovered that synchronous traces serve as the pre-training source for VGG-Net architecture, functioning successfully in terms of jittering with minimal fine-adjusting after training

Measuring the security of business processes of enterprises has become essential in the wake of different threat scenarios. During the last two decades, a lot of research has been done on metrics for the network security, software system security, attack severity, situation assessment, etc. In the process level, business impact analysis models and security maturity models, as well as well-established risk analysis methods, exist. With the all-pervasive IT implementation of business processes, it has become imperative for the chief information security officers to come up with metrics for the security of business processes in the context of the relevant threat scenario. This paper introduces a novel security metric to assess the business process security, viz., Security Concern. The metric quantitatively measures the “concern” due to various attributes of the security of a business process in the context of the threat scenario and asset sensitivity. We present the model of the business process, its assets, their dependencies, exploits, and impacts on assets in a top–down fashion. Based on the model, security concern as a metric has been defined. The bottom–up method of the computation of the metric is explained. It is also demonstrated how the proposed metric can be of practical utility for the horizontal and temporal comparison of the business process security.

In this paper the effect of high-performance techniques for high speed applications in secure cryptographic implementations is studied. The use of dual pre charge logic styles with fine-grained pipelining with an overlapping three-phase clock scheme is studied, also including a correct distribution of the clock signal in the cryptographic implementation. To make this study, four different implementations of the Sbox-9 of the Kasumi algorithm have been implemented using an 90nm TSMC technology. Simulation-based DPA attacks have been carried out, showing how the proper synchronization of data signals gives better results in terms of power consumption and operating frequency, but affects negatively the security against side channel attacks, decreasing the number of input patterns needed to disclosure the secret key.

Together with masking, shuffling is one of the most frequently considered solutions to improve the security of small embedded devices against side-channel attacks. In this paper, we provide a comprehensive study of this countermeasure, including improved implementations and a careful information theoretic and security analysis of its different variants. Our analyses lead to important conclusions as they moderate the strong security improvements claimed in previous works. They suggest that simplified versions of shuffling (e.g. using random start indexes) can be significantly weaker than their counterpart using full permutations. We further show with an experimental case study that such simplified versions can be as easy to attack as unprotected implementations. We finally exhibit the existence of “indirect leakages” in shuffled implementations that can be exploited due to the different leakage models of the different resources used in cryptographic implementations. This suggests the design of fully shuffled (and efficient) implementations, were both the execution order of the instructions and the physical resources used are randomized, as an interesting scope for further research.

Understanding how the brain extracts the behavioral meaning carried by specific vocalization types that can be emitted by various vocalizers and in different conditions is a central question in auditory research. This semantic categorization is a fundamental process required for acoustic communication, and presupposes discriminative and invariance properties of the auditory system for conspecific vocalizations. Songbirds have been used extensively to study vocal learning, but the communicative function of all their vocalizations and their neural representation has yet to be examined. In this study, we first generated a library containing almost the entire zebra finch vocal repertoire, and organised communication calls along nine different categories according to their behavioral meaning. We then investigated the neural representations of these semantic categories in the primary and secondary auditory areas of six anesthetised zebra finches. To analyse how single units encode these call categories, we described neural responses in terms of their discrimination, selectivity and invariance properties. Quantitative measures for these neural properties were obtained with an optimal decoder using both spike counts and spike patterns. Information theoretic metrics show that almost half of the single units encode semantic information. Neurons achieve higher discrimination of these semantic categories by being more selective and more invariant. These results demonstrate that computations necessary for semantic categorization of meaningful vocalizations are already present in the auditory cortex, and emphasise the value of a neuro-ethological approach to understand vocal communication.

Navigation in mobile robotics involves two tasks, keeping track of the robot's position and moving according to a control strategy. In addition, when no prior knowledge of the environment is available, the problem is even more difficult, as the robot has to build a map of its surroundings as it moves. These three problems ought to be solved in conjunction since they depend on each other. <br/>This thesis is about simultaneously controlling an autonomous vehicle, estimating its location and building the map of the environment. The main objective is to analyse the problem from a control theoretical perspective based on the EKF-SLAM implementation. <br/>The contribution of this thesis is the analysis of system's properties such as observability, controllability and stability, which allow us to propose an appropriate navigation scheme that produces well-behaved estimators, controllers, and consequently, the system as a whole. <br/>We present a steady state analysis of the SLAM problem, identifying the conditions that lead to partial observability. It is shown that the effects of partial observability appear even in the ideal linear Gaussian case. This indicates that linearisation alone is not the only cause of SLAM inconsistency, and that observability must be achieved as a prerequisite to tackling the effects of linearisation. Additionally, full observability is also shown to be necessary during diagonalisation of the covariance matrix, an approach often used to reduce the computational complexity of the SLAM algorithm, and which leads to full controllability as we show in this work.<br/>Focusing specifically on the case of a system with a single monocular camera, we present an observability analysis using the nullspace basis of the stripped observability matrix. The aim is to get a better understanding of the well known intuitive behaviour of this type of systems, such as the need for triangulation to features from different positions in order to get accurate relative pose estimates between vehicle and camera. Through characterisation the unobservable directions in monocular SLAM, we are able to identify the vehicle motions required to maximise the number of observable states in the system. <br/>When closing the control loop of the SLAM system, both the feedback controller and the estimator are shown to be asymptotically stable. Furthermore, we show that the tracking error does not influence the estimation performance of a fully observable system and viceversa, that control is not affected by the estimation. Because of this, a higher level motion strategy is required in order to enhance estimation, specially needed while performing SLAM with a single camera. Considering a real-time application, we propose a control strategy to optimise both the localisation of the vehicle and the feature map by computing the most appropriate control actions or movements. The actions are chosen in order to maximise an information theoretic metric. Simulations and real-time experiments are performed to demonstrate the feasibility of the proposed control strategy.

A grammatical gender system divides a lexicon into a small number of relatively fixed grammatical categories. How similar are these gender systems across languages? To quantify the similarity, we define gender systems extensionally, thereby reducing the problem of comparisons between languages’ gender systems to cluster evaluation. We borrow a rich inventory of statistical tools for cluster evaluation from the field of community detection (Driver and Kroeber, 1932; Cattell, 1945), that enable us to craft novel information theoretic metrics for measuring similarity between gender systems. We first validate our metrics, then use them to measure gender system similarity in 20 languages. We then ask whether our gender system similarities alone are sufficient to reconstruct historical relationships between languages. Towards this end, we make phylogenetic predictions on the popular, but thorny, problem from historical linguistics of inducing a phylogenetic tree over extant Indo-European languages. Of particular interest, languages on the same branch of our phylogenetic tree are notably similar, whereas languages from separate branches are no more similar than chance.

Joint entropy analysis of anterior-posterior and medial-lateral body sway.