Biblio

The widespread adoption of eCommerce, iBanking, and eGovernment institutions has resulted in an exponential rise in the use of web applications. Due to a large number of users, web applications have become a prime target of cybercriminals who want to steal Personally Identifiable Information (PII) and disrupt business activities. Hence, there is a dire need to audit the websites and ensure information security. In this regard, several web vulnerability scanners are employed for vulnerability assessment of web applications but attacks are still increasing day by day. Therefore, a considerable amount of research has been carried out to measure the effectiveness and limitations of the publicly available web scanners. It is identified that most of the publicly available scanners possess weaknesses and do not generate desired results. In this paper, the evaluation of publicly available web vulnerability scanners is performed against the top ten OWASP11OWASP® The Open Web Application Security Project (OWASP) is an online community that produces comprehensive articles, documentation, methodologies, and tools in the arena of web and mobile security. vulnerabilities and their performance is measured on the precision of their results. Based on these results, we proposed an Integrated Multi-Agent Blackbox Security Assessment Tool (SAT) for the security assessment of web applications. Research has proved that the vulnerabilities assessment results of the SAT are more extensive and accurate.

Adversarial attacks have recently been proposed to scrutinize the security of deep neural networks. Most blackbox adversarial attacks, which have partial access to the target through queries, are target-specific; e.g., they require a well-trained surrogate that accurately mimics a given target. In contrast, target-agnostic black-box attacks are developed to attack any target; e.g., they learn a generalized surrogate that can adapt to any target via fine-tuning on samples queried from the target. Despite their success, current state-of-the-art target-agnostic attacks require tremendous fine-tuning steps and consequently an immense number of queries to the target to generate successful attacks. The high query complexity of these attacks makes them easily detectable and thus defendable. We propose a novel query-efficient target-agnostic attack that trains a generalized surrogate network to output the adversarial directions iv.r.t. the inputs and equip it with an effective fine-tuning strategy that only fine-tunes the surrogate when it fails to provide useful directions to generate the attacks. Particularly, we show that to effectively adapt to any target and generate successful attacks, it is sufficient to fine-tune the surrogate with informative samples that help the surrogate get out of the failure mode with additional information on the target’s local behavior. Extensive experiments on CIFAR10 and CIFAR-100 datasets demonstrate that the proposed target-agnostic approach can generate highly successful attacks for any target network with very few fine-tuning steps and thus significantly smaller number of queries (reduced by several order of magnitudes) compared to the state-of-the-art baselines.

The topological structure of the network relationship is described by the network diagram, and the formation and evolution process of the network is analyzed by using the cost-benefit method. Assuming that the self-interested network member nodes can connect or break the connection, the network topology model is established based on the dynamic random pairing evolution network model. The static structure of the network is studied. Respecting the psychological cognition law of college students and innovating the core value cultivation model can reverse the youth's identification dilemma with the core values, and then create a good political environment for the normal, healthy, civilized and orderly network participation of the youth. In recognition of the atmosphere, an automatic learning algorithm of Bayesian network structure that effectively integrates expert knowledge and data-driven methods is realized.

In view of the characteristics that rolling bearing is prone to failure under actual working conditions, and it is difficult to classify the fault category and fault degree, the deep belief network is introduced to diagnose the rolling bearing fault. Firstly, principal component analysis is used to reduce the dimension of original input data and delete redundant input information. Then, the dimension reduced data are input into the deep belief network to extract the low dimensional fault feature representation, and the extracted features are input into the classifier for rolling bearing fault pattern recognition. Finally, the diagnosis effect of the proposed network is compared with the existing common shallow neural network. The simulation experiment is carried out through the bearing data in the United States.

In the operation of information technology (IT) services, operators monitor the equipment-issued alarms, to locate the cause of a failure and take action. Alarms generate simultaneously from multiple devices with physical/logical connections. Therefore, if the time and location of the alarms are close to each other, it can be judged that the alarms are likely to be caused by the same event. In this paper, we propose a method that takes a novel approach by correlating alarms considering event units using a Bayesian network based on alarm generation time, generation place, and alarm type. The topology information becomes a critical decision element when doing the alarm correlation. However, errors may occur when topology information updates manually during failures or construction. Therefore, we show that event-by-event correlation with 100% accuracy is possible even if the topology information is 25% wrong by taking into location information other than topology information.

The Internet of Things (IoT) is rapidly evolving, allowing physical items to share information and coordinate with other nodes, increasing IoT’s value and being widely applied to various applications. Radio Frequency Identification (RFID) is usually used in IoT applications to automate item identification by establishing symmetrical communication between the tag device and the reader. Because RFID reading data is typically in plain text, a security mechanism is required to ensure that the reading results from this RFID data remain confidential. Researchers propose a lightweight encryption algorithm framework for IoT-based RFID applications to address this security issue. Furthermore, this research assesses the implementation of lightweight encryption algorithms, such as Grain v1 and Espresso, as two systems scenarios. The Grain v1 encryption is the final eSTREAM project that accepts an 80-bit key, 64-bit IV, and has a 160-bit internal state with limited application. In contrast, the Espresso algorithm has been implemented in various applications such as 5G wireless communication. Furthermore, this paper tested the performance of each encryption algorithm in the microcontroller and inspected the network performance in an IoT system.

Despite the strict measures taken by authorities for children safety, crime against children is increasing. To curb this crime, it is important to improve the safety of children. School authorities can be severely penalized for these incidents, hence monitoring the school bus is significantly important in limiting these incidents. The developing worry of families for the security and insurance of their kids has started incredible interest in creating strong frameworks that give successful following and oversight of kids driving among home and school. Coordinated transport following permits youngsters to partake more in their normal schoolwork longer than trusting that a transport will be late with the assistance of notice and guarantees the security of every understudy. These days, reacting to the necessities existing apart from everything else, numerous instructive foundations have begun to push more towards a compelling global positioning framework of their vehicles that ensures the wellbeing of their understudies. Effective transport following is accomplished by procuring the geographic directions utilizing the GPS module and communicating the informationto a distant server. The framework depends on prepared to-utilize inactive RFID peruses. Make a message pop-up from the server script subsequent to checking the understudy's RFID tag be. The RFID examine exhibiting that the understudy boarded the vehicle to the specific trained professionals and the parent. Successful transport following permits school specialists, guardians, and drivers to precisely design their schedules while protecting kids from the second they get on until they get off the transport. The framework overall makes it conceivable to educate the administration regarding crises or protests. A variety of reports can be generated for different school-wide real-time bus and vehicle activities. This paper reviews the various smart security transport systems proposed for providing security features.

In this paper, a novel composite right/left-handed transmission line (CRLH TL) 3-unit cell is presented for finding excellent time-delay (TD) efficiency of Chipless RFID's True-Time-Delay Lines (TTDLs). RFID (Radio Frequency Identification) is a non-contact automatic identification technology that uses radio frequency (RF) signals to identify target items automatically and retrieve pertinent data without the need for human participation. However, as compared to barcodes, RFID tags are prohibitively expensive and complex to manufacture. Chipless RFID tags are RFID tags that do not contain silicon chips and are therefore less expensive and easier to manufacture. It combines radio broadcasting technology with radar technology. Radio broadcasting technology use radio waves to send and receive voice, pictures, numbers, and symbols, whereas radar technology employs the radio wave reflection theory. Chipless RFID lowers the cost of sensors such as gas, temperature, humidity, and pressure. In addition, Chipless RFID tags can be used as sensors which are also required for security purposes and future IoT applications.

In recent years, body-worn RFID and NFC (near field communication) devices have become one of the principal technologies concurring to the rise of healthcare internet of thing (H-IoT) systems. Similarly, points of care (PoCs) moved increasingly closer to patients to reduce the costs while supporting precision medicine and improving chronic illness management, thanks to timely and frequent feedback from the patients themselves. A typical PoC involves medical sensing devices capable of sampling human health, personal equipment with communications and computing capabilities (smartphone or tablet) and a secure software environment for data transmission to medical centers. Hybrid platforms simultaneously employing NFC and ultra-high frequency (UHF) RFID could be successfully developed for the first sensing layer. An application example of the proposed hybrid system for the monitoring of acute myocardial infarction (AMI) survivors details how the combined use of NFC and UHF-RFID in the same PoC can support the multifaceted need of AMI survivors while protecting the sensitive data on the patient’s health.

Ferroelectric capacitor memory devices with carbon-free Hf0.5Zr0.5O2 (HZO) ferroelectric films are fabricated and characterized. The HZO ferroelectric films are deposited by ALD at temperatures from 225 to 300°C, with HfCl4 and ZrCl4 as the precursors. Residual chlorine from the precursors is measured and studied systematically with various process temperatures. 10nm HZO films with optimal ALD growth temperature at 275°C exhibit remanent polarization of 25µC/cm2 and cycle endurance of 5×1011. Results will be compared with those from HZO films deposited with carbon containing metal-organic precursors.

Dielectric capacitors have attracted attention as energy storage devices that can achieve rapid charge and discharge. But the key to restricting its development is the low energy storage density of dielectric materials. Polyvinylidene fluoride (PVDF), as a polymer with high dielectric properties, is expected to improve the energy storage density of dielectric materials. In this work, the multilayer structure of PVDF ferroelectric polymer is designed, and the influence of the number of layers on the maximum polarization, remanent polarization, applied electric field and energy storage density of the dielectric material is studied. The final obtained double-layer PVDF obtained a discharge energy storage density of 10.6 J/cm3 and an efficiency of 49.1% at an electric field of 410 kV/mm; the three-layer PVDF obtained a discharge energy storage density of 11.0 J/cm3 and an efficiency of 37.2% at an electric field of 440 kV/mm.

This paper proposes a novel concept for an electric generator in which both ac windings and permanent magnets (PMs) are placed in the stator. Concentrated windings with a special pattern and phase coils placed in separate slots are employed. The PMs are positioned in a spoke-type field concentrating arrangement, which provides high flux intensification and enables the use of lower remanence and energy non-rare earth magnets. The rotor is exterior to the stator and has a simple and robust reluctance-type configuration without any active electromagnetic excitation components. The principle of operation is introduced based on the concept of virtual work with closed-form analytical airgap flux density distributions. Initial and parametric design studies were performed using electromagnetic FEA for a 3MW direct-drive wind turbine generator employing PMs of different magnetic remanence and specific energy. Results include indices for the goodness of excitation and the goodness of the electric machine designs; loss; and efficiency estimations, indicating that performance comparable to PM synchronous designs employing expensive and critical supply rare-earth PMs may be achieved with non-rare earth PMs using the proposed configuration.

This paper proposes a magnetic actuator using a partially magnetized FePt thick film as a permanent magnet and membrane material for bi-directional micropumps. The magnetized areas act as flux sources, while the magnetized and unmagnetized areas play a role of the membrane part. The mechanical and magnetic characterization results show FePt has a large tensile strength and a lower Young’s modulus than Si crystal, and a comparable remanence to NdFeB. A magnetic pattern transfer technique with a post thermal demagnetization is proposed and experimentally verified to magnetize the FePt partially. Using the proposed magnetic actuator with partially magnetized FePt film is beneficial to simplify the complicated structure and fabrication process of the bi-directional magnetic micropump besides other magnetic MEMS devices.

Contrary to previous opinion, ‘frost shattering’ is not the only major contributor to rock weathering at mid latitudes and high elevations, more specifically along edges of bedrock escarpments. Lightning is also a significant contributor to land surface denudation. We can show this as lightning strikes on outcrops can dramatically alter the magnetic signature of rocks and is one of the main sources of noise in paleomagnetic studies. Igneous rocks in the highlands of Lesotho, southern Africa (\textgreater 3000 m elevation) provide an ideal study location, as flow lavas remain as prominent ridges that are relatively resistant to weathering. It is well known that lightning strikes can cause large remanent magnetization in rocks with little resultant variation in susceptibility. At two adjoining peaks in the Lesotho highlands, mapped freshly fractured rock correlates with areas of high magnetic intensity (remanent component), but little variation in susceptibility (related to the induced field), and is therefore a clear indicator of lightning damage. The majority of these mapped strike sites occur at the edges of topographic highs. Variations in magnetic intensity are correlated with the much lower resolution national lightning strikes dataset. These data confirm that high elevation edges of peak scarps are the focus of previous lightning strikes. This method of magnetic surveying compared with lightning strike data is a new method of confirming the locations of lightning strikes, and reduces the need for intensive paleomagnetic studies of the area to confirm remanence.

It is proposed to address existing methodological issues in the educational process with the development of intellectual technologies and knowledge representation systems to improve the efficiency of higher education institutions. For this purpose, the structure of relational database is proposed, it will store the information about defended dissertations in the form of a set of attributes (heuristics), representing the mandatory qualification attributes of theses. An inference algorithm is proposed to process the information. This algorithm represents an artificial intelligence, its work is aimed at generating queries based on the applicant preferences. The result of the algorithm's work will be a set of choices, presented in ranked order. Given technologies will allow applicants to quickly become familiar with known scientific results and serve as a starting point for new research. The demand for co-researcher practice in solving the problem of updating the projective thinking methodology and managing the scientific research process has been justified. This article pays attention to the existing parallels between the concepts of technical and human sciences in the framework of their convergence. The concepts of being (economic good and economic utility) and the concepts of consciousness (humanitarian economic good and humanitarian economic utility) are used to form projective thinking. They form direct and inverse correspondences of technology and humanitarian practice in the techno-humanitarian mathematical space. It is proposed to place processed information from the language of context-free formal grammar dissertation abstracts in this space. The principle of data manipulation based on formal languages with context-free grammar allows to create new structures of subject areas in terms of applicants' preferences.It is believed that the success of applicants’ work depends directly on the cognitive training of applicants, which needs to be practiced psychologically. This practice is based on deepening the objectivity and adequacy qualities of obtaining information on the basis of heuristic methods. It requires increased attention and development of intelligence. The paper studies the use of heuristic methods by applicants to find new research directions leads to several promising results. These results can be perceived as potential options in future research. This contributes to an increase in the level of retention of higher education professionals.

Malicious attacks, malware, and ransomware families pose critical security issues to cybersecurity, and it may cause catastrophic damages to computer systems, data centers, web, and mobile applications across various industries and businesses. Traditional anti-ransomware systems struggle to fight against newly created sophisticated attacks. Therefore, state-of-the-art techniques like traditional and neural network-based architectures can be immensely utilized in the development of innovative ransomware solutions. In this paper, we present a feature selection-based framework with adopting different machine learning algorithms including neural network-based architectures to classify the security level for ransomware detection and prevention. We applied multiple machine learning algorithms: Decision Tree (DT), Random Forest (RF), Naïve Bayes (NB), Logistic Regression (LR) as well as Neural Network (NN)-based classifiers on a selected number of features for ransomware classification. We performed all the experiments on one ransomware dataset to evaluate our proposed framework. The experimental results demonstrate that RF classifiers outperform other methods in terms of accuracy, F -beta, and precision scores.

True Random Number Generator (TRNG) is an important hardware security primitive for system security. TRNGs are capable of providing random bits for initialization vectors in encryption engines, for padding and nonces in authentication protocols and for seeds to pseudo random number generators (PRNG). A TRNG needs to meet the same statistical quality standards as a physical unclonable function (PUF) with regard to randomness and uniqueness, and therefore one can envision a unified architecture for both functions. In this paper, we investigate a FPGA implementation of a TRNG using the Shift-register Reconvergent-Fanout (SiRF) PUF. The SiRF PUF measures path delays as a source of entropy within a engineered logic gate netlist. The delays are measured at high precision using a time-to-digital converter, and then processed into a random bitstring using a series of linear-time mathematical operations. The SiRF PUF algorithm that is used for key generation is reused for the TRNG, with simplifications that improve the bit generation rate of the algorithm. This enables the TRNG to leverage both fixed PUF-based entropy and random noise sources, and makes the TRNG resilient to temperature-voltage attacks. TRNG bitstrings generated from a programmable logic implementation of the SiRF PUF-TRNG on a set of FPGAs are evaluated using statistical testing tools.

Recently, Cloud Computing became one of today’s great innovations for provisioning Information Technology (IT) resources. Moreover, a new model has been introduced named Fog Computing, which addresses Cloud Computing paradigm issues regarding time delay and high cost. However, security challenges are still a big concern about the vulnerabilities to both Cloud and Fog Computing systems. Man- in- the- Middle (MITM) is considered one of the most destructive attacks in a Fog Computing context. Moreover, it’s very complex to detect MiTM attacks as it is performed passively at the Software-Defined Networking (SDN) level, also the Fog Computing paradigm is ideally suitable for MITM attacks. In this paper, a MITM mitigation scheme will be proposed consisting of an SDN network (Fog Leaders) which controls a layer of Fog Nodes. Furthermore, Multi-Path TCP (MPTCP) has been used between all edge devices and Fog Nodes to improve resource utilization and security. The proposed solution performance evaluation has been carried out in a simulation environment using Mininet, Ryu SDN controller and Multipath TCP (MPTCP) Linux kernel. The experimental results showed that the proposed solution improves security, network resiliency and resource utilization without any significant overheads compared to the traditional TCP implementation.

Recent years have witnessed several educational innovations to provide effective and engaging classroom instruction with the integration of immersive interactions based on augmented reality and virtual reality (AR/VR). This paper outlines the development of an ARCore-based application (app) that can impart interactive experiences for hands-on learning in engineering laboratories. The ARCore technology enables a smartphone to sense its environment and detect horizontal and vertical surfaces, thus allowing the smartphone to estimate any position in its workspace. In this mobile app, with touch-based interaction and AR feedback, the user can interact with a wheeled mobile robot and reinforce the concepts of kinematics for a differential drive mobile robot. The user experience is evaluated and system performance is validated through a user study with participants. The assessment shows that the proposed AR interface for interacting with the experimental setup is intuitive, easy to use, exciting, and recommendable.

In this paper, an implementation of a human in the loop (HITL) technique for robot navigation in an indoor environment is described. The HITL technique is integrated into the reinforcement learning algorithms for mobile robot navigation. Reinforcement algorithms, specifically Q-learning and SARSA, are used combined with HITL since these algorithms are good in exploration and navigation. Turtlebot3 has been used as the robot for validating the algorithms by implementing the system using Robot Operating System and Gazebo. The robot-assisted with human feedback was found to be better in navigation task execution when compared to standard algorithms without using human in the loop. This is a work in progress and the next step of this research is exploring other reinforced learning methods and implementing them on a physical robot.

Learning movement control is a fundamental process integral to infant development. However, it is still unclear how infants learn to control leg movement. This work explores the potential of using socially assistive robots to provide real-time adaptive reinforcement learning for infants. Ten 6 to 8-month old typically-developing infants participated in a study where a robot provided reinforcement when the infant’s right leg acceleration fell within the range of 9 to 20 m/s2. If infants increased the proportion of leg accelerations in this band, they were categorized as "performers". Six of the ten participating infants were categorized as performers; the performer subgroup increased the magnitude of acceleration, proportion of target acceleration for right leg, and ratio of right/left leg acceleration peaks within the target acceleration band and their right legs increased movement intensity from the baseline to the contingency session. The results showed infants specifically adjusted their right leg acceleration in response to a robot- provided reward. Further study is needed to understand how to improve human-robot interaction policies for personalized interventions for young infants.

In recent years, the use of the Internet of Things (IoT) has increased rapidly in different areas. Due to many IoT applications, many limitations have emerged such as power consumption and limited resources. The security of connected devices is becoming more and more a primary need for the reliability of systems. Among other things, power consumption remains an essential constraint with a major impact on the quality of the encryption system. For these, several lightweight cryptography algorithms were proposed and developed. The PRESENT algorithm is one of the lightweight block cipher algorithms that has been proposed for a highly restrictive application. In this paper, we have proposed an efficient hardware serial architecture that uses 16 bits for data path encryption. It uses fewer FPGA resources and achieves higher throughput compared to other existing hardware applications.

Recent years have witnessed impressive advances in technology which led to the rapid growth of the Internet of Things (IoT) and Wireless Sensor Networks (WSNs) using numerous low-powered devices with a huge number of actuators and sensors. These devices gather and exchange data over the internet and generate enormous amounts of data needed to be secured. Although traditional cryptography provides an efficient means of addressing device and communication confidentiality, integrity, and authenticity issues, it may not be appropriate for very resource-constrained systems, particularly for end-nodes such as a simply connected sensor. Thus, there is an ascent need to use lightweight cryptography (LWC) providing the needed level of security with less complexity, area and energy overhead. In this paper, four lightweight cryptographic algorithms called PRESENT, LED, Piccolo, and SPARX were implemented over a Contiki-based IoT operating system, dedicated for IoT platforms, and assessed regarding RAM and ROM usage, power and energy consumption, and CPU cycles number. The Cooja network simulator is used in this study to determine the best lightweight algorithms to use in IoT applications utilizing wireless sensor networks technology.

This paper explores high throughput architectures for the substitution modules, which are an integral component of encryption algorithms. The security algorithms chosen belong to the category of lightweight crypto-primitives suitable for pervasive computing. The focus of this work is on the implementation of encryption algorithms on hardware platforms to improve speed and facilitate optimization in the area and power consumption of the design. In this work, the architecture for the encryption algorithms' substitution box (S-box) is modified using switching circuits (i.e., MUX-based) along with a logic generator and included in the overall cipher design. The modified architectures exhibit high throughput and consume less energy in comparison to the state-of-the-art designs. The percentage increase in throughput or maximum frequency differs according to the chosen algorithms discussed elaborately in this paper. The evaluation of various metrics specific to the design are executed at RFID-specific frequency so that they can be deployed in an IoT environment. The designs are mainly simulated and compared on Nexys4 DDR FPGA platform, along with a few other FPGAs, to meet similar design and implementation environments for a fair comparison. The application of the proposed S-box modification is explored for the healthcare scenario with promising results.

Ant Colony Optimization is applied to design a suitable and shortest route between the starting node point and the end node point in the Wireless Sensor Network (WSN). In general ant colony algorithm plays a good role in path planning process that can also applied in improving the network security. Therefore to protect the network from the malicious nodes an ACO based Dynamic Key Management (ACO-DKM) scheme is proposed. The routes are diagnosed through ACO method also the actual coverage distance and pheromone updating strategy is updated simultaneously that prevents the node from continuous monitoring. Simulation analysis gives the efficiency of the proposed scheme.