Biblio

Since the neural networks are utilized to extract information from an image, Gatys et al. found that they could separate the content and style of images and reconstruct them to another image which called Style Transfer. Moreover, there are many feed-forward neural networks have been suggested to speeding up the original method to make Style Transfer become practical application. However, this takes a price: these feed-forward networks are unchangeable because of their fixed parameters which mean we cannot transfer arbitrary styles but only single one in real-time. Some coordinated approaches have been offered to relieve this dilemma. Such as a style-swap layer and an adaptive normalization layer (AdaIN) and soon. Its worth mentioning that we observed that the AdaIN layer only aligns the means and variance of the content feature maps with those of the style feature maps. Our method is aimed at presenting an operational approach that enables arbitrary style transfer in real-time, reserving more statistical information by histogram matching, providing more reliable texture clarity and more humane user control. We achieve performance more cheerful than existing approaches without adding calculation, complexity. And the speed comparable to the fastest Style Transfer method. Our method provides more flexible user control and trustworthy quality and stability.

The idea behind collective perception is to improve vehicles' awareness about their surroundings. Every vehicle shares information describing its perceived environment by means of V2X communication. Similar to other information shared using V2X communication, collective perception information is potentially safety relevant, which means there is a need to assess the reliability and quality of received information before further processing. Transmitted information may have been forged by attackers or contain inconsistencies e.g. caused by malfunctions. This paper introduces a novel approach for estimating a belief that a pair of entities, e.g. two remote vehicles or the host vehicle and a remote vehicle, within a Vehicular ad hoc Network (VANET) are both trustworthy. The method updates the belief based on the consistency of the data that both entities provide. The evaluation shows that the proposed method is able to identify forged information.

We present ctrlTCP, a method to combine the congestion controls of multiple TCP connections. In contrast to the previous methods such as the Congestion Manager, ctrlTCP can couple all TCP flows that leave one sender, traverse a common bottleneck (e.g., a home user's thin uplink) and arrive at different destinations. Using ns-2 simulations and an implementation in the FreeBSD kernel, we show that our mechanism reduces queuing delay, packet loss, and short flow completion times while enabling precise allocation of the share of the available bandwidth between the connections according to the needs of the applications.

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.

The presence of robots is becoming more apparent as technology progresses and the market focus transitions from smart phones to robotic personal assistants such as those provided by Amazon and Google. The integration of robots in our societies is an inevitable tendency in which robots in many forms and with many functionalities will provide services to humans. This calls for an understanding of how humans are affected by both the presence of and the reliance on robots to perform services for them. In this paper we explore the effects that robots have on humans when a service is performed on request. We expose three groups of human participants to three levels of service completion performed by robots. We record and analyse human perceptions such as propensity to trust, competency, responsiveness, sociability, and team work ability. Our results demonstrate that humans tend to trust robots and are more willing to interact with them when they autonomously recover from failure by requesting help from other robots to fulfil their service. This supports the view that autonomy and team working capabilities must be brought into robots in an effort to strengthen trust in robots performing a service.

The discovery of the interstitial rare earth nitride Sm2Fe17N3 came about seven years after the discovery of the rare earth iron boride Nd2Fe [1],[2], and the nitride initially seemed to offer intrinsic magnetic properties that were superior (Curie temperature TC, magnetocrystalline anisotropy K1 or comparable (spontaneous magnetization Ms to those of its illustrious predecessor. However, the promise of the new material to seriously challenge Nd2Fe14B was not realized. The 2:17 nitride powder, prepared by a low-temperature gas-phase interstitial modification process proved difficult to orient and worse still, it lost its nitrogen at the temperatures needed to process dense sintered magnets [3]. Attempts at explosive compaction [4] or spark sintering [5] failed to yield material with good enough coercivity. Nevertheless, work continued in Japan and China to develop a coercive powder that could be used for bonded magnets. An early realization was zinc-bonded Sm2Fe17N3 [6] with an energy product of 84 kJm3 but a rather low coercivity of 480 kAm-1, less than 5 % of the anisotropy field (Ha = 2K1/Ms ≈ 11 MAm-1). The anisotropy field of Nd2Fe14B is significantly less (6 MAm-1) yet several decades of intensive development have led to higher values and continuous improvements of the coercivity, even in unsubstituted material. Historical experience with permanent magnets shows that a long period of materials development is needed to arrive at the best composition and processing conditions for a microstructure that allows the hard magnetism to be optimized. Coercivities of about 25% of the anisotropy field are ultimately achieved. Here we compare the magnetic properties of melt-spun material. Our Nitroquench powder, produced by Daido Steel, was in the form of flakes 10 μm thick and up to 100 μm in diameter. A crystal-lite size of approximately 15 nm deduced from Scherrer broadening of the X-ray reflections. Composition was checked by EDX microprobe analysis. Hysteresis loops have been measured in applied fields of up to 14 T, at room temperature and at 4 K.The material exhibits a room-temperature coercivity of 690 kAm-1 after saturation in 14 T, with a remanence of 700 kAm-1 in zero applied field and an extrapolated saturation magnetization of 1230 kAm-1. The remanence ratio Mr/Ms of 63% when the remanence is corrected to zero internal field, is reflected in a preferred orientation seen in the X-ray powder diffraction patterns and in 57Fe Mössbauer spectra of magnetized powder. Spectra obtained after saturation of an immobilized powder absorber either in-plane or perpendicular to the sample plane exhibit distinctly different relative intensities of the ΔM=0 absorption lines. The maximum energy product for the powder, assuming full density, is 162 kJm-3. The remanence enhancement is attributed to fact that the nanocrystallite size is not much greater than the exchange length. Melt-spun Sm-Fe-N powder has superior corrosion resistance and thermal stability compared to melt-spun Nd-Fe-B. The Nitroquench powder may be used to produce polymer-bonded magnets with an energy product in excess of 100 kJm-3.

In online payment, customers must transmit their personal and financial information through the website to conclude their purchase and pay the services or items selected. They may face possible fears from online transactions raised by their risk perception about financial or privacy loss. They may have concerns over the payment decision with the possible negative behaviors such as shopping cart abandonment. Therefore, customers have three major players that need to be addressed in online payment: the online seller, the payment page, and their own perception. However, few studies have explored these three players in an online purchasing environment. In this paper, we focus on the customer concerns and examine the antecedents of trust, payment security perception as well as their joint effect on two fundamentally important customers' aspects privacy concerns and financial fear perception. A total of 392 individuals participated in an online survey. The results highlight the importance, of the seller website's components (such as ease of use, security signs, and quality information) and their impact on the perceived payment security as well as their impact on customer's trust and financial fear perception. The objective of our study is to design a research model that explains the factors contributing to an online payment decision.

This Work-In-Progress Paper for the Innovative Practice Category presents a novel experiment in active learning of cybersecurity. We introduced a new workshop on hacking for an existing science-popularizing program at our university. The workshop participants, 28 teenagers, played a cybersecurity game designed for training undergraduates and professionals in penetration testing. Unlike in learning environments that are simplified for young learners, the game features a realistic virtual network infrastructure. This allows exploring security tools in an authentic scenario, which is complemented by a background story. Our research aim is to examine how young players approach using cybersecurity tools by interacting with the professional game. A preliminary analysis of the game session showed several challenges that the workshop participants faced. Nevertheless, they reported learning about security tools and exploits, and 61% of them reported wanting to learn more about cybersecurity after the workshop. Our results support the notion that young learners should be allowed more hands-on experience with security topics, both in formal education and informal extracurricular events.

Testing which is an indispensable part of software engineering is itself an art and science which emerged as a discipline over a period. On testing, if defects are found, testers diminish the risk by providing the awareness of defects and solutions to deal with them before release. If testing does not find any defects, testing assure that under certain conditions the system functions correctly. To guarantee that enough testing has been done, major risk areas need to be tested. We have to identify the risks, analyse and control them. We need to categorize the risk items to decide the extent of testing to be covered. Also, Implementation of structured metrics is lagging in software testing. Efficient metrics are necessary to evaluate, manage the testing process and make testing a part of engineering discipline. This paper proposes the usage of risk based testing using FMEA technique and provides an ideal set of metrics which provides a way to ensure effective testing process.

Crowd sensing is one of the core features of internet of vehicles, the use of internet of vehicles for crowd sensing is conducive to the rational allocation of sensing tasks. This paper mainly studies the problem of task allocation for crowd sensing in internet of vehicles, proposes a trajectory-based task allocation scheme for crowd sensing in internet of vehicles. With limited budget constraints, participants' trajectory is taken as an indicator of the spatiotemporal availability. Based on the solution idea of the minimal-cover problem, select the minimum number of participating vehicles to achieve the coverage of the target area.

As malware family classification methods, image-based classification methods have attracted much attention. Especially, due to the fast classification speed and the high classification accuracy, Convolutional Neural Network (CNN)-based malware family classification methods have been studied. However, previous studies on CNN-based classification methods focused only on improving the classification accuracy of malware families. That is, previous studies did not consider the cases that the accuracy of CNN-based malware classification methods can be decreased under the existence of adversarial attacks. In this paper, we analyze the robustness of various CNN-based malware family classification models under adversarial attacks. While adding imperceptible non-random perturbations to the input image, we measured how the accuracy of the CNN-based malware family classification model can be affected. Also, we showed the influence of three significant visualization parameters(i.e., the size of input image, dimension of input image, and conversion color of a special character)on the accuracy variation under adversarial attacks. From the evaluation results using the Microsoft malware dataset, we showed that even the accuracy over 98% of the CNN-based malware family classification method can be decreased to less than 7%.

IoT devices can be used to fulfil many of our daily tasks. IoT could be wearable devices, home appliances, or even light bulbs. With the introduction of this new technology, however, vulnerabilities are being introduced and can be leveraged or exploited by malicious users. One common vehicle of exploitation is malicious software, or malware. Malware can be extremely harmful and compromise the confidentiality, integrity and availability (CIA triad) of information systems. This paper analyzes the types of malware attacks, introduce some mitigation approaches and discusses future challenges.

In our daily lives, the advances of new technology can be used to sustain the development of people across the globe. Particularly, e-government can be the dynamo of the development for the people. The development of technology and the rapid growth in the use of internet creates a big challenge in the administration in both the public and the private sector. E-government is a vital accomplishment, whereas the security is the main downside which occurs in each e-government process. E-government has to be secure as technology grows and the users have to follow the procedures to make their own transactions safe. This paper tackles the challenges and obstacles to enhance the security of information in e-government. Hence to achieve security data hiding techniques are found to be trustworthy. Reversible data hiding (RDH) is an emerging technique which helps in retaining the quality of the cover image. Hence it is preferred over the traditional data hiding techniques. Modification in the existing algorithm is performed for image encryption scheme and data hiding scheme in order to improve the results. To achieve this secret data is split into 20 parts and data concealing is performed on each part. The data hiding procedure includes embedding of data into least significant nibble of the cover image. The bits are further equally distributed in the cover image to obtain the key security parameters. Hence the obtained results validate that the proposed scheme is better than the existing schemes.

Collective self-adaptive systems (CSAS) are distributed and interconnected systems composed of multiple agents that can perform complex tasks such as environmental data collection, search and rescue operations, and discovery of natural resources. By providing individual agents with learning capabilities, CSAS can cope with challenges related to distributed sensing and decision-making and operate in uncertain environments. This unique characteristic of CSAS enables the collective to exhibit robust behaviour while achieving system-wide and agent-specific goals. Although learning has been explored in many CSAS applications, selecting suitable learning models and techniques remains a significant challenge that is heavily influenced by expert knowledge. We address this gap by performing a multifaceted analysis of existing CSAS with learning capabilities reported in the literature. Based on this analysis, we introduce a 3D framework that illustrates the learning aspects of CSAS considering the dimensions of autonomy, knowledge access, and behaviour, and facilitates the selection of learning techniques and models. Finally, using example applications from this analysis, we derive open challenges and highlight the need for research on collaborative, resilient and privacy-aware mechanisms for CSAS.

Emerging networking technologies, such as cloud and Software Defined Networking, provide flexibility, elasticity and functionalities to change the network configurations over time. However, changes also impose unpredictable security postures at different times, creating difficulties to the security assessment of the network. To address this issue, we propose a stateless security risk assessment, which combines the security posture of network states at different times to provide an overall security overview. This paper describes the methodologies of the stateless security risk assessment. Our approach is applicable to any emerging networking technologies with dynamic changes.

The trending technological research platform is Internet of Things (IoT)and most probably it will stay that way for a while. One of the main application areas of IoT is Cyber-Physical Systems (CPSs), in which IoT devices can be leveraged as actuators and sensors in accordance with the system needs. The public acceptance and adoption of CPS services and applications will create a huge amount of privacy issues related to the processing, storage and disclosure of the user location information. As a remedy, our paper proposes a methodology to provide location privacy for the users of CPSs. Our proposal takes advantage of concepts such as mix-zone, context-awareness, and location-obfuscation. According to our best knowledge, the proposed methodology is the first privacy-preserving location service for CPSs that offers adaptable privacy levels related to the current context of the user.

The increasingly networked human society requires that human beings have a clear understanding and control over the structure, nature and behavior of various social networks. There is a tendency towards privacy in the study of network evolutions because privacy disclosure behavior in the network has gradually developed into a serious concern. For this purpose, we extended information theory and proposed a brand-new concept about so-called “habitual privacy” to quantitatively analyze privacy exposure behavior and facilitate privacy computation. We emphasized that habitual privacy is an inherent property of the user and is correlated with their habitual behaviors. The widely approved driving force in recent modeling complex networks is originated from activity. Thus, we propose the privacy-driven model through synthetically considering the activity impact and habitual privacy underlying the decision process. Privacy-driven model facilitates to more accurately capture highly dynamical network behaviors and figure out the complex evolution process, allowing a profound understanding of the evolution of network driven by privacy.

Generating adversarial samples is gathering much attention as an intuitive approach to evaluate the robustness of learning models. Extensive recent works have demonstrated that numerous advanced image classifiers are defenseless to adversarial perturbations in the white-box setting. However, the white-box setting assumes attackers to have prior knowledge of model parameters, which are generally inaccessible in real world cases. In this paper, we concentrate on the hard-label black-box setting where attackers can only pose queries to probe the model parameters responsible for classifying different images. Therefore, the issue is converted into minimizing non-continuous function. A black-box approach is proposed to address both massive queries and the non-continuous step function problem by applying a combination of a linear fine-grained search, Fibonacci search, and a zeroth order optimization algorithm. However, the input dimension of a image is so high that the estimation of gradient is noisy. Hence, we adopt a zeroth-order optimization method in high dimensions. The approach converts calculation of gradient into a linear regression model and extracts dimensions that are more significant. Experimental results illustrate that our approach can relatively reduce the amount of queries and effectively accelerate convergence of the optimization method.

The demand of highly functioning storage systems has led to the evolution of the filesystems which are capable of successfully and effectively carrying out the data management, configures the new storage hardware, proper backup and recovery as well. The research paper aims to find out which file system can serve better in backup storage (e.g. NAS storage) and compute-intensive systems (e.g. database consolidation in cloud computing). We compare such two most potential opensource filesystem ZFS and BTRFS based on their file I/O performance on a storage pool of flash drives, which are made available over iSCSI (internet) for different record sizes. This paper found that ZFS performed better than BTRFS in this arrangement.

Today, data is all around us, every device that has computation power is generating the data and we can assume that in today's world there is about 2 quintillion bytes of data is been generating every day. as data increase in the database of the world servers so as the risk of data leak where we are talking about unlimited confidential data that is available online but as humans are developing their data online so as its security, today we've got hundreds of way to secure out data but not all are very successful or compatible there the big question arises that how to secure our data to hide our all the confidential information online, in other words one's all life work can be found online which is on risk of leak. all that says is today we have cloud above all of our data centers that stores all the information so that one can access anything from anywhere. in this paper we are introducing a new multimodal biometric system that is possible for the future smartphones to be supported where one can upload, download or modify the files using cloud without worrying about the unauthorized access of any third person as this security authentication uses combination of multiple security system available today that are not easy to breach such as DNA encryption which mostly is based on AES cipher here in this paper there we have designed triple layer of security.

This study aims to detect genuine suspicious events and false alarms within a dataset of network traffic alerts. The rapid development of cloud computing and artificial intelligence-oriented automatic services have enabled a large amount of data and information to be transmitted among network nodes. However, the amount of cyber-threats, cyberattacks, and network intrusions have increased in various domains of network environments. Based on the fields of data science and machine learning, this paper proposes a series of solutions involving data preprocessing, exploratory data analysis, new features creation, features selection, ensemble learning, models construction, and verification to identify suspicious network events. This paper proposes a modified form of stacking ensemble machine learning which includes AdaBoost, Neural Networks, Random Forest, LightGBM, and Extremely Randomised Trees (Extra Trees) to realise a high-performance classification. A suspicious network event recognition dataset for a security operations centre, which uses real network log observations from the 2019 IEEE BigData Cup Challenge, is used as an experimental dataset. This paper investigates the possibility of integrating big-data analytics, machine learning, and data science to improve intelligent cybersecurity.

In the open network environment, the network offensive information is implanted in big data environment, so it is necessary to carry out accurate location marking of network offensive information, to realize network attack detection, and to implement the process of accurate location marking of network offensive information. Combined with big data analysis method, the location of network attack nodes is realized, but when network attacks cross in series, the performance of attack information tagging is not good. An accurate marking technique for network attack information is proposed based on big data fusion tracking recognition. The adaptive learning model combined with big data is used to mark and sample the network attack information, and the feature analysis model of attack information chain is designed by extracting the association rules. This paper classifies the data types of the network attack nodes, and improves the network attack detection ability by the task scheduling method of the network attack information nodes, and realizes the accurate marking of the network attacking information. Simulation results show that the proposed algorithm can effectively improve the accuracy of marking offensive information in open network environment, the efficiency of attack detection and the ability of intrusion prevention is improved, and it has good application value in the field of network security defense.

Mobile Ad hoc Network (MANET) is the collection of mobile devices which could change the locations and configure themselves without a centralized base point. Mobile Ad hoc Networks are vulnerable to attacks due to its dynamic infrastructure. The routing attacks are one among the possible attacks that causes damage to MANET. This paper gives a new method of risk aware response technique which is combined version the Dijkstra's shortest path algorithm and Destination Sequenced Distance Vector (DSDV) algorithm. This can reduce black hole attacks. Dijkstra's algorithm finds the shortest path from the single source to the destination when the edges have positive weights. The DSDV is an improved version of the conventional technique by adding the sequence number and next hop address in each routing table.

This paper mainly focuses on presenting a novel attack detection system to thread out the risk issues in IoT. The presented attack detection system links the interconnection of DevOps as it creates the correlation between development and IT operations. Further, the presented attack detection model ensures the operational security of different applications. In view of this, the implemented system incorporates two main stages named Proposed Feature Extraction process and Classification. The data from every application is processed with the initial stage of feature extraction, which concatenates the statistical and higher-order statistical features. After that, these extracted features are supplied to classification process, where determines the presence of attacks. For this classification purpose, this paper aims to deploy the optimized Deep Belief Network (DBN), where the activation function is tuned optimally. Furthermore, the optimal tuning is done by a renowned meta-heuristic algorithm called Lion Algorithm (LA). Finally, the performance of proposed work is compared and proved over other conventional methods.

The Internet of Things (IoT) relies on sensor devices to measure real-world phenomena in order to provide IoT services. The sensor readings are shared with multiple entities, such as IoT services, other IoT devices or other third parties. The collected data may be sensitive and include personal information. To protect the privacy of the users, the data needs to be protected through an encryption algorithm. For sharing cryptographic cipher-texts with a group of users Attribute-Based Encryption (ABE) is well suited, as it does not require to create group keys. However, the creation of ABE cipher-texts is slow when executed on resource constraint devices, such as IoT sensors. In this paper, we present a modification of an ABE scheme, which not only allows to encrypt data efficiently using ABE but also reduces the size of the cipher-text, that must be transmitted by the sensor. We also show how our modification can be used to realise an instantaneous key revocation mechanism.