Biblio
The term "artificial intelligence" is a buzzword today and is heavily used to market products, services, research, conferences, and more. It is scientifically disputed which types of products and services do actually qualify as "artificial intelligence" versus simply advanced computer technologies mimicking aspects of natural intelligence. Yet it is undisputed that, despite often inflationary use of the term, there are mainstream products and services today that for decades were only thought to be science fiction. They range from industrial automation, to self-driving cars, robotics, and consumer electronics for smart homes, workspaces, education, and many more contexts. Several technological advances enable what is commonly referred to as "artificial intelligence". It includes connected computers and the Internet of Things (IoT), open and big data, low cost computing and storage, and many more. Yet regardless of the definition of the term artificial intelligence, technological advancements in this area provide immense potential, especially for people with disabilities. In this paper we explore some of these potential in the context of web accessibility. We review some existing products and services, and their support for web accessibility. We propose accessibility conformance evaluation as one potential way forward, to accelerate the uptake of artificial intelligence, to improve web accessibility.
With the transition from IPv4 IPv6 protocol to improve network communications, there are concerns about devices and applications' security that must be dealt at the beginning of implementation or during its lifecycle. Automate the vulnerability assessment process reduces management overhead, enabling better management of risks and control of the vulnerabilities. Consequently, it reduces the effort needed for each test and it allows the increase of the frequency of application, improving time management to perform all the other complicated tasks necessary to support a secure network. There are several researchers involved in tests of vulnerability in IPv6 networks, exploiting addressing mechanisms, extension headers, fragmentation, tunnelling or dual-stack networks (using both IPv4 and IPv6 at the same time). Most existing tools use the programming languages C, Java, and Python instead of a language designed specifically to create a suite of tests, which reduces maintainability and extensibility of the tests. This paper presents a solution for IPv6 vulnerabilities scan tests, based on attack simulations, combining passive analysis (observing the manifestation of behaviours of the system under test) and an active one (stimulating the system to become symptomatic). Also, it describes a prototype that simulates and detects denial-of-service attacks on the ICMPv6 Protocol from IPv6. Also, a detailed report is created with the identified vulnerability and the possible existing solutions to mitigate such a gap, thus assisting the process of vulnerability management.
This paper studies the principle of vulnerability generation and mechanism of cross-site scripting attack, designs a dynamic cross-site scripting vulnerabilities detection technique based on existing theories of black box vulnerabilities detection. The dynamic detection process contains five steps: crawler, feature construct, attacks simulation, results detection and report generation. Crawling strategy in crawler module and constructing algorithm in feature construct module are key points of this detection process. Finally, according to the detection technique proposed in this paper, a detection tool is accomplished in Linux using python language to detect web applications. Experiments were launched to verify the results and compare with the test results of other existing tools, analyze the usability, advantages and disadvantages of the detection method above, confirm the feasibility of applying dynamic detection technique to cross-site scripting vulnerabilities detection.
Smartphone has become the tool which is used daily in modern human life. Some activities in human life, according to the usage of the smartphone can be related to the information which has a high privilege and needs a privacy. It causes the owners of the smartphone needs a system which can protect their privacy. Unfortunately, the secure the system, the unease of the usage. Hence, the system which has an invulnerable environment but also gives the ease of use is very needful. The aspect which is related to the ease of use is an authentication mechanism. Sometimes, this aspect correspondence to the effectiveness and the efficiency. This study is going to analyze the application related to this aspect which is a lock screen application. This lock screen application uses the context data based on the environment condition around the user. The context data used are GPS location and Mac Address of Wi-Fi. The system is going to detect the context and is going to determine if the smartphone needs to run the authentication mechanism or to bypass it based on the analysis of the context data. Hopefully, the smartphone application which is developed still can provide mobility and usability features, and also can protect the user privacy even though it is located in the environment which its context data is unknown.
Multirotor Unmanned Aerial Vehicles (UAV) have grown in popularity for research and education, overcoming challenges associated with fixed wing and ground robots. Unfortunately, extensive physical testing can be expensive and time consuming because of short flight times due to battery constraints and safety precautions. Simulation tools offer a low barrier to entry and enable testing and validation before field trials. However, most of the well-known simulators today have a high barrier to entry due to the need for powerful computers and the time required for initial set up. In this paper, we present OpenUAV, an open source test bed for UAV education and research that overcomes these barriers. We leverage the Containers as a Service (CaaS) technology to enable students and researchers carry out simulations on the cloud. We have based our framework on open-source tools including ROS, Gazebo, Docker, PX4, and Ansible, we designed the simulation framework so that it has no special hardware requirements. Two use-cases are presented. First, we show how a UAV can navigate around obstacles, and second, we test a multi-UAV swarm formation algorithm. To our knowledge, this is the first open-source, cloud-enabled testbed for UAVs. The code is available on GitHub: https://github.com/Open-UAV.
Although the importance of mobile applications grows every day, recent vulnerability reports argue the application's deficiency to meet modern security standards. Testing strategies alleviate the problem by identifying security violations in software implementations. This paper proposes a novel testing methodology that applies state machine learning of mobile Android applications in combination with algorithms that discover attack paths in the learned state machine. The presence of an attack path evidences the existence of a vulnerability in the mobile application. We apply our methods to real-life apps and show that the novel methodology is capable of identifying vulnerabilities.
As a result of the globalization of integrated circuits (ICs) design and fabrication process, ICs are becoming vulnerable to hardware Trojans. Most of the existing hardware Trojan detection works suppose that the testing stage is trustworthy. However, testing parties may conspire with malicious attackers to modify the results of hardware Trojan detection. In this paper, we propose a trusted and robust hardware Trojan detection framework against untrustworthy testing parties exploiting a novel clustering ensemble method. The proposed technique can expose the malicious modifications on Trojan detection results introduced by untrustworthy testing parties. Compared with the state-of-the-art detection methods, the proposed technique does not require fabricated golden chips or simulated golden models. The experiment results on ISCAS89 benchmark circuits show that the proposed technique can resist modifications robustly and detect hardware Trojans with decent accuracy (up to 91%).
Compressed sensing (CS) integrates sampling and compression into a single step to reduce the processed data amount. However, the CS reconstruction generally suffers from high complexity. To solve this problem, compressive signal processing (CSP) is recently proposed to implement some signal processing tasks directly in the compressive domain without reconstruction. Among various CSP techniques, compressive detection achieves the signal detection based on the CS measurements. This paper investigates the compressive detection problem of random signals when the measurements are corrupted. Different from the current studies that only consider the dense noise, our study considers both the dense noise and sparse error. The theoretical performance is derived, and simulations are provided to verify the derived theoretical results.
Spam emails have been a chronic issue in computer security. They are very costly economically and extremely dangerous for computers and networks. Despite of the emergence of social networks and other Internet based information exchange venues, dependence on email communication has increased over the years and this dependence has resulted in an urgent need to improve spam filters. Although many spam filters have been created to help prevent these spam emails from entering a user's inbox, there is a lack or research focusing on text modifications. Currently, Naive Bayes is one of the most popular methods of spam classification because of its simplicity and efficiency. Naive Bayes is also very accurate; however, it is unable to correctly classify emails when they contain leetspeak or diacritics. Thus, in this proposes, we implemented a novel algorithm for enhancing the accuracy of the Naive Bayes Spam Filter so that it can detect text modifications and correctly classify the email as spam or ham. Our Python algorithm combines semantic based, keyword based, and machine learning algorithms to increase the accuracy of Naive Bayes compared to Spamassassin by over two hundred percent. Additionally, we have discovered a relationship between the length of the email and the spam score, indicating that Bayesian Poisoning, a controversial topic, is actually a real phenomenon and utilized by spammers.
The subsystem of IoMT (Internet of Military of Things) called IoBT (Internet of Battle of Things) is the major resource of the military where the various stack holders of the battlefield and different categories of equipment are tightly integrated through the internet. The proposed architecture mentioned in this paper will be helpful to design IoBT effectively for warfare using irresistible technologies like information technology, embedded technology, and network technology. The role of Machine intelligence is essential in IoBT to create smart things and provide accurate solutions without human intervention. Non-Destructive Testing (NDT) is used in Industries to examine and analyze the invisible defects of equipment. Generally, the ultrasonic waves are used to examine and analyze the internal defects of materials. Hence the proposed architecture of IoBT is enhanced by ultrasonic based NDT to study the properties of the things of the battlefield without causing any damage.
Existing systems allow manufacturers to acquire factory floor data and perform analysis with cloud applications for machine health monitoring, product quality prediction, fault diagnosis and prognosis etc. However, they do not provide capabilities to perform testing of machine tools and associated components remotely, which is often crucial to identify causes of failure. This paper presents a fault diagnosis system in a cyber-physical manufacturing cloud (CPMC) that allows manufacturers to perform diagnosis and maintenance of manufacturing machine tools through remote monitoring and online testing using Machine Tool Communication (MTComm). MTComm is an Internet scale communication method that enables both monitoring and operation of heterogeneous machine tools through RESTful web services over the Internet. It allows manufacturers to perform testing operations from cloud applications at both machine and component level for regular maintenance and fault diagnosis. This paper describes different components of the system and their functionalities in CPMC and techniques used for anomaly detection and remote online testing using MTComm. It also presents the development of a prototype of the proposed system in a CPMC testbed. Experiments were conducted to evaluate its performance to diagnose faults and test machine tools remotely during various manufacturing scenarios. The results demonstrated excellent feasibility to detect anomaly during manufacturing operations and perform testing operations remotely from cloud applications using MTComm.
Combining conventional power networks and information communication technologies forms smart grid concept. Researches on the evolution of conventional power grid system into smart grid continue thanks to the development of communication and information technologies hopefully. Testing of smart grid systems is usually performed in simulation environments. However, achieving more effective real-world implementations, a smart grid application needs a real-world test environment, called testbed. Smart grid, which is the combination of conventional electricity line with information communication technologies, is vulnerable to cyber-attacks and this is a key challenge improving the smart grid. The vulnerabilities to cyber-attacks in smart grid arise from information communication technologies' nature inherently. Testbeds, which cyber-security researches and studies can be performed, are needed to find effective solutions against cyber-attacks capabilities in smart grid practices. In this paper, an evaluation of existing smart grid testbeds with the capability of cyber security is presented. First, background, domains, research areas and security issues in smart grid are introduced briefly. Then smart grid testbeds and features are explained. Also, existing security-oriented testbeds and cyber-attack testing capabilities of testbeds are evaluated. Finally, we conclude the study and give some recommendations for security-oriented testbed implementations.
Assertions are helpful in program analysis, such as software testing and verification. The most challenging part of automatically recommending assertions is to design the assertion patterns and to insert assertions in proper locations. In this paper, we develop Weak-Assert, a weakness-oriented assertion recommendation toolkit for program analysis of C code. A weakness-oriented assertion is an assertion which can help to find potential program weaknesses. Weak-Assert uses well-designed patterns to match the abstract syntax trees of source code automatically. It collects significant messages from trees and inserts assertions into proper locations of programs. These assertions can be checked by using program analysis techniques. The experiments are set up on Juliet test suite and several actual projects in Github. Experimental results show that Weak-Assert helps to find 125 program weaknesses in 26 actual projects. These weaknesses are confirmed manually to be triggered by some test cases.
Reconnaissance phase is where attackers identify their targets and how to collect information from professional social networks which can be used to select and exploit targeted employees to penetrate in an organization. Here, a framework is proposed for the early detection of attackers in the reconnaissance phase, highlighting the common characteristic behavior among attackers in professional social networks. And to create artificial honeypot profiles within the organizational social network which can be used to detect a potential incoming threat. By analyzing the dataset of social Network profiles in combination of machine learning techniques, A DspamRPfast model is proposed for the creation of a classifier system to predict the probabilities of the profiles being fake or malicious and to filter them out using XGBoost and for the faster classification and greater accuracy of 84.8%.
This Innovative Practice Work in Progress paper makes the case for using concept inventories in cybersecurity education and presents an example of the development of a concept inventory in the field of secure programming. The secure programming concept inventory is being developed by a team of researchers from four universities. We used a Delphi study to define the content area to be covered by the concept inventory. Participants in the Delphi study included ten experts from academia, government, and industry. Based on the results, we constructed a concept map of secure programming concepts. We then compared this concept map to the Joint Task Force on Cybersecurity Education Curriculum 2017 guidelines to ensure complete coverage of secure programming concepts. Our mapping indicates a substantial match between the concept map and those guidelines.
The 2018 Biometric Technology Rally was an evaluation, sponsored by the U.S. Department of Homeland Security, Science and Technology Directorate (DHS S&T), that challenged industry to provide face or face/iris systems capable of unmanned, traveler identification in a high-throughput security environment. Selected systems were installed at the Maryland Test Facility (MdTF), a DHS S&T affiliated bio-metrics testing laboratory, and evaluated using a population of 363 naive human subjects recruited from the general public. The performance of each system was examined based on measured throughput, capture capability, matching capability, and user satisfaction metrics. This research documents the performance of unmanned face and face/iris systems required to maintain an average total subject interaction time of less than 10 seconds. The results highlight discrepancies between the performance of biometric systems as anticipated by the system designers and the measured performance, indicating an incomplete understanding of the main determinants of system performance. Our research shows that failure-to-acquire errors, unpredicted by system designers, were the main driver of non-identification rates instead of failure-to-match errors, which were better predicted. This outcome indicates the need for a renewed focus on reducing the failure-to-acquire rate in high-throughput, unmanned biometric systems.
The "aging" phenomenon occurs after the long-term running of software, with the fault rate rising and running efficiency dropping. As there is no corresponding testing type for this phenomenon among conventional software tests, "software runtime accumulative testing" is proposed. Through analyzing several examples of software aging causing serious accidents, software is placed in the system environment required for running and the occurrence mechanism of software aging is analyzed. In addition, corresponding testing contents and recommended testing methods are designed with regard to all factors causing software aging, and the testing process and key points of testing requirement analysis for carrying out runtime accumulative testing are summarized, thereby providing a method and guidance for carrying out "software runtime accumulative testing" in software engineering.
Due to its costly and time-consuming nature and a wide range of passive barrier elements and tools for their breaching, testing the delay time of passive barriers is only possible as an experimental tool to verify expert judgements of said delay times. The article focuses on the possibility of creating and utilizing a new method of acquiring values of delay time for various passive barrier elements using expert judgements which could add to the creation of charts where interactions between the used elements of mechanical barriers and the potential tools for their bypassing would be assigned a temporal value. The article consists of basic description of methods of expert judgements previously applied for making prognoses of socio-economic development and in other societal areas, which are called soft system. In terms of the problem of delay time, this method needed to be modified in such a way that the prospective output would be expressible by a specific quantitative value. To achieve this goal, each stage of the expert judgements was adjusted to the use of suitable scientific methods to select appropriate experts and then to achieve and process the expert data. High emphasis was placed on evaluation of quality and reliability of the expert judgements, which takes into account the specifics of expert selection such as their low numbers, specialization and practical experience.