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.

     

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.

     

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.

     

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.

     

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.

     

Solving time-invariant differential matrix Riccati equations using GPGPU computing

Differential matrix Riccati equations (DMREs) enable to model many physical systems appearing in different branches of science, in some cases, involving very large problem sizes. In this paper, we propose an adaptive algorithm for time-invariant DMREs that uses a piecewise-linearized approach based on the Padé approximation of the matrix exponential. The algorithm designed is based upon intensive use of matrix products and linear system solutions so we can seize the large computational capability that modern graphics processing units (GPUs) have on these types of operations using CUBLAS and CULATOOLS libraries (general purpose GPU), which are efficient implementations of BLAS and LAPACK libraries, respectively, for NVIDIA \(\copyright \) GPUs. A thorough analysis showed that some parts of the algorithm proposed can be carried out in parallel, thus allowing to leverage the two GPUs available in many current compute nodes. Besides, our algorithm can be used by any interested researcher through a friendly MATLAB \(\copyright \) interface.     

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.

     

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.

     

SaBRe: load-time selective binary rewriting

Binary rewriting consists in disassembling a program to modify its instructions. However, existing solutions suffer from shortcomings in terms of soundness and performance. We present SaBRe, a load-time system for selective binary rewriting. SaBRe rewrites specific constructs—particularly system calls and functions—when the program is loaded into memory, and intercepts them using plugins through a simple API. We also discuss the theoretical underpinnings of disassembling and rewriting. We developed two backends—for x86_64 and RISC-V—which were used to implement three plugins: a fast system call tracer, a multi-version executor, and a fault injector. Our evaluation shows that SaBRe imposes little overhead, typically below 3%.

     

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.

     

Performance modeling and optimization of sparse matrix-vector multiplication on NVIDIA CUDA platform

In this article, we discuss the performance modeling and optimization of Sparse Matrix-Vector Multiplication ( ) on NVIDIA GPUs using CUDA. has a very low computation-data ratio and its performance is mainly bound by the memory bandwidth. We propose optimization of based on ELLPACK from two aspects: (1) enhanced performance for the dense vector by reducing cache misses, and (2) reduce accessed matrix data by index reduction. With matrix bandwidth reduction techniques, both cache usage enhancement and index compression can be enabled. For GPU with better cache support, we propose differentiated memory access scheme to avoid contamination of caches by matrix data. Performance evaluation shows that the combined speedups of proposed optimizations for GT-200 are 16% (single-precision) and 12.6% (double-precision) for GT-200 GPU, and 19% (single-precision) and 15% (double-precision) for GF-100 GPU.     

Numerical simulations based on shifted second-order difference/finite element algorithms for the time fractional Maxwell’s system

In this article, mixed element algorithms with second-order time convergence results for the two-dimensional time fractional Maxwell’s equations in the Cole–Cole dispersive medium are developed. Fully discrete mixed element systems with shifted parameters \(\theta\) at time \(t=t_{n-\theta }\), which are constructed by combining the generalized BDF2-\(\theta\) schemes in temporal direction and a mixed element method in space direction, are formulated. For the two-dimensional case of the fractional Maxwell’s system, the algorithm implementation process based on the rectangular subdivision is shown in detail. Finally, two numerical examples are provided to confirm the validity of our method and to analyze the influence of parameters \(\alpha\), \(\theta\) for numerical solutions.

     

SMT solving for the validation of B and Event-B models

ProB provides a constraint solver for the B-method written in Prolog and can make use of different backends based on SAT and SMT solving. One such backend translates B and Event-B operators to SMT-LIB using the Z3 solver. This translation uses quantifiers to axiomatize some operators, which are not well-handled by Z3. Several relational constraints such as the transitive closure are not supported by this translation. In this article, we substantially improve the translation to SMT-LIB by employing a more constructive rather than axiomatized style using Z3’s lambda function. Thereby, we are able both to translate more B and Event-B operators to SMT-LIB and improve the overall performance. We further extend ProB’s interface to Z3 to run different solver configurations in parallel. In addition, we present a direct implementation of SMT solving in Prolog using ProB’s constraint solver as a theory solver. We hereby aim to combine the strengths of conflict-driven clause learning for identifying contradictions with ProB’s constraint solver for finding solutions. We deem this implementation to be worthwhile since ProB’s constraint solver is tailored toward solving B and Event-B constraints, and we herewith avoid the dependency on an external SMT solver. Empirical results show that the new integration of Z3 has improved performance of constraint solving and enables to solve several constraints which cannot be solved by ProB’s constraint solver. Furthermore, the direct implementation of SMT solving in ProB shows benefits compared to ProB’s constraint solver and the integration of Z3.

     

MAPAS: a practical deep learning-based android malware detection system

A lot of malicious applications appears every day, threatening numerous users. Therefore, a surge of studies have been conducted to protect users from newly emerging malware by using machine learning algorithms. Albeit existing machine or deep learning-based Android malware detection approaches achieve high accuracy by using a combination of multiple features, it is not possible to employ them on our mobile devices due to the high cost for using them. In this paper, we propose MAPAS, a malware detection system, that achieves high accuracy and adaptable usages of computing resources. MAPAS analyzes behaviors of malicious applications based on API call graphs of them by using convolution neural networks (CNN). However, MAPAS does not use a classifier model generated by CNN, it only utilizes CNN for discovering common features of API call graphs of malware. For efficiently detecting malware, MAPAS employs a lightweight classifier that calculates a similarity between API call graphs used for malicious activities and API call graphs of applications that are going to be classified. To demonstrate the effectiveness and efficiency of MAPAS, we implement a prototype and thoroughly evaluate it. And, we compare MAPAS with a state-of-the-art Android malware detection approach, MaMaDroid. Our evaluation results demonstrate that MAPAS can classify applications 145.8% faster and uses memory around ten times lower than MaMaDroid. Also, MAPAS achieves higher accuracy (91.27%) than MaMaDroid (84.99%) for detecting unknown malware. In addition, MAPAS can generally detect any type of malware with high accuracy.

     

CPA/Tiger-MGP: test-goal set partitioning for efficient multi-goal test-suite generation

Software model checkers can be used to generate high-quality test cases from counterexamples of a reachability analysis. However, naïvely invoking a software model checker for each test goal in isolation does not scale to large programs as a repeated construction of an abstract program model is expensive. In contrast, invoking a software model checker for reaching all test goals in a single run leads to few abstraction possibilities and thus to low scalability. Therefore, our approach pursues a test-suite generation technique that incorporates configurable multi-goal set partitioning (MGP) including configurable partitioning strategies and simultaneous processing of multiple test goals in one reachability analysis. Our approach employs recent techniques from multi-property verification in order to control the computational overhead for tracking multi-goal reachability information. Our tool, called CPA/Tiger-MGP, uses predicate-abstraction-based program analysis in the model-checking framework CPA checker.

     

A tuning approach for iterative multiple 3d stencil pipeline on GPUs: Anisotropic Nonlinear Diffusion algorithm as case study

This paper focuses on challenging applications that can be expressed as an iterative pipeline of multiple 3d stencil stages and explores their optimization space on GPUs. For this study, we selected a representative example from the field of digital signal processing, the Anisotropic Nonlinear Diffusion algorithm. An open issue to these applications is to determine the optimal fission/fusion level of the involved stages and whether that combination benefits from data tiling. This implies exploring a large space of all the possible fission/fusion combinations with and without tiling, thus making the process non-trivial. This study provides insights to reduce the optimization tuning space and programming effort of iterative multiple 3d stencils. Our results demonstrate that all combinations that fuse the bottleneck stencil with high halos update cost (\(>25\%\), this percentage can be measured or estimated experimentally for each single stencil) and high registers and shared memory accesses must not be considered in the exploration process. The optimal fission/fusion combination is up to 1.65\(\times \) faster than the case in which we fully decompose our stencil without tiling and 5.3\(\times \) faster with respect to the fully fused version on the NVIDIA GPUs.     

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.