Biblio

The precise integrative control between the stereoscopic image and the tactile feedback is very essential in augmented reality[1]-[4]. In order to study this question, this paper will introduce a stereoscopic-imaging and tactile integrative augmented-reality system, and a stereoscopic-imaging and tactile integrative algorithm. The system includes a stereoscopic-imaging part and a string-based tactile part. The integrative algorithm is used to precisely control the interaction between the two parts. The results for testing the system and the algorithm demonstrate the system to be perfect through 5 testers' operation and will be presented in the last part of the paper.

The surveillance video management system is rapidly expanding its scope of application at the request of citizens and the development of related technologies. In addition, as Cloud Computing and 5G network are applied with AI, scope and function of surveillance systems are being enhanced to intelligent CCTV beyond simple monitoring. However, intelligent CCTV systems with Mobile Edge Computing and 5G, which have the risk of privacy infringement. Accordingly, it is necessary to identify various types of security threats that can be occurred through the cloud based surveillance system and to eliminate the risk of privacy and personal information breaches. So, in this paper, we propose a hierarchical cloud based video surveillance system considering security on the 5G Network.

Although deep convolutional neural network (CNN) performs well in many computer vision tasks, its classification mechanism is very vulnerable when it is exposed to the perturbation of adversarial attacks. In this paper, we proposed a new algorithm to generate the substitute model of black-box CNN models by using knowledge distillation. The proposed algorithm distills multiple CNN teacher models to a compact student model as the substitution of other black-box CNN models to be attacked. The black-box adversarial samples can be consequently generated on this substitute model by using various white-box attacking methods. According to our experiments on ResNet18 and DenseNet121, our algorithm boosts the attacking success rate (ASR) by 20% by training the substitute model based on knowledge distillation.

Cyber-security is indispensable as malicious incidents are ubiquitous on the Internet. Intrusion Detection Systems have an important role in detecting and thwarting cyber-attacks. However, it is more effective in a centralized system but not in peer-to-peer networks which makes it subject to central point failure, especially in collaborated intrusion detection systems. The novel blockchain technology assures a fully distributed security system through its powerful features of transparency, immutability, decentralization, and provenance. Therefore, in this paper, we investigate and demonstrate several methods of collaborative intrusion detection with blockchain to analyze the suitability and security of blockchain for collaborative intrusion detection systems. We also studied the difference between the existing means of the integration of intrusion detection systems with blockchain and categorized the major vulnerabilities of blockchain with their potential losses and current enhancements for mitigation.

Computer Networks fights with a continues issues with attackers and intruders. Attacks on distributed systems becoming more powerful and more frequent day by day. Intrusion detection methods are performing main role to detect intruders and attackers. To identify intrusion on computer or computer networks an intrusion detection system methods are used. Network Intrusion Detection System (NIDS) performs an prime role by presenting the network security. It gives a defense layer by monitoring the traffic on network for predefined distrustful activity or pattern. In this paper we have analyze and compare existing signature based and anomaly based algorithm with Openstack private cloud.

Image Encryption is a technique where an algorithm along with a set of characters called key encrypts the data into cipher text. The cipher text can be converted back into plaintext by decryption. This technique is employed for the security of data such that confidentiality, integrity and authenticity of data is maintained. In today's era security of information has become a crucial task, unauthorized access and use of data has become a noticeable issue. To provide the security required, there are several algorithms to suit the purposes. While the use and transferring of images has become easy and faster due to technological advancements especially wireless sensor network, image destruction and illegitimate use has become a potential threat. Different transfer mediums and various uses of images require different and appropriately suiting encryption approaches. Hence, in this paper we discuss the types of image encryption techniques. We have also discussed several encryption algorithms, their advantages and suitability.

Vehicular Ad Hoc Network (VANET) is a promising technology for autonomous driving as it provides many benefits and user conveniences to improve road safety and driving comfort. Sybil attack is one of the most serious threats in vehicular communications because attackers can generate multiple forged identities to disseminate false messages to disrupt safety-related services or misuse the systems. To address this issue, we propose a Sybil attack detection scheme using ADAS (Advanced Driving Assistant System) sensors installed on modern passenger vehicles, without the assistance of trusted third party authorities or infrastructure. Also, a deep learning based object detection technique is used to accurately identify nearby objects for Sybil attack detection and the multi-step verification process minimizes the false positive of the detection.

Multi-agent architectures have been successful in attaining considerable attention among computer security researchers. This is so, because of their demonstrated capabilities such as autonomy, embedded intelligence, learning and self-growing knowledge-base, high scalability, fault tolerance, and automatic parallelism. These characteristics have made this technology a de facto standard for developing ambient security systems to meet the open and dynamic nature of today's online communities. Although multi-agent architectures are increasingly studied in the area of computer security, there is still not enough empirical evidence on their performance in intrusions and attacks detection. The aim of this paper is to report the systematic literature review conducted in the context of specific research questions, to investigate multi-agent IDS architectures to highlight the issues that affect their performance in terms of detection accuracy and response time. We used pertinent keywords and terms to search and retrieve the most recent research studies, on multi-agent IDS architectures, from the major research databases and digital libraries such as SCOPUS, Springer, and IEEE Explore. The search processes resulted in a number of studies; among them, there were journal articles, book chapters, conference papers, dissertations, and theses. The obtained studies were assessed and filtered out, and finally, there were over 71 studies chosen to answer the research questions. The results of this study have shown that multi-agent architectures include several advantages that can help in the development of ambient IDS. However, it has been found that there are several issues in the current multi-agent IDS architectures that may degrade the accuracy and response time of intrusions and attacks detection. Based on our findings, the issues of multi-agent IDS architectures include limitations in the techniques, mechanisms, and schemes used for multi-agent IDS adaptation and learning, load balancing, scalability, fault-tolerance, and high communication overhead. It has also been found that new measurement metrics are required for evaluating multi-agent IDS architectures.

Deep learning classifiers are hugely vulnerable to adversarial examples, and their existence raised cybersecurity concerns in many tasks with an emphasis on malware detection, computer vision, and speech recognition. While there is a considerable effort to investigate attacks and defense strategies in these tasks, only limited work explores the influence of targeted attacks on input data (e.g., images, textual descriptions, audio) used in multimedia recommender systems (MR). In this work, we examine the consequences of applying targeted adversarial attacks against the product images of a visual-based MR. We propose a novel adversarial attack approach, called Target Adversarial Attack against Multimedia Recommender Systems (TAaMR), to investigate the modification of MR behavior when the images of a category of low recommended products (e.g., socks) are perturbed to misclassify the deep neural classifier towards the class of more recommended products (e.g., running shoes) with human-level slight images alterations. We explore the TAaMR approach studying the effect of two targeted adversarial attacks (i.e., FGSM and PGD) against input pictures of two state-of-the-art MR (i.e., VBPR and AMR). Extensive experiments on two real-world recommender fashion datasets confirmed the effectiveness of TAaMR in terms of recommendation lists changing while keeping the original human judgment on the perturbed images.

An Internet of Things (IoT) architecture comprised of cloud, fog and resource constrained IoT end devices. The exponential development of IoT has increased the processing and footprint overhead in IoT end devices. All the components of IoT end devices that establish Chain of Trust (CoT) to ensure security are termed as Trusted Computing Base (TCB). The increased overhead in the IoT end device has increased the demand to increase the size of TCB surface area hence increases complexity of TCB surface area and also the increased the visibility of TCB surface area to the external world made the IoT end devices architecture over-architectured and unsecured. The TCB surface area minimization that has been remained unfocused reduces the complexity of TCB surface area and visibility of TCB components to the external un-trusted world hence ensures security in terms of confidentiality, integrity, authenticity (CIA) at the IoT end devices. The TCB minimization thus will convert the over-architectured IoT end device into lightweight and secured architecture highly desired for resource constrained IoT end devices. In this paper we review the IoT end device architectures proposed in the recent past and concluded that these architectures of resource constrained IoT end devices are over-architectured due to larger TCB and ignored bugs and vulnerabilities in TCB hence un-secured. We propose the Novel levelled architecture with TCB minimization by replacing oversized hypervisor with lightweight Micro(μ)-hypervisor i.e. μ-visor and transferring μ-hypervisor based virtualization over fog node for light weight and secured IoT End device architecture. The bug free TCB components confirm stable CoT for guaranteed CIA resulting into robust Trusted Execution Environment (TEE) hence secured IoT end device architecture. Thus the proposed resulting architecture is secured with minimized SRAM and flash memory combined footprint 39.05% of the total available memory per device. In this paper we review the IoT end device architectures proposed in the recent past and concluded that these architectures of resource constrained IoT end devices are over-architectured due to larger TCB and ignored bugs and vulnerabilities in TCB hence un-secured. We propose the Novel levelled architecture with TCB minimization by replacing oversized hypervisor with lightweight Micro(μ)-hypervisor i.e. μ-visor and transferring μ-hypervisor based virtualization over fog node for light weight and secured IoT End device architecture. The bug free TCB components confirm stable CoT for guaranteed CIA resulting into robust Trusted Execution Environment (TEE) hence secured IoT end device architecture. Thus the proposed resulting architecture is secured with minimized SRAM and flash memory combined footprint 39.05% of the total available memory per device.

Visual Analytics uses data visualization techniques for enabling compelling data analysis by engaging graphical and visual portrayal. In the domain of cybersecurity, convincing visual representation of data enables to ascertain valuable observations that allow the domain experts to construct efficient cyberattack mitigation strategies and provide useful decision support. We present a survey of visual analytics tools and methods in the domain of cybersecurity. We explore and discuss Technical Threat Intelligence visualization tools using the Five Question Method. We conclude the analysis of the works using Moody's Physics of Notations, and VIS4ML ontology as a methodological background of visual analytics process. We summarize our analysis as a high-level model of visual analytics for cybersecurity threat analysis.

We define discounted differential privacy, as an alternative to (conventional) differential privacy, to investigate privacy of evolving datasets, containing time series over an unbounded horizon. We use privacy loss as a measure of the amount of information leaked by the reports at a certain fixed time. We observe that privacy losses are weighted equally across time in the definition of differential privacy, and therefore the magnitude of privacy-preserving additive noise must grow without bound to ensure differential privacy over an infinite horizon. Motivated by the discounted utility theory within the economics literature, we use exponential and hyperbolic discounting of privacy losses across time to relax the definition of differential privacy under continual observations. This implies that privacy losses in distant past are less important than the current ones to an individual. We use discounted differential privacy to investigate privacy of evolving datasets using additive Laplace noise and show that the magnitude of the additive noise can remain bounded under discounted differential privacy. We illustrate the quality of privacy-preserving mechanisms satisfying discounted differential privacy on smart-meter measurement time-series of real households, made publicly available by Ausgrid (an Australian electricity distribution company).

Proliferation of the Internet usage is rapidly increasing, and it is necessary to support the performance requirements for multimedia applications, including lower latency, improved security, faster content retrieval, and adjustability to the traffic load. Nevertheless, because the current Internet architecture is a host-oriented one, it often fails to support the necessary demands such as fast content delivery. A promising networking paradigm called Information-Centric Networking (ICN) focuses on the name of the content itself rather than the location of that content. A distinguished alternative to this ICN concept is Content-Centric Networking (CCN) that exploits more of the performance requirements by using in-network caching and outperforms the current Internet in terms of content transfer time, traffic load control, mobility support, and efficient network management. In this paper, instead of using the saturated method of validating a theory by simulation, we present a testbed-based performance evaluation of the ICN network. We used several new functions of the proposed testbed to improve the performance of the basic CCN. In this paper, we also show that the proposed testbed architecture performs better in terms of content delivery time compared to the basic CCN architecture through graphical results.

Despite the surging development and utilization of IoT devices, the security of IoT devices is still in infancy. The security pitfalls of IoT devices have made it easy for hackers to take over IoT devices and use them for malicious activities like botnet attacks. With the rampant emergence of IoT devices, botnet attacks are surging. The botnet attacks are not only catastrophic for IoT device users but also for the rest of the world. Therefore, there is a crucial need to identify and mitigate the possible threats in IoT devices during the design phase. Threat modelling is a technique that is used to identify the threats in the earlier stages of the system design activity. In this paper, we propose a threat modelling approach to analyze and mitigate the botnet attacks in an IoT smart home use case. The proposed methodology identifies the development-level and application-level threats in smart home use case using STRIDE and VAST threat modelling methods. Moreover, we reticulate the identified threats with botnet attacks. Finally, we propose the mitigation techniques for all identified threats including the botnet threats.

In the era of universal digitization, ensuring network and data security is extremely important. As a part of the Regional Center for Cybersecurity initiative, a three-stage machine learning network security solution is being developed and will be deployed in March 2021. The solution consists of prevention, monitoring, and curation stages. As prevention, we utilize Natural Language Processing to extract the security-related information from social media, news portals, and darknet. A deep learning architecture is used to monitor the network in real-time and detect any abnormal traffic. A combination of regular expressions, pattern recognition, and heuristics are applied to the abuse reports to automatically identify intrusions that passed other security solutions. The lessons learned from the ongoing development of the system, alongside the results, extensive analysis, and discussion is provided. Additionally, a cybersecurity-related corpus is described and published within this work.

Wireless sensor network (WSN) is a wireless self-organizing multi-hop network that can sense and collect the information of the monitored environment through a certain number of sensor nodes which deployed in a certain area and transmit the collected information to the client. Due to the limited power and data capacity stored by the micro sensor, it is weak in communication with other nodes, data storage and calculation, and is very vulnerable to attack and harm to the entire network. The Sybil attack is a classic example. Sybil attack refers to the attack in which malicious nodes forge multiple node identities to participate in network operation. Malicious attackers can forge multiple node identities to participate in data forwarding. So that the data obtained by the end user without any use value. In this paper, we propose a three-tier detection scheme for the Sybil node in the severe environment. Every sensor node will determine whether they are Sybil nodes through the first-level and second-level high-energy node detection. Finally, the base station determines whether the Sybil node detected by the first two stages is true Sybil node. The simulation results show that our proposed scheme significantly improves network lifetime, and effectively improves the accuracy of Sybil node detection.

In the application scenarios of cloud computing, big data, and mobile Internet, covert and diverse network attacks have become a serious problem that threatens the security of enterprises and personal information assets. Abnormal network behaviour detection based on network behaviour characteristics has become an important means to protect network security. However, existing frameworks do not make full use of the characteristics of the correlation between continuous network behaviours, and do not use an algorithm that can process time-series data or process the original feature set into time-series data to match the algorithm. This paper proposes a time-series abnormal network behaviour detection framework. The framework consists of two parts: an algorithm model (DBN-BiGRU) that combines Deep Belief Network (DBN) and Bidirectional Gated Recurrent Unit (BiGRU), and a pre-processing scheme that processes the original feature analysis files of CICIDS2017 to good time-series data. This detection framework uses past and future behaviour information to determine current behaviours, which can improve accuracy, and can adapt to the large amount of existing network traffic and high-dimensional characteristics. Finally, this paper completes the training of the algorithm model and gets the test results. Experimental results show that the prediction accuracy of this framework is as high as 99.82%, which is better than the traditional frameworks that do not use time-series information.

The need for faster and practical cryptography is a research topic for decades. In case of elliptic curve cryptography, which was proposed by Koblitz and Miller in 1985 as a more efficient alternative to RSA, the applications in real life started after 2000s. Today, most of the popular applications and protocols like Whatsapp, Signal, iOS, Android, TLS, SSH, Bitcoin etc. make use of Elliptic curve cryptography. One of the important factor for high performance elliptic curve cryptography is the finite field multiplication. In this paper, we first describe how to choose proper prime fields that makes use of Topelitz-matrices to get faster field multiplication, then we give parameter choice details to select prime fields that supports Toeplitz-matrix vector product operations. Then, we introduce the safe curve selection rationale and discuss about security. We propose new curves, discuss implementation and benchmark results and conclude our work.

By exploiting the simultaneous particle-conserving and particle-nonconserving phonon-photon interactions in an optomechanical array, we find a topologically protected edge state for phonons that can be parametrically amplified when all the bulk states remain stable.

The number of organizations adopting the Data Warehouse (DW) technology along with data analytics in order to improve the effectiveness of their decision-making processes is permanently increasing. Despite the efforts invested, the DW design remains a great challenge research domain. More accurately, the design quality of the DW depends on several aspects; among them, the requirement-gathering phase is a critical and complex task. In this context, we propose a Natural language (NL) NL-template based design approach, which is twofold; firstly, it facilitates the involvement of decision-makers in the early step of the DW design; indeed, using NL is a good and natural means to encourage the decision-makers to express their requirements as query-like English sentences. Secondly, our approach aims to generate a DW multidimensional schema from a set of gathered requirements (as OLAP: On-Line-Analytical-Processing queries, written according to the NL suggested templates). This approach articulates around: (i) two NL-templates for specifying multidimensional components, and (ii) a set of five heuristic rules for extracting the multidimensional concepts from requirements. Really, we are developing a software prototype that accepts the decision-makers' requirements then automatically identifies the multidimensional components of the DW model.

Amount and variety of training data drastically affect the performance of CNNs. Thus, annotation methods are becoming more and more critical to collect data efficiently. In this paper, we propose a simple yet efficient Interactive Self-Annotation framework to cut down both time and human labor cost for video object bounding box annotation. Our method is based on recurrent self-supervised learning and consists of two processes: automatic process and interactive process, where the automatic process aims to build a supported detector to speed up the interactive process. In the Automatic Recurrent Annotation, we let an off-the-shelf detector watch unlabeled videos repeatedly to reinforce itself automatically. At each iteration, we utilize the trained model from the previous iteration to generate better pseudo ground-truth bounding boxes than those at the previous iteration, recurrently improving self-supervised training the detector. In the Interactive Recurrent Annotation, we tackle the human-in-the-loop annotation scenario where the detector receives feedback from the human annotator. To this end, we propose a novel Hierarchical Correction module, where the annotated frame-distance binarizedly decreases at each time step, to utilize the strength of CNN for neighbor frames. Experimental results on various video datasets demonstrate the advantages of the proposed framework in generating high-quality annotations while reducing annotation time and human labor costs.

Reported vulnerabilities have grown significantly over the recent years, with SQL injection (SQLi) being one of the most prominent, especially in web applications. For these, such increase can be explained by the integration of multiple software parts (e.g., various plugins and modules), often developed by different organizations, composing thus web application variants. Machine Learning has the potential to be a great ally on finding vulnerabilities, aiding experts by reducing the search space or even by classifying programs on their own. However, previous work usually does not consider SQLi or utilizes techniques hard to scale. Moreover, there is a clear gap in vulnerability detection with machine learning for PHP, the most popular server-side language for web applications. This paper presents a Deep Learning model able to classify PHP slices as vulnerable (or not) to SQLi. As slices can belong to any variant, we propose the use of an intermediate language to represent the slices and interpret them as text, resorting to well-studied Natural Language Processing (NLP) techniques. Preliminary results of the use of the model show that it can discover SQLi, helping programmers and precluding attacks that would eventually cost a lot to repair.

Converging Information Technology (IT) and Operations Technology (OT) in modern factories remains a challenging task. Several approaches such as Cloud, Fog or Edge computing aim to provide possible solutions for bridging OT that requires strict real-time processing with IT that targets computing functionality. In this context, this paper contributes to ongoing Fog computing research by presenting three industrial use cases with a specific focus on consolidation of functionality. Each use case exemplifies scenarios on how to use the computational resources closer to the edge of the network provided by a Fog Computing Platform (FCP). All use-cases utilize the same proposed FCP, which allows drawing a set of requirements on future FCPs, e.g. hardware, virtualization, security, communication and resource management. The central element of the FCP is the Fog Node (FN), built upon commercial off-the-shelf (COTS) multicore processors (MCPs) and virtualization support. Resource management tools, advanced security features and state of the art communication protocols complete the FCP. The paper concludes by outlining future research challenges by comparing the proposed FCP with the identified requirements.

Fast IPv4 scanning has made sufficient progress in network measurement and security research. However, it is infeasible to perform brute-force scanning of the IPv6 address space. We can find active IPv6 addresses through scanning candidate addresses generated by the state-of-the-art algorithms, whose probing efficiency of active IPv6 addresses, however, is still very low. In this paper, we aim to improve the probing efficiency of IPv6 addresses in two ways. Firstly, we perform a longitudinal active measurement study over four months, building a high-quality dataset called hitlist with more than 1.3 billion IPv6 addresses distributed in 45.2k BGP prefixes. Different from previous work, we probe the announced BGP prefixes using a pattern-based algorithm, which makes our dataset overcome the problems of uneven address distribution and low active rate. Secondly, we propose an efficient address generation algorithm DET, which builds a density space tree to learn high-density address regions of the seed addresses in linear time and improves the probing efficiency of active addresses. On the public hitlist and our hitlist, we compare our algorithm DET against state-of-the-art algorithms and find that DET increases the de-aliased active address ratio by 10%, and active address (including aliased addresses) ratio by 14%, by scanning 50 million addresses.

To enable virtual machines (VMs) with a large amount of memory to be flexibly migrated, split migration has been proposed. It divides a large-memory VM into small pieces and transfers them to multiple hosts. After the migration, the VM runs across those hosts and exchanges memory data between hosts using remote paging. For such a split-memory VM, however, it becomes difficult to securely run intrusion detection systems (IDS) outside the VM using a technique called IDS offloading. This paper proposes VMemTrans to support transparent IDS offloading for split-memory VMs. In VMemTrans, offloaded IDS can monitor a split-memory VM as if that memory were not distributed. To achieve this, VMemTrans enables IDS running in one host to transparently access VM's remote memory. To consider a trade-off, it provides two methods for obtaining memory data from remote hosts: self paging and proxy paging. We have implemented VMemTrans in KVM and compared the execution performance between the two methods.