Weaknesses of some lightweight blockciphers suitable for IoT systems and their applications in hash modes

Twelve PGV models, MDC-2, and HIROSE, which are blockcipher-based hash functions, have been proven to be secure as hash functions when they are instantiated with ideal blockciphers. However, their security cannot be guaranteed when the base blockciphers use weak key-schedules. In this paper, we propose various related-key or chosen-key differential paths of Fantomas, Midori-128, GOST, and 12-round reduced AES-256 using key-schedules with weak diffusion effects. We then describe how these differential paths undermine the security of PGV models, MDC-2, or HIROSE. In addition, we show that the invariant subspace attacks on PRINT and Midori-64 can be transferred to collision attacks on their some hash modes.

     

Multi-label feature ranking with ensemble methods

In this paper, we propose three ensemble-based feature ranking scores for multi-label classification (MLC), which is a generalisation of multi-class classification where the classes are not mutually exclusive. Each of the scores (Symbolic, Genie3 and Random forest) can be computed from three different ensembles of predictive clustering trees: Bagging, Random forest and Extra trees. We extensively evaluate the proposed scores on 24 benchmark MLC problems, using 15 standard MLC evaluation measures. We determine the ranking quality saturation points in terms of the ensemble sizes, for each ranking-ensemble pair, and show that quality rankings can be computed really efficiently (typically 10 or 50 trees suffice). We also show that the proposed feature rankings are relevant and determine the most appropriate ensemble method for every feature ranking score. We empirically prove that the proposed feature ranking scores outperform current state-of-the-art methods in the quality of the rankings (for the majority of the evaluation measures), and in time efficiency. Finally, we determine the best performing feature ranking scores. Taking into account the quality of the rankings first and—in the case of ties—time efficiency, we identify the Genie3 feature ranking score as the optimal one.

     

Cryptanalysis of hash functions based on blockciphers suitable for IoT service platform security

It is well-known that blockcipher-based hash functions may be attacked when adopting blockciphers having related-key differential properties. However, all forms of related-key differentials are not always effective to attack them. In this paper we provide the general frameworks for collision and second-preimage attacks on hash functions by using related-key differential properties of instantiated blockciphers, and show their various applications. In the literature, there have been several provably secure blockcipher-based hash functions such as 12 PGV schemes, MDC-2, MJH, Abreast-DM, Tandem-DM, and HIROSE. However, their security cannot be guaranteed when they are instantiated with specific blockciphers. In this paper, we first observe related-key differential properties of some blockciphers such as Even-Mansour (EM), Single-key Even-Mansour (SEM), XPX with a fixed tweak (XPX₁₁₁₁), Chaskey cipher, and LOKI, which are suitable for IoT service platform security. We then present how these properties undermine the security of the aforementioned blockcipher-based hash functions. In our analysis, the collision and second-preimage attacks can be applied to several PGV schemes, MDC-2, MJH instantiated with SEM, XPX₁₁₁₁, Chaskey cipher, to PGV no.5, MJH, HIROSE, Abreast-DM, Tandem-DM instantiated with EM. Furthermore, LOKI-based MDC-2 is vulnerable to the collision attack. We also provide the necessary conditions for related-key differentials of blockciphers in order to attack each of the hash functions. To the best of our knowledge, this study is the first comprehensive analysis of hash functions based on blockciphers having related-key differential properties. Our cryptanalytic results support the well-known claim that blockcipher-based hash functions should avoid adopting blockciphers with related-key differential properties, such as the fixed point property in compression functions. We believe that this study provides a better understanding of the security of blockcipher-based hash functions.

     

A mathematical framework for design discovery from multi-threaded applications using neural sequence solvers

Comprehending existing multi-threaded applications effectively is a challenge without proper assistance. Research has been proposed to mine programs to extract aspects of high-level design but not much to reverse-engineer the concurrent design from multi-threaded applications. To address the same, we develop a generic mathematical model to interpret run-time non-deterministic events and encode functional as well as thread-specific behaviour in form of quantifiable features, which can be fitted into a standard solver for automated inference of design aspects from multi-threaded applications. We build a tool Dcube based on the mathematical model and use various classifiers of a machine learning framework to infer design aspects related to concurrency and resource management. We collect a dataset of 480 projects from Github, CodeProject and Stack Overflow and 3 benchmark suites—CDAC Pthreads, Open POSIX Test Suites and PARSEC 3.0 and achieve an accuracy score of around 93.71% for all the design choices.

     

MISCELA: discovering simultaneous and time-delayed correlated attribute patterns

This article addresses a new pattern mining problem in time series sensor data, which we call correlated attribute pattern mining. The correlated attribute patterns (CAPs for short) are the sets of attributes (e.g., temperature and traffic volume) on sensors that are spatially close to each other and temporally correlated in their measurements. Although the CAPs are useful to accurately analyze and understand spatio-temporal correlation between attributes, the existing mining methods are inefficient to discover CAPs because they extract unnecessary patterns. Therefore, we propose a mining method Miscela to efficiently discover CAPs. Miscela can discover not only simultaneous correlated patterns but also time delayed correlated patterns. Furthermore, we extend Miscela to automatically search for correlated patterns with any time delays. Through our experiments using three real sensor datasets, we show that the response time of Miscela is up to 20.84 times faster compared with the state-of-the-art method. We show that Miscela discovers meaningful patterns for urban managements and environmental studies.

     

Interpretable tabular data generation

Generative adversarial network (GAN) models have been successfully utilized in a wide range of machine learning applications, and tabular data generation domain is not an exception. Notably, some state-of-the-art models of tabular data generation, such as CTGAN,  TableGan, MedGAN, etc. are based on GAN models. Even though these models have resulted in superior performance in generating artificial data when trained on a range of datasets, there is a lot of room (and desire) for improvement. Not to mention that existing methods do have some weaknesses other than performance. For example, the current methods focus only on the performance of the model, and limited emphasis is given on the interpretation of the model. Secondly, the current models operate on raw features only, and hence they fail to exploit any prior knowledge on explicit feature interactions that can be utilized during data generation process. To alleviate the two above-mentioned limitations, in this work, we propose a novel tabular data generation model—Generative Adversarial Network modelling inspired from Naive Bayes and Logistic Regression’s relationship (\({ { \texttt {GANBLR} } }\)), which not only address the interpretation limitation of existing tabular GAN-based models but provides capability to handle explicit feature interactions as well. Through extensive evaluations on wide range of datasets, we demonstrate \({ { \texttt {GANBLR} } }\)’s superior performance as well as better interpretable capability (explanation of feature importance in the synthetic generation process) as compared to existing state-of-the-art tabular data generation models.

     

Computing matrix trigonometric functions with GPUs through Matlab

This paper presents an implementation of one of the most up-to-day algorithms proposed to compute the matrix trigonometric functions sine and cosine. The method used is based on Taylor series approximations which intensively uses matrix multiplications. To accelerate matrix products, our application can use from one to four NVIDIA GPUs by using the NVIDIA cublas and cublasXt libraries. The application, implemented in C++, can be used from the Matlab command line thanks to the mex files provided. We experimentally assess our implementation in modern and very high-performance NVIDIA GPUs.

     

CoVeriTest: interleaving value and predicate analysis for test-case generation

Verification techniques are well-suited for automatic test-case generation. They basically need to check the reachability of every test goal and generate test cases for all reachable goals. This is also the basic idea of our CoVeriTest submission. However, the set of test goals is not fixed in CoVeriTest , instead we can configure the set of test goals. For Test-Comp’19, we support the set of all __VERIFIER_error() calls as well as the set of all branches. Thus, we can deal with the two test specifications considered in Test-Comp’19. Since the tasks in Test-Comp are diverse and verification techniques have different strengths and weaknesses, we also do not stick to a single verification technique, but use a hybrid approach that combines multiple techniques. More concrete, CoVeriTest interleaves different verification techniques and allows to configure the cooperation (i.e., information exchange and time limits). To choose from a large set of verification techniques, CoVeriTest is integrated into the analysis framework CPAchecker. For the competition, we interleave CPAchecker’s value and predicate analysis and let both analyses resume their analysis performed in the previous iteration.

     

Debug-localize-repair: a symbiotic construction for heap manipulations

We present Wolverine2, an integrated Debug-Localize-Repair environment for heap manipulating programs. Wolverine2 provides an interactive debugging environment: while concretely executing a program via on an interactive shell supporting common debugging facilities, Wolverine2 displays the abstract program states (as box-and-arrow diagrams) as a visual aid to the programmer, packages a novel, proof-directed repair algorithm to quickly synthesize the repair patches and a new bug localization algorithm to reduce the search space of repairs. Wolverine2 supports “hot-patching” of the generated patches to provide a seamless debugging environment, and also facilitates new debug-localize-repair possibilities: specification refinement and checkpoint-based hopping. We evaluate Wolverine2 on 6400 buggy programs (generated using automated fault injection) on a variety of data-structures like singly, doubly, and circular linked lists, AVL trees, Red-Black trees, Splay Trees and Binary Search Trees; Wolverine2 could repair all the buggy instances within realistic programmer wait-time (less than 5 s in most cases). Wolverine2 could also repair more than 80% of the 247 (buggy) student submissions where a reasonable attempt was made.

     

Parallel algorithms for parameter-free structural diversity search on graphs

Structural diversity of a user in a social network is the number of social contexts in his/her contact neighborhood. The problem of structural diversity search is to find the top-k vertices with the largest structural diversity in a graph. However, when identifying distinct social contexts, existing structural diversity models (e.g., t-sized component, t-core, and t-brace) are sensitive to an input parameter of t. To address this drawback, we propose a parameter-free structural diversity model. Specifically, we propose a novel notation of discriminative core, which automatically models various kinds of social contexts without parameter t. Leveraging on discriminative cores and h-index, the structural diversity score for a vertex is calculated. We study the problem of parameter-free structural diversity search in this paper. An efficient top-k search algorithm with a well-designed upper bound for pruning is proposed. To further speed up the computation, we design a novel parallel algorithm for efficient top-k search over large graphs. The parallel algorithm computes diversity scores for a batch of vertices simultaneously using multi-threads. Extensive experiment results demonstrate the parameter sensitivity of existing t-core based model and verify the superiority of our methods.

     

Bu-Dash: a universal and dynamic graphical password scheme (extended version)

Passwordless authentication is a trending theme in cyber security, while biometrics gradually replace knowledge-based schemes. However, Personal Identification Numbers, passcodes, and graphical passwords are still considered as the primary means for authentication. Passwords must be memorable to be usable; therefore, users tend to choose easy to guess secrets, compromising security. The Android Pattern Unlock is a popular graphical password scheme that can be easily attacked by exploiting human behavioristic traits. Despite its vulnerabilities, the popularity of the scheme has led researchers to propose adjustments and variations that enhance security but maintain its familiar user interface. Nevertheless, prior work demonstrated that improving security while preserving usability remains frequently a hard task. In this paper we propose a novel graphical password scheme built on the foundations of the well-accepted Android Pattern Unlock method, which is usable, inclusive, universal, and robust against shoulder surfing and (basically) smudge attacks. Our scheme, named Bu-Dash, features a dynamic user interface that mutates every time a user swipes the screen. Our pilot studies illustrate that Bu-Dash attracts positive user acceptance rates, it is secure, and maintains high usability levels. We define complexity metrics that can be used to further diversify user input, and we conduct complexity and security assessments.

     

Bounds on Threshold Probabilities for Coloring Properties of Random Hypergraphs

We study the threshold probability for the property of existence of a special-form \(r\)?-?coloring for a random \(k\)?-?uniform hypergraph in the \(H(n,k,p)\) binomial model. A parametric set of \(j\)?-?chromatic numbers of a random hypergraph is considered. A coloring of hypergraph vertices is said to be \(j\)?-?proper if every edge in it contains no more than \(j\) vertices of each color. We analyze the question of finding the sharp threshold probability of existence of a \(j\)?-?proper \(r\)?-?coloring for \(H(n,k,p)\). Using the second moment method, we obtain rather tight bounds for this probability provided that \(k\) and \(j\) are large as compared to \(r\).

     

Numerical solution of a phase field model for polycrystallization processes in binary mixtures

We consider the numerical solution of a phase field model for polycrystallization in the solidification of binary mixtures in a domain \( \varOmega \subset \mathbb {R}^2\). The model is based on a free energy in terms of three order parameters: the local orientation \(\varTheta \) of the crystals, the local crystallinity \(\phi \), and the concentration c of one of the components of the binary mixture. The equations of motion are given by an initial-boundary value problem for a coupled system of partial differential equations consisting of a regularized second order total variation flow in \( \varTheta \), an \(L^2\) gradient flow in \(\phi \), and a \(W^{1,2}(\varOmega )^*\) gradient flow in c. Based on an implicit discretization in time by the backward Euler scheme, we suggest a splitting method such that the three semidiscretized equations can be solved separately and prove existence of a solution. As far as the discretization in space is concerned, the fourth order Cahn–Hilliard type equation in c is taken care of by a \(\hbox {C}^0\) Interior Penalty Discontinuous Galerkin approximation which has the advantage that the same finite element space can be used as well for the spatial discretization of the equations in \( \varTheta \) and \( \phi \). The fully discretized equations represent parameter dependent nonlinear algebraic systems with the discrete time as a parameter. They are solved by a predictor corrector continuation strategy featuring an adaptive choice of the time-step. Numerical results illustrate the performance of the suggested numerical method.

     

Portfolio management system in equity market neutral using reinforcement learning

Portfolio management involves position sizing and resource allocation. Traditional and generic portfolio strategies require forecasting of future stock prices as model inputs, which is not a trivial task since those values are difficult to obtain in the real-world applications. To overcome the above limitations and provide a better solution for portfolio management, we developed a Portfolio Management System (PMS) using reinforcement learning with two neural networks (CNN and RNN). A novel reward function involving Sharpe ratios is also proposed to evaluate the performance of the developed systems. Experimental results indicate that the PMS with the Sharpe ratio reward function exhibits outstanding performance, increasing return by 39.0% and decreasing drawdown by 13.7% on average compared to the reward function of trading return. In addition, the proposed PMS_CNN model is more suitable for the construction of a reinforcement learning portfolio, but has 1.98 times more drawdown risk than the PMS_RNN. Among the conducted datasets, the PMS outperforms the benchmark strategies in TW50 and traditional stocks, but is inferior to a benchmark strategy in the financial dataset. The PMS is profitable, effective, and offers lower investment risk among almost all datasets. The novel reward function involving the Sharpe ratio enhances performance, and well supports resource-allocation for empirical stock trading.

     

Plain random test generation with PRTest

Automatic test-suite generation tools are often complex and their behavior is not predictable. To provide a minimum baseline that test-suite generators should be able to surpass, we present PRTest, a random black-box test-suite generator for C programs: To create a test, PRTest natively executes the program under test and creates a new, random test value whenever an input value is required. After execution, PRTest checks whether any new program branches were covered and, if this is the case, the created test is added to the test suite. This way, tests are rapidly created either until a crash is found, or until the user aborts the creation. While this naive mechanism is not competitive with more sophisticated, state-of-the-art test-suite generation tools, it is able to provide a good baseline for Test-Comp and a fast alternative for automatic test-suite generation for programs with simple control flow. PRTest is publicly available and open source.

     

The training and practice of crew resource management: recommendations from an inductive in vivo study of the flight deck

Abstract

Crew resource management (CRM) is credited with saving 185 lives at Sioux City. While the theory behind CRM is well documented, there are few studies of how CRM manifests on the line. This inductive in vivo study had three objectives. First, to describe how CRM manifests. Secondly, to evaluate the efficacy of CRM vis-à-vis flight safety. Thirdly, to suggest improvements to the CRM training syllabus. The study produced five conclusions: First, CRM is durable under conditions of moderate strain. Secondly, crews embed and refine CRM through reflection and action. Thirdly, CRM facilitates and shapes social relations. Fourthly, mindlessness (Langer 1989 Langer, E. J. 1989. Minding matters: The consequences of mindlessness-mindfulness. In Advances in Experimental Social Psychology, edited by L. Berkowitz, New York, NY: Academic Press.[Crossref] , [Google Scholar]) undermines CRM. Finally, the interruption of flight-deck routines by third-parties poses a threat to flight-safety. The paper recommends multi-profession CRM training as a means of improving communication and co-ordination in and around aircraft. The study’s limitations include a monocultural flight-deck: flights were operated by pilots with European backgrounds. Mindful of Hofstede’s (1980 Hofstede, G. 1980. Culture’s Consequences. Beverley Hills, CA: Sage. [Google Scholar]), Engle's (2000 Engle, M. 2000. “Culture in the Cockpit — CRM in a Multicultural World.” Journal of Air Transportation World Wide 5 (1): 107–114. [Google Scholar]) and Helmreich and Merritt's (2001 Helmreich, R. L., and A. Merritt. 2001. Culture at Work in Aviation and Medicine. Aldershot, England: Ashgate. [Google Scholar]) examination of the relationship between culture and performance, the author suggests the study be repeated with carriers that employ pilots from a variety of cultures.

Practitioner Summary: This in vivo study evaluates the efficacy of CRM vis-à-vis flight safety and supports a critique of the CRM syllabus. The author observed twenty sectors and attended a CRM training day. To improve safety and efficiency, it is recommended that airlines run multi-profession (inclusive) CRM training courses.     

Explanations for over-constrained problems using QuickXPlain with speculative executions

Conflict detection is used in various scenarios ranging from interactive decision making (e.g., knowledge-based configuration) to the diagnosis of potentially faulty models (e.g., using knowledge base analysis operations). Conflicts can be regarded as sets of restrictions (constraints) causing an inconsistency. Junker’s QuickXPlain is a divide-and-conquer based algorithm for the detection of preferred minimal conflicts. In this article, we present a novel approach to the detection of such conflicts which is based on speculative programming. We introduce a parallelization of QuickXPlain and empirically evaluate this approach on the basis of synthesized knowledge bases representing feature models. The results of this evaluation show significant performance improvements in the parallelized QuickXPlain version.