A Modular End-to-End Framework for Secure Firmware Updates on Embedded Systems

Firmware refers to device read-only resident code which includes microcode and macro-instruction-level routines. For Internet-of-Things (IoT) devices without an operating system, firmware includes all the necessary instructions on how such embedded systems operate and communicate. Thus, firmware updates are essential parts of device functionality. They provide the ability to patch vulnerabilities, address operational issues, and improve device reliability and performance during the lifetime of the system. This process, however, is often exploited by attackers in order to inject malicious firmware code into the embedded device. In this article, we present a framework for secure firmware updates on embedded systems. This approach is based on hardware primitives and cryptographic modules, and it can be deployed in environments where communication channels might be insecure. The implementation of the framework is flexible, as it can be adapted in regards to the IoT device’s available hardware resources and constraints. Our security analysis shows that our framework is resilient to a variety of attack vectors. The experimental setup demonstrates the feasibility of the approach. By implementing a variety of test cases on FPGA, we demonstrate the adaptability and performance of the framework. Experiments indicate that the update procedure for a 1183-kB firmware image could be achieved, in a secure manner, under 1.73 seconds.

Enhancing Search-based Testing with Testability Transformations for Existing APIs

Search-based software testing (SBST) has been shown to be an effective technique to generate test cases automatically. Its effectiveness strongly depends on the guidance of the fitness function. Unfortunately, a common issue in SBST is the so-called flag problem , where the fitness landscape presents a plateau that provides no guidance to the search. In this article, we provide a series of novel testability transformations aimed at providing guidance in the context of commonly used API calls (e.g., strings that need to be converted into valid date/time objects). We also provide specific transformations aimed at helping the testing of REST Web Services. We implemented our novel techniques as an extension to EvoMaster , an SBST tool that generates system-level test cases. Experiments on nine open-source REST web services, as well as an industrial web service, show that our novel techniques improve performance significantly.

Short term prediction of atmospheric temperature using neural networks

In this paper, a neural network based forecasting model for the maximum and the minimum temperature for the ground level is proposed. A backpropagation method of gradient-decent learning in multi-layer perceptron (MLP) type of neural network with only one hidden layer is considered. This network consists of 25 input nodes and two output nodes. The network is trained with a varying number of nodes in the hidden layer using a set of training sample and each of them is tested with a set of test sample. It accepts previous two consecutive days information (such as pressures, temperatures, relative humidities, etc.) as inputs for the estimation of the maximum and the minimum temperature as output. The network with 20 or less neurons in the hidden layer is found to be "optimum" and it produces an error within ±2° C for 80% of test cases.

CALIBRATION OF A CFD METHODOLOGY FOR THE SIMULATION OF ROUGHNESS EFFECTS ON FRICTION AND HEAT TRANSFER IN ADDITIVE MANUFACTURED COMPONENTS

It is well-known from the literature that surface roughness significantly affects friction and heat transfer. This is even more evident for additive manufactured (AM) components, which are taking an increasingly important role in the gas turbine field. However, the exploitation of numerical approaches to improve their design is hindered by the lack of dedicated correlations and CFD models developed for such high roughness conditions. Usually the additive manufactured components are simulated considering the surfaces as smooth or applying an equivalent sand-grain roughness (ks) that results in a velocity shift in the boundary layer. However, determining a priori the most appropriate value of ks is challenging, as dozens of correlations are available, returning scattered and uncertain results. A previous work proved how the CFD prediction of friction and heat transfer returns significant deviations, even exploiting the ks values obtained from experimental tests on the very same test case. That work also allowed identification of a promising CFD methodology based on friction and thermal corrections proposed by Aupoix from ONERA. The aim of this work is to further the assessment and calibration activity of the model, by analyzing additional experimental data of friction factor and Nusselt number from new test cases considering different geometries and flow conditions. The new coupons consisted of straight circular channels and wavy channels. This work represents a further step in the generation of a more validated and general methodology for the high-fidelity CFD analysis of additive-manufactured components.

Parameter Identification of Photovoltaic Models by Hybrid Adaptive JAYA Algorithm

As one of the most promising forms of renewable energy, solar energy is increasingly deployed. The simulation and control of photovoltaic (PV) systems requires identification of their parameters. A Hybrid Adaptive algorithm based on JAYA and Differential Evolution (HAJAYADE) is developed to identify these parameters accurately and reliably. The HAJAYADE algorithm consists of adaptive JAYA, adaptive DE, and the chaotic perturbation method. Two adaptive coefficients are introduced in adaptive JAYA to balance the local and global search. In adaptive DE, the Rank/Best/1 mutation operator is put forward to boost the exploration and maintain the exploitation. The chaotic perturbation method is applied to reinforce the local search further. The HAJAYADE algorithm is employed to address the parameter identification of PV systems through five test cases, and the eight latest meta-heuristic algorithms are its opponents. The mean RMSE values of the HAJAYADE algorithm from five test cases are 9.8602 × 10−4, 9.8294 × 10−4, 2.4251 × 10−3, 1.7298 × 10−3, and 1.6601 × 10−2. Consequently, HAJAYADE is proven to be an efficient and reliable algorithm and could be an alternative algorithm to identify the parameters of PV systems.

Q-BOR–FDTD method for solving Schrodinger equation for rotationally symmetric nanostructures with hydrogenic impurity

An efficient method inspired by the traditional body of revolution finite-difference time-domain (BOR-FDTD) method is developed to solve the Schrodinger equation for rotationally symmetric problems. As test cases, spherical, cylindrical, cone-like quantum dots, harmonic oscillator, and spherical quantum dot with hydrogenic impurity are investigated to check the efficiency of the proposed method which we coin as Quantum BOR-FDTD (Q-BOR-FDTD) method. The obtained results are analysed and compared to the 3-D FDTD method, and the analytical solutions. Q-BOR-FDTD method proves to be very accurate and time and memory efficient by reducing a three-dimensional problem to a two-dimensional one, therefore one can employ very fine meshes to get very precise results. Moreover, it can be exploited to solve problems including hydrogenic impurities which is not an easy task in the traditional FDTD calculation due to singularity problem. To demonstrate its accuracy, we consider spherical and cone-like core-shell QD with hydrogenic impurity. Comparison with analytical solutions confirms that Q-BOR–FDTD method is very efficient and accurate for solving Schrodinger equation for problems with hydrogenic impurity

Building Ant System for Multi-Faceted Test Case Prioritization

This article presents the empirical study of multi-criteria test case prioritization. In this article, a test case prioritization problem with time constraints is being solved by using the ant colony optimization (ACO) approach. The ACO is a meta-heuristic and nature-inspired approach that has been applied for the statement of a coverage-based test case prioritization problem. The proposed approach ranks test cases using statement coverage as a fitness criteria and the execution time as a constraint. The proposed approach is implemented in MatLab and validated on widely used benchmark dataset, freely available on the Software Infrastructure Repository (SIR). The results of experimental study show that the proposed ACO based approach provides near optimal solution to test case prioritization problem.

Проблемы обнаружения отрыва потока датчиками давления на беспилотных летательных аппаратах с пропеллером

An experimental study of pulsations characteristics of the zone of flow separation arising at a small airplane-type UAV with a pushing two-blade propeller were carried out. The measurements were done in wind tunnel by unsteady pressure sensors and microphones built into the skin of the UAV for the test cases with and without a rotating propeller. A significant effect of the propeller on the level of pulsations was found. An increase of the incoming flow velocity led to a weakening of this effect. Analysis of the spectral data of the disturbances did not reveal a direct relationship between the propeller noise and the unsteady characteristics of the separation zone.

A Literature Review on Automation Testing Using Selenium+Sikuli

Automation testing is a methodology that uses an application to implement the entire life cycle of the software in less time and provides efficiency and effectiveness to the testing software. In automation testing, the tester writes scripts and uses any suitable application software to test the software application. Automation is basically an automated process that is comprised of lots of manual activities. In other words, automation testing uses automation tools like Selenium, Sikuli, Appium, etc., to write test script and execute test cases, with no or minimal manual involvement required while executing an automated test suite. Usually, automation testers write test scripts and test cases using any of the automation tool and then groups test several cases. Here, we will discuss a neat case study explaining the automation testing using a hybrid test script.

Metaheuristic Techniques for Test Case Generation

The primary objective of software testing is to locate bugs as many as possible in software by using an optimum set of test cases. Optimum set of test cases are obtained by selection procedure which can be viewed as an optimization problem. So metaheuristic optimizing (searching) techniques have been immensely used to automate software testing task. The application of metaheuristic searching techniques in software testing is termed as Search Based Testing. Non-redundant, reliable and optimized test cases can be generated by the search based testing with less effort and time. This article presents a systematic review on several meta heuristic techniques like Genetic Algorithms, Particle Swarm optimization, Ant Colony Optimization, Bee Colony optimization, Cuckoo Searches, Tabu Searches and some modified version of these algorithms used for test case generation. The authors also provide one framework, showing the advantages, limitations and future scope or gap of these research works which will help in further research on these works.