Search based path and input data generation for web application testing

Test case generation for web applications aims at ensuring full coverage of the navigation structure. Existing approaches resort to crawling and manual/random input generation, with or without a preliminary construction of the navigation model. However, crawlers might be unable to reach some parts of the web application and random input generation might not receive enough guidance to produce the inputs needed to cover a given path. In this paper, we take advantage of the navigation structure implicitly specified by developers when they write the page objects used for web testing and we define a novel set of genetic operators that support the joint generation of test inputs and feasible navigation paths. On a case study, our tool Subweb was able to achieve higher coverage of the navigation model than crawling based approaches, thanks to its intrinsic ability of generating inputs for feasible paths and of discarding likely infeasible paths

SmartFog: Training the Fog for the energy-saving analytics of Smart-Meter data

In this paper, we characterize the main building blocks and numerically verify the classification accuracy and energy performance of SmartFog, a distributed and virtualized networked Fog technological platform for the support for Stacked Denoising Auto-Encoder (SDAE)-based anomaly detection in data flows generated by Smart-Meters (SMs). In SmartFog, the various layers of an SDAE are pretrained at different Fog nodes, in order to distribute the overall computational efforts and, then, save energy. For this purpose, a new Adaptive Elitist Genetic Algorithm (AEGA) is “ad hoc” designed to find the optimized allocation of the SDAE layers to the Fog nodes. Interestingly, the proposed AEGA implements a (novel) mechanism that adaptively tunes the exploration and exploitation capabilities of the AEGA, in order to quickly escape the attraction basins of local minima of the underlying energy objective function and, then, speed up the convergence towards global minima. As a matter of fact, the main distinguishing feature of the resulting SmartFog paradigm is that it accomplishes the joint integration on a distributed Fog computing platform of the anomaly detection functionality and the minimization of the resulting energy consumption. The reported numerical tests support the effectiveness of the designed technological platform and point out that the attained performance improvements over some state-of-the-art competing solutions are around 5%, 68% and 30% in terms of detection accuracy, execution time and energy consumption, respectively

Present Scenario of Fog Computing and Hopes for Future Research

According to the forecast that billions of devices will get connected to the Internet by 2020. All these devices will produce a huge amount of data that will have to be handled rapidly and in a feasible manner. It will become a challenge for real-time applications to handle this huge data while considering security issues as well as time constraints. The main highlights of cloud computing are on-demand service and scalability; therefore the data generated from IoT devices are generally handled in cloud infrastructure. Though, dealing with IoT application requests on the cloud exclusively is not a proficient result for some IoT applications particularly time-sensitive ones. These issues can be settled by utilizing another idea called, Fog computing. Fog computing has become one of the major fields of research from both academia and industry perspectives. The ongoing research commitments on few issues in fog computing are figuring out in this paper. At long last, this paper also highlights some open issues in fog with IoT, which will determine the future research direction for implementing Fog computing paradigm

End-to-End Application Cloning for Distributed Cloud Microservices with Ditto

We present Ditto, an automated framework for cloning end-to-end cloud applications, both monolithic and microservices, which captures I/O and network activity, as well as kernel operations, in addition to application logic. Ditto takes a hierarchical approach to application cloning, starting with capturing the dependency graph across distributed services, to recreating each tier's control/data flow, and finally generating system calls and assembly that mimics the individual applications. Ditto does not reveal the logic of the original application, facilitating publicly sharing clones of production services with hardware vendors, cloud providers, and the research community. We show that across a diverse set of single- and multi-tier applications, Ditto accurately captures their CPU and memory characteristics as well as their high-level performance metrics, is portable across platforms, and facilitates a wide range of system studies

Enhancing Security and Privacy on Smart City’s Collected Data: A Fog Computing Perspective

Smart cities use information and communication technologies to deliver services to their citizens. Use of ICT makes them to be more intelligent and efficient in usage of resources, resulting in cost and energy savings, improved service delivery and quality of life. Smart cities are expected to be the fundamental pillars of continued economic growth and improved services delivery. Smart City technology is having ability to constantly gather information about the city, sharing the data with people, devices and technologies or borrowing relevant data from elsewhere, for analysis to enable informed decision making. For instance internet of things has emerged as a technological driving force in real time service delivery in smart cities. These applications provide new abilities, enhancing monitoring, and provision of action oriented process on control and device management. Smart devices are a major source of big data in smart cities. With expected increase of billions of smart devices and sensors in smart city by the year 2020, more data will be generated which will reduce efficiency of cloud access, due to increased volume. Security and privacy of data is a challenge in smart city, negligence in data security and privacy can be amplified in folds resulting to faulty applications, services along with paralyzing the entire city through Denial of Service (DDoS) attack, Spear Phishing Attacksand Brute-Force Attacks among others.Fog computing FC is a new paradigm that is intended to extend cloud computing CC through deployment of processing and localized units into the network edge, enabling low latency, offering location awareness and latency sensitiveness. Homomorphism for encryption, authorization, authentication, and classification are performed on collected data in smart cities to improve security and privacy. In this paper assimilation and analysis, is performed with fog computing aspects of decentralization, different policies for datacenter transferstrategies being analyzed.Processing time, access time, request time, response time and cost analysis show system efficiency

Enhancing Security and Privacy on Smart City’s Collected Data: A Fog Computing Perspective

Smart cities use information and communication technologies to deliver services to their citizens. Use of ICT makes them to be more intelligent and efficient in usage of resources, resulting in cost and energy savings, improved service delivery and quality of life. Smart cities are expected to be the fundamental pillars of continued economic growth and improved services delivery. Smart City technology is having ability to constantly gather information about the city, sharing the data with people, devices and technologies or borrowing relevant data from elsewhere, for analysis to enable informed decision making. For instance internet of things has emerged as a technological driving force in real time service delivery in smart cities. These applications provide new abilities, enhancing monitoring, and provision of action oriented process on control and device management. Smart devices are a major source of big data in smart cities. With expected increase of billions of smart devices and sensors in smart city by the year 2020, more data will be generated which will reduce efficiency of cloud access, due to increased volume. Security and privacy of data is a challenge in smart city, negligence in data security and privacy can be amplified in folds resulting to faulty applications, services along with paralyzing the entire city through Denial of Service (DDoS) attack, Spear Phishing Attacksand Brute-Force Attacks among others.Fog computing FC is a new paradigm that is intended to extend cloud computing CC through deployment of processing and localized units into the network edge, enabling low latency, offering location awareness and latency sensitiveness. Homomorphism for encryption, authorization, authentication, and classification are performed on collected data in smart cities to improve security and privacy. In this paper assimilation and analysis, is performed with fog computing aspects of decentralization, different policies for datacenter transferstrategies being analyzed.Processing time, access time, request time, response time and cost analysis show system efficiency

A fog computing solution for context-based privacy leakage detection for android healthcare devices

Intelligent medical service system integrates wireless internet of things (WIoT), including medical sensors, wireless communications, and middleware techniques, so as to collect and analyze patients' data to examine their physical conditions by many personal health devices (PHDs) in real time. However, large amount of malicious codes on the Android system can compromise consumers' privacy, and further threat the hospital management or even the patients' health. Furthermore, this sensor-rich system keeps generating large amounts of data and saturates the middleware system. To address these challenges, we propose a fog computing security and privacy protection solution. Specifically, first, we design the security and privacy protection framework based on the fog computing to improve tele-health and tele-medicine infrastructure. Then, we propose a context-based privacy leakage detection method based on the combination of dynamic and static information. Experimental results show that the proposed method can achieve higher detection accuracy and lower energy consumption compared with other state-of-art methods.This work was supported by the National Natural Science Foundation of China (General Program) under Grant No.61572253, the 13th Five-Year Plan Equipment Pre-Research Projects Fund under Grant No.61402420101HK02001, and the Aviation Science Fund under Grant No. 2016ZC52030

The Influence of Sociological Variables on Users’ Feelings about Programmatic Advertising and the Use of Ad-Blockers

The evolution of digital advertising, which is aimed at a mass audience, to programmatic advertising, which is aimed at individual users depending on their profile, has raised concerns about the use of personal data and invasion of user privacy on the Internet. Concerned users install ad-blockers that prevent users from seeing ads and this has resulted in many companies using antiad-blockers. This study investigates the sociological variables that make users feel that advertising is annoying and then decide to use ad-blockers to avoid it. Our results provide useful information for companies to appropriately segment user profiles. To do this, data collected from Internet users (n = 19,973) about what makes online advertising annoying and why they decide to use ad-blockers are analyzed. First, the existing literature on the subject was reviewed and then the relevant sociological variables that influence users’ feelings about online advertising and the use of ad-blockers were investigated. This work contributes new information to the discussion about user privacy on the Internet. Some of the key findings suggest that Internet advertising can be very intrusive for many users and that all the variables investigated, except marital status and education, influence the users’opinions. It was also found that all the variables in this study are important when a user decides to use an ad-blocker. A clear and inverse correlation between age and opinion about advertising as annoying could be seen, along with a clear difference of opinion due to gender. The results suggest that users without children use ad-blockers the least, while retirees and housewives use them the most

Detecting Deceptive Dark-Pattern Web Advertisements for Blind Screen-Reader Users

Advertisements have become commonplace on modern websites. While ads are typically designed for visual consumption, it is unclear how they affect blind users who interact with the ads using a screen reader. Existing research studies on non-visual web interaction predominantly focus on general web browsing; the specific impact of extraneous ad content on blind users\u27 experience remains largely unexplored. To fill this gap, we conducted an interview study with 18 blind participants; we found that blind users are often deceived by ads that contextually blend in with the surrounding web page content. While ad blockers can address this problem via a blanket filtering operation, many websites are increasingly denying access if an ad blocker is active. Moreover, ad blockers often do not filter out internal ads injected by the websites themselves. Therefore, we devised an algorithm to automatically identify contextually deceptive ads on a web page. Specifically, we built a detection model that leverages a multi-modal combination of handcrafted and automatically extracted features to determine if a particular ad is contextually deceptive. Evaluations of the model on a representative test dataset and \u27in-the-wild\u27 random websites yielded F1 scores of 0.86 and 0.88, respectively