Input Validation Research Articles

FuzzFactory: domain-specific fuzzing with waypoints

Coverage-guided fuzz testing has gained prominence as a highly effective method of finding security vulnerabilities such as buffer overflows in programs that parse binary data. Recently, researchers have introduced various specializations to the coverage-guided fuzzing algorithm for different domain-specific testing goals, such as finding performance bottlenecks, generating valid inputs, handling magic-byte comparisons, etc. Each such solution can require non-trivial implementation effort and produces a distinct variant of a fuzzing tool. We observe that many of these domain-specific solutions follow a common solution pattern. In this paper, we present FuzzFactory, a framework for developing domain-specific fuzzing applications without requiring changes to mutation and search heuristics. FuzzFactory allows users to specify the collection of dynamic domain-specific feedback during test execution, as well as how such feedback should be aggregated. FuzzFactory uses this information to selectively save intermediate inputs, called waypoints, to augment coverage-guided fuzzing. Such waypoints always make progress towards domain-specific multi-dimensional objectives. We instantiate six domain-specific fuzzing applications using FuzzFactory: three re-implementations of prior work and three novel solutions, and evaluate their effectiveness on benchmarks from Google's fuzzer test suite. We also show how multiple domains can be composed to perform better than the sum of their parts. For example, we combine domain-specific feedback about strict equality comparisons and dynamic memory allocations, to enable the automatic generation of LZ4 bombs and PNG bombs.

An empirical study of security warnings from static application security testing tools

The Open Web Application Security Project (OWASP) defines Static Application Security Testing (SAST) tools as those that can help find security vulnerabilities in the source code or compiled code of software. Such tools detect and classify the vulnerability warnings into one of many types (e.g., input validation and representation). It is well known that these tools produce high numbers of false positive warnings. However, what is not known is if specific types of warnings have a higher predisposition to be false positives or not. Therefore, our goal is to investigate the different types of SAST-produced warnings and their evolution over time to determine if one type of warning is more likely to have false positives than others. To achieve our goal, we carry out a large empirical study where we examine 116 large and popular C++ projects using six different state-of-the-art open source and commercial SAST tools that detect security vulnerabilities. In order to track a piece of code that has been tagged with a warning, we use a new state of the art framework called cregit+ that traces source code lines across different commits. The results demonstrate the potential of using SAST tools as an assessment tool to measure the quality of a product and the possible risks without manually reviewing the warnings. In addition, this work shows that pattern-matching static analysis technique is a very powerful method when combined with other advanced analysis methods.

Evolution from ancient medication to human‐centered Healthcare 4.0: A review on health care recommender systems

SummaryThe evolution of intelligent and data‐driven systems has pushed for the tectonic transition from ancient medication to human‐centric Healthcare 4.0. The rise of Internet of Things, Internet of Systems, and wireless body area networks has endowed the health care ecosystem with a new digital transformation supported by sophisticated machine learning and artificial intelligence algorithms. Under this umbrella, health care recommendation systems have emerged as a driver for providing patient‐centric personalized health care services. Recommendation systems are automatic systems that derive the decisions on the basis of some valid input parameters and vital health information collected through wearable devices, implantable equipments, and various sensor. Therefore, to understand the state‐of‐the‐art developments in the health care ecosystem, this paper provides a comprehensive survey on health care recommendation systems and the associated paradigms. This survey starts from the ancient health care era and move toward the Healthcare 4.0 in a phased manner. The road map from Healthcare 1.0 to Healthcare 4.0 is analyzed to highlight different technology verticals supporting the digital transformation. This study also provides the systematic review of the health care systems, the types of health care systems, and the recommender systems. Moreover, a deep analysis of health care recommender systems and its types is also presented. Finally, the open issues and challenges associated with the adaption and implementation of human‐centric Healthcare 4.0 ecosystem are discussed. This is provided to find out the possible research questions and gaps so that the corresponding solutions could be developed in the near future.

DeepMoney: counterfeit money detection using generative adversarial networks.

Conventional paper currency and modern electronic currency are two important modes of transactions. In several parts of the world, conventional methodology has clear precedence over its electronic counterpart. However, the identification of forged currency paper notes is now becoming an increasingly crucial problem because of the new and improved tactics employed by counterfeiters. In this paper, a machine assisted system—dubbed DeepMoney—is proposed which has been developed to discriminate fake notes from genuine ones. For this purpose, state-of-the-art models of machine learning called Generative Adversarial Networks (GANs) are employed. GANs use unsupervised learning to train a model that can then be used to perform supervised predictions. This flexibility provides the best of both worlds by allowing unlabelled data to be trained on whilst still making concrete predictions. This technique was applied to Pakistani banknotes. State-of-the-art image processing and feature recognition techniques were used to design the overall approach of a valid input. Augmented samples of images were used in the experiments which show that a high-precision machine can be developed to recognize genuine paper money. An accuracy of 80% has been achieved. The code is available as an open source to allow others to reproduce and build upon the efforts already made.

SEPTIC: Detecting Injection Attacks and Vulnerabilities Inside the DBMS

Databases continue to be the most commonly used backend storage in enterprises, but they are often integrated with vulnerable applications, such as web frontends, which allow injection attacks to be performed. The effectiveness of such attacks stems from a semantic mismatch between how SQL queries are believed to be executed and the actual way in which databases process them. This leads to subtle vulnerabilities in the way input validation is done in applications. In this paper, we propose SEPTIC, a mechanism for DBMS attack prevention, which can also assist on the identification of the vulnerabilities in the applications. The mechanism was implemented in MySQL and evaluated experimentally with various applications and alternative protection approaches. Our results show no false negatives and no false positives with SEPTIC, on the contrary to other solutions. They also show that SEPTIC introduces a low performance overhead, in the order of 2.2%.

Analysis of sheet cavitation with bubble/bubble interaction models

A new cavitation model based on bubble/bubble interactions has been recently proposed by the authors Adama Maiga, Coutier-Delgosha, and Buisine [“A new Cavitation model based on bubble-bubble interactions,” Phys. Fluids 30, 123301 (2018)]. It includes all nonlinear interaction terms between two bubbles, and the only input is the local velocity divergence, while the classical Rayleigh-Plesset or Gilmore models only take into account the linear interaction terms and require the local pressure evolution. In this previous publication, the model has been validated against cases of single bubbles growths and collapses. In the present paper, the same model is applied to configurations of high-speed cavitating flow on a Venturi profile and a two-dimensional foil section, where experiments based on X-ray imaging have been recently conducted. Based on the flow velocity fields available in the two cases, the local velocity divergence is calculated, and a one-dimensional mean divergence evolution from the leading edge to the wake of the cavity is extracted and used as an input for the model validation. The evolutions of the biggest bubble and the volume of vapor are both found in good agreement with the experiments. A typical velocity divergence evolution is then defined, and our model is compared to the modified Rayleigh-Plesset equation that includes the interactions between multiple bubbles. It confirms that the radius evolution of the biggest bubble is systematically better estimated by the new model due to its capability to continuously take into account the interactions of big bubbles with smaller bubbles. Eventually, the model is used to investigate the scale effect, by applying 6 different time scales of cavitation development covering more than 5 orders of magnitude. It is shown that the bubble size does not vary proportionally to the scale, but according to a law that is empirically determined.

Batch and data streaming classification models for detecting adverse events and understanding the influencing factors

Constructing effective models for detecting, reducing, and/or preventing adverse events is very important in domains such as aviation safety, healthcare, drug administration, and war theaters. This study presents batch and data streaming models to detecting adverse events using data from a war theater context. In all the previous studies, regression models and several machine learning techniques were used for predicting continuous values in an active theater of war, and the error values reported on the test sets were large. In order to overcome the shortcoming, this study investigates the effectiveness of batch and data streaming classification algorithms in detecting or classifying adverse events given infrastructure development spending data and other variables in an active theater of war in Afghanistan. By the feature selection, the valid input variables are obtained and their indexes show that the input variables are mainly the adverse events (t-1) at the previous month, the population densities and related project investments. From the country level, fewer of the 14 project investments affect the adverse events. From the region level, some projects with higher index values, such as Security in the South Western region, Energy and Emergency Assistance in the North Eastern region, and Education in the Eastern region are mainly affecting factors. Three batch classification methods and three data streaming classification methods were assessed for their ability to detect adverse events given infrastructure development data. The study uses cost-sensitive measures to address the very unbalanced nature of the data and it applies variable reduction techniques to identify significant variables. The three batch classification algorithms are C4.5, k-nearest Neighbor, and Support Vector Machine. The three data streaming algorithms are Naïve Bayes, Hoeffding Tree, and Single Classifier Drift. In general, the performance of the cost-sensitive methods in the batch setting is comparable to those in the data stream setting. However, in the batch setting the cost matrix needs to be adjusted manually. In contrast the data stream setting allows one to adjust the models based on the analysis of the classifiers’ performance over time and changing data distribution. The Kappa values using Naïve Bayes are the highest in the three data stream algorithms in the whole country and its regions. The Naïve Bayes classifier has the best global performance. By the Kappa statistic curve, we can observe the concept drifts. In a region level, many models have a better performance including more investments related to project compared with those in a country level. In addition as data distribution becomes more balanced, the classifiers in the data stream setting outperform in terms of the overall classification rates in comparison to the classifiers in the batch setting. The results thus demonstrate the potential of data streaming algorithms to significantly outperform when the data become less unbalanced, and can be used for detecting adverse events in similar areas.

Multi-step ahead prediction of vapor compression air conditioning system behaviour using neural networks

Cooling capacity and super heat temperature control for air conditioning (AC) system operation is necessary to ensure that the system operates efficiently. In this paper, multi-step-ahead prediction of AC system behaviour is presented using backpropagation neural network model as the first effort to develop the effective control strategy. Several step-ahead cooling capacity and superheat temperature performance are predicted under modulation of compressor speed and expansion valve opening. The prediction is proposed to capture the dynamic behaviour of system that can be applied in predictive control purpose. The configuration of ANN model is developed based on nonlinear autoregressive network with exogenous input (NARX) structure. Input and output data for training and validation of ANN model are generated by AC simulator. The ANN model is optimized by investigating the effect of number of neuron and time delay input on prediction accuracy. The results show that the ANN model developed in present study has good accuracy in predicting several step-ahead of cooling capacity and superheat temperature. Accordingly, this ANN model is applicable for predictive control in future study.

Kinodynamic planning for spherical tensegrity locomotion with effective gait primitives

Tensegrity-based robots can achieve locomotion through shape deformation and compliance. They are highly adaptable to their surroundings, and are lightweight, low cost, and physically robust. Their high dimensionality and strongly dynamic nature, however, can complicate motion planning. Efforts to date have primarily considered quasi-static reconfiguration and short-term dynamic motion of tensegrity robots, which do not fully exploit the underlying system dynamics in the long term. Longer-horizon planning has previously required costly search over the full space of valid control inputs. This work synthesizes new and existing approaches to produce dynamic long-term motion while balancing the computational demand. A numerical process based upon quasi-static assumptions is first applied to deform the system into an unstable configuration, causing forward motion. The dynamical characteristics of the result are then altered via a few simple parameters to produce a small but diverse set of useful behaviors. The proposed approach takes advantage of identified symmetries on the prototypical spherical tensegrity robot, which reduce the number of needed gaits but allow motion along different directions. These gaits are first combined with a standard search method to achieve long-term planning in environments where the developed gaits are effective. For more complex environments, the various motion primitives are paired with the fall-back option of random valid actions and are used by an informed sampling-based kinodynamic motion planner with anytime properties. Evaluations using a physics-based model for the prototypical robot demonstrate that modest but efficiently applied search effort can unlock the utility of dynamic tensegrity motion to produce high-quality solutions.

A survey of cancer patients’ rehabilitation therapy needs in Southwest China.

e18003 Background: Medical advance in the recent decades has turned cancer into a chronic disease. At the same time, quality of life (LoF) during the treatment period has emerged as a new concern of cancer patients. Rehabilitation therapy, which can help patients conquer fear and be more engaged in the process, has gradually attracted more attentions in China. We designed a survey, for both the cancer patients and their families, to understand their view of the rehabilitation therapy. Methods: We sent paper-based surveys to volunteers consisted of cancer patients and their families. Majority of patients are from 10 cancer centers at the southwest region of China. The main cancer types are lung cancer, breast cancer and colon cancer. The survey questions focus on: patients and their families’ engagement of the treatment, their view on rehabilitation therapy, and their method preference for the rehabilitation therapy. Results: We sent 2,000 copies of the survey and received 1,920 valid inputs. Below are the key findings. 1. Their biggest concern: the outcome of the treatment 55.8%, side effects 19.7%, financial burden 12.8% and mental stress 11.5%. 2. The importance of the rehabilitation therapy as compared to traditional medical treatment, such as surgery and chemotherapy: equally important 67.8%, less important 17.7%, more important 10.1%, no preference 4.4%. 3. The necessity of rehabilitation therapy: very necessary 65.7%, somewhat necessary 22.7%, not necessary 11.6%. 4. The first three reasons for participating rehabilitation therapy: it helped go through the treatment 42.8%, it improved quality of life 31.6% and it helped release their mental stress 22.3%. 5. The best form to conduct rehabilitation therapy: face-to-face communicate with the doctors 60.6%, telecommunication 15.8% and combination of both 22.1%. Conclusions: The results of the survey strongly indicate the demands for rehabilitation therapy. Patients started to understand it – more than half of them think it is as important as the traditional medical treatment. Patients’ quality of life and mental health, which is part of the holistic treatment, are gradually recognized. Next, we plan to group our survey results by types of cancers, patients and their families’ genders and age, as well as the local economics, to better understand the data. We hope this study can serve as a data point to help build China’s rehabilitation therapy infrastructure.

Challenges to the use of fertilisers derived from human excreta: The case of vegetable exports from Kenya to Europe and influence of certification systems

Land degradation and inadequate faecal sludge management are two major issues in Sub-Saharan Africa (SSA). The transformation of human excreta into soil amendments and their wide-scale adoption could improve soil health and contribute to solving the sanitation crisis in SSA. There are however perception challenges around these fertilisers because of the potentially harmful components they contain such as pathogens and heavy metals, which can be removed with appropriate treatment such as composting. A major barrier to the wide scale commercialisation of human excreta derived fertiliser (HEDF) is the unclear regulations surrounding their use. The aim of this study was to identify barriers to the use of HEDF by farmers participating in the horticultural export market with Kenya as focus area since horticultural exports are a major contributor to the country’s economy. Global GAP is the most widely adopted standard for quality assurance of horticultural crops and the use of human sewage sludge is currently not allowed on certified farms. Interviews with stakeholders along the food export chain highlighted the complex interactions that exist between them and showed that Global GAP certified farmers were not willing to use HEDF on their farms even if local regulations recognise treated sludge as a valid input to agriculture. Several countries (like the UK, Sweden, Australia and the USA) created specific certification or assurance schemes to improve public perception of biosolids. The creation of a similar assurance or certification scheme specific to fertilisers made from source-separated human excreta would be a step into formalising them as a product, establishing production procedures, limits on contaminants content as well as testing protocols. Such a certification scheme could increase the confidence of regulating bodies in HEDF and lead to their acceptance by global farming standards.

Security Testing Tool for NoSQL Systems

NoSQL systems are becoming more popular due to their inherent advantages and solutions it provides to the limits of a relational database. However, despite its benefits, it comes with security challenges. In this paper, an input validation mechanism architecture is proposed for Mongo DB to detect and prevent NoSQL injection attacks, the mechanism employs a Deterministic Finite Automaton (DFA) approach to detect and prevent attacks on NoSQL systems. Furthermore, a security comparison of some NoSQL systems is provided based on recent literature. The security features compared are authentication, authorization, data encryption and input validation. The proposed mechanism will improve the security of Mongo DB system because invalid inputs requests will be detected and prevented from being processed.

An algorithm for detecting SQL injection vulnerability using black-box testing

SQL Injection Attack (SQLIA) is one of the most severe attack that can be used against web database-driven applications. Attackers use SQLIA to obtain unauthorized access and perform unauthorized data modifications due to initial improper input validation by the web application developer. Various studies have shown that, on average, 64% of web applications worldwide are vulnerable to SQLIA due to improper input. To mitigate the devastating problem of SQLIA, this research proposes an automatic black box testing for SQL Injection Vulnerability (SQLIV). This acts to automate an SQLIV assessment in SQLIA. In addition, recent studies have shown that there is a need for improving the effectiveness of existing SQLIVS in order to reduce the cost of manual inspection of vulnerabilities and the risk of being attacked due to inaccurate false negative and false positive results. This research focuses on improving the effectiveness of SQLIVS by proposing an object-oriented approach in its development in order to help and minimize the incidence of false positive and false negative results, as well as to provide room for improving a proposed scanner by potential researchers. To test and validate the accuracy of research work, three vulnerable web applications were developed. Each possesses a different type of vulnerabilities and an experimental evaluation was used to validate the proposed scanner. In addition, an analytical evaluation is used to compare the proposed scanner with the existing academic scanners. The result of the experimental analysis shows significant improvement by achieving high accuracy compared to existing studies. Similarly, the analytical evaluations showed that the proposed scanner is capable of analyzing attacked page response using four different techniques.

Comparative evaluation of intelligent algorithms to improve adaptive neuro-fuzzy inference system performance in precipitation modelling

Existent algorithms to train adaptive neuro-fuzzy inference system (ANFIS) such as gradient descent and least-square methods are useful, but they have problems such as trapping in local optimum as well as high-volume of computations. This paper examines the application of genetic algorithm (GA), ant colony optimization for the continuous domain (ACOR), particle swarm optimization (PSO) and differential evolution (DE) for improving the performance of ANFIS models in simulating monthly rainfall magnitudes. Monthly rainfall records of nine foothills to arid weather stations of Isfahan province, central Iran were used for evaluating the proposed methodology. The correlation coefficient and non-linear sensitivity analysis were performed on the available rainfall records. Monthly rainfall modelling was carried out based on three input groups and used separately in five applied models (e.g. ANFIS, ANFIS-PSO, ANFIS-ACOR, ANFIS-DE, and ANFIS-GA). The obtained results showed that proposed hybrid models had better accuracy than the simple ANFIS model in escaping local optima. The best model was ANFIS-ACOR (with R2 = 0.92, RMSE = 2.73 mm and SI = 0.26), while sole ANFIS model (with R2 = 0.33, RMSE = 9.98 mm and SI = 0.90) presented the worst output. Moreover, the obtained results showed that: (1) Coupling the optimization algorithms with ANFIS can improve its performance in modeling the monthly rainfall time series; (2) the correlation coefficient and sensitivity analysis of input data provided a valid input selection that could improve the model accuracy with neglecting the redundant input parameters.

Adaptive frequency adjustment technique in current mode DC–DC converters for input voltage range extension

This study presents a switching frequency adjustment technique that helps widen the input voltage range of a current mode DC-DC converter. Conventional current-mode switching converters suffer the minimum on/off time restrictions that limit the input-to-output conversion ratio. This work introduces an additional regulation loop on top of an existing converter that regulates the switching period when the minimum on/off time limit is approached. Compared with a converter with fixed switching frequency, the proposed approach allows the free-running switching to be set without being limited by the minimum on/off time. A boost topology with output voltage of 24 V was built based on the proposed approach, and the measured valid input voltage range was extended from 8-16 to 3-21 V by the proposed scheme under 1 MHz preset frequency. Since the slope-compensation current, the inductor size, and the output capacitor are all optimised at 1 MHz and is not limited by the minimum on/off time which may lower the switching frequency of a conventional converter down to 450 kHz, the measured maximum available output current and the typical output voltage ripple under 12 V input achieved 1.57 A and 57 mV, respectively.

Stochastic inversion by trace matching for carbonate reservoir property prediction: A Mishrif Reservoir case study

Estimation of reservoir properties and facies from seismic data is a well-established technique, and there are numerous methods in common usage. Our 1D stochastic inversion process (ODiSI), based on matching large numbers of pseudowells to color-inverted angle stacks, produces good estimations of reservoir properties, facies probabilities, and associated uncertainties. Historically, ODiSI has only been applied to siliciclastic reservoir intervals. However, the technique is equally suited to carbonate reservoirs, and ODiSI gives good results for the Mishrif Reservoir interval in the Rumaila Field in Iraq. Of course, a thorough awareness of the quality of all input well data and detailed validation of the parameters input to the inversion process is crucial to understanding the accuracy of the results.

Повышение эффективность фаззинга с помощью интервальных мутаций

This paper presents a novel approach of generation effective inputs for fuzz testing. Most applications check input format before performing basic calculations. That kind of applications usually parse service information of input file to decide whether it is supported or not. Input formats which are not supported are discarded and the application finishes its execution immediately. For example, the service information of ELF (Extensible Linking Format) file should start with the following data: 0x7f 'E' 'L' 'F'. If a file does not contain this information in header section then it will not be considered as ELF. Effective fuzzing of an application which has input validation stage is a relevant and important problem. Random changes of input files usually malform service data and the target application finishes immediately without execution of main code. This makes fuzzing process inefficient. To solve this problem, we have designed and implemented three special plugins for ISP-Fuzzer. The first plugin is intended to collect execution traces. The second plugin connects fragments of input data and executed basic blocks of the target program. Based on that information we can determine potential fragments (critical fragments) of input data which should not be mutated for new test case generation. The third plugin is designed for interval mutations. It mutates input file escaping critical fragments detected by the second plugin. Experimental results prove the effectiveness of proposed method.

Secure Programming with SAS: Mitigating Risks and Protecting Data Integrity

This article examines the significance of safe programming with SAS (Statistical Analysis System) for risk mitigation and data integrity protection in data-driven environments. The study intends to investigate data protection strategies to improve application security, look at best practices for secure SAS coding, and identify prevalent security threats related to SAS applications. The review process is secondary data, with insights gleaned from online resources, industry reports, conference papers, and academic journals. Important discoveries show that SAS programs frequently have vulnerabilities, including SQL injection, cross-site scripting (XSS), and unsafe data handling. The best practices that have been established encompass data encryption, secure access controls, output encoding, parameterized queries, and input validation. The policy implications emphasize the significance of legislative frameworks for data protection and encouraging instruction in secure programming practices. This report emphasizes how important it is for businesses to use SAS for secure programming to protect sensitive data, abide by data protection laws, and defend against cyberattacks.

EPISTEMOLOGICAL AUTHENTICITY AS A MORAL IMPERATIVE TO EXPLAIN TOURIST REALITIES

Purpose. Our research intention is to reveal new contexts of authenticity in tourism and their sources. Analytical works also provide justification for moral responsibility of researchers regarding the course of research works and circumstances in which they are conducted. Method. The principal research method was critical review of subject-matter literature. We used the autoethnographic approach combined with field participant observation. Findings. Critical desk research on the subject-matter literature and other sources of methodical reflection has led to a new understanding of authenticity in tourism. The paper also discusses traits of original research attitudes. Finally, it proposes a diagnosis of the contemporary model of science. Research and conclusions limitations. This is a theoretical contribution resulting from the heuristic approach. The effects of research published here reflect the author’s views. Practical implications. Considerations included in the paper and results flowing from them are addressed to those who manage tourist space at different administrative levels. Ideas and methodical suggestions included in it may provide valid input into scientific debates. Originality. The ideas outlined here represent a new approach to the issue of authenticity in tourism. They also justify the introduction of autoethnography into research on tourism. Type of work. This work is an overview and a conceptual paper.

VESSEL-INDUCED SURGE MODEL VALIDATION IN A COMPLEX HARBOR USING FIELD MEASUREMENTS AND AIS DATA

Large ships moving in narrow waterways generate complex hydrodynamic phenomena, which can cause extreme forces and moments on berthed vessels, and damage shoreline structures and marine habitat. Deep-draft vessels can generate complex phenomena such as bow solitary waves, broken supercritical bore waves, and ship-induced harbor seiche/oscillations. Particular focus is paid in the present paper to simulation and validation of the Vessel Hydrodynamics Longwave Unsteady (VH-LU) modeling system (Fenical et al. 2006) for these complex hydrodynamic phenomena, in particular sloshing and resonance of vessel-induced waves in the complex harbor of Port Canaveral, FL. This paper summarizes two field measurement campaigns and utilization of high-resolution AIS data to provide detailed input for model validation. Analysis indicates that the modeling system is able to reproduce water level oscillations generated by individual vessels, oscillation interactions within the harbor, and interactions between separate basins in the harbor over time. Previous publications have provided limited information on validations performed with the VH-LU modeling system (e.g. Fenical et al. 2011). The model has been shown to accurately reproduce hydrodynamics and berthed vessel forces on tankers in open water conditions, hydrodynamics and loads on berthed Panamax container ships against a vertical quay wall, and supercritical flow and broken bore formation and propagation. This paper will summarize the complete range of model validations with the highest-quality openly available laboratory data, and provide recommendations for future studies and research based on observed needs from practical applications.