Biblio

In this study, we present the abstracted pedestrian behavior representation and abnormal detection method based on user feedback for pedestrian video surveillance system. Video surveillance data is large in size and difficult to process in real time. To solve this problem, we suggested a method of expressing the pedestrian behavior with abbreviated map. In the video surveillance system, false detection of an abnormal situation becomes a big problem. If surveillance user can guide the false detection case as human in the loop, the surveillance system can learn the case and reduce the false detection error in the future. We suggested user feedback based abnormal pedestrian detection method. By the suggested user feedback algorithm, the false detection can be reduced to less than 0.5%.

Personalized IoT adapt their behavior based on contextual information, such as user behavior and location. Unfortunately, the fact that personalized IoT adapt to user context opens a side-channel that leaks private information about the user. To that end, we start by studying the extent to which a malicious eavesdropper can monitor the actions taken by an IoT system and extract user's private information. In particular, we show two concrete instantiations (in the context of mobile phones and smart homes) of a new category of spyware which we refer to as Context-Aware Adaptation Based Spyware (SpyCon). Experimental evaluations show that the developed SpyCon can predict users' daily behavior with an accuracy of 90.3%. Being a new spyware with no known prior signature or behavior, traditional spyware detection that is based on code signature or system behavior are not adequate to detect SpyCon. We discuss possible detection and mitigation mechanisms that can hinder the effect of SpyCon.

X-ray baggage security screening is a demanding task for aviation and rail transit security; automatic prohibited item detection in X-ray baggage images can help reduce the work of inspectors. However, as many items are placed too close to each other in the baggages, it is difficult to fully trust the detection results of intelligent prohibited item detection algorithms. In this paper, a human-in-the-loop baggage inspection framework is proposed. The proposed framework utilizes the deep-learning-based algorithm for prohibited item detection to find suspicious items in X-ray baggage images, and select manual examination when the detection algorithm cannot determine whether the baggage is dangerous or safe. The advantages of proposed inspection process include: online to capture new sample images for training incrementally prohibited item detection model, and augmented prohibited item detection intelligence with human-computer collaboration. The preliminary experimental results show, human-in-the-loop process by combining cognitive capabilities of human inspector with the intelligent algorithms capabilities, can greatly improve the efficiency of in-baggage security screening.

Digital forensics investigation process begins with the acquisition, investigation until the presentation of investigation findings. Investigators are required to manage bits and pieces of digital evidence in the cloud and to correlate with evidence found in physical machines and network. The process could be made easy with a proper case management tool that is hosted in the web. The challenge of maintaining chain of custody, determining access to evidence, assignment of forensics investigator could be overcome when digital evidence is fully integrated in a single platform. Our proposed case management tool streamlines information gathering and integrates information on different platforms, shares information, tracks cases, and uploads data directly into a database. In addition, the case management tool facilitates the collaboration of investigators through sharing of forensics findings. These features allow case owner or administrator to track and monitor investigation progress in a forensically sound manner.

Cyberattacks on cyber supply chain (CSC) systems and the cascading impacts have brought many challenges and different threat levels with unpredictable consequences. The embedded networks nodes have various loopholes that could be exploited by the threat actors leading to various attacks, risks, and the threat of cascading attacks on the various systems. Key factors such as lack of common ontology vocabulary and semantic interoperability of cyberattack information, inadequate conceptualized ontology learning and hierarchical approach to representing the relationships in the CSC security domain has led to explicit knowledge representation. This paper explores cyberattack ontology learning to describe security concepts, properties and the relationships required to model security goal. Cyberattack ontology provides a semantic mapping between different organizational and vendor security goals has been inherently challenging. The contributions of this paper are threefold. First, we consider CSC security modelling such as goal, actor, attack, TTP, and requirements using semantic rules for logical representation. Secondly, we model a cyberattack ontology for semantic mapping and knowledge representation. Finally, we discuss concepts for threat intelligence and knowledge reuse. The results show that the cyberattack ontology concepts could be used to improve CSC security.

Return address authentication mechanisms protect return addresses by calculating and checking their message authentication codes (MACs) at runtime. However, these works only provide empirical analysis on their security, and it is still unclear whether the attacker can bypass these defenses by launching reuse attacks.In this paper, we present a solution to quantitatively analysis the security of return address authentication mechanisms against reuse attacks. Our solution utilizes some libc functions that could leakage data from memory. First, we perform reaching definition analysis to identify the source of parameters of these functions. Then we infer how many MACs could be observed at runtime by modifying these parameters. Afterward, we select the gadgets that could be exploited by reusing these observed MACs. Finally, we stitch desired gadget to craft attacks. We evaluated our solution on 5 real-word applications and successfully crafted reuse attacks on 3 of them. We find that the larger an application is, the more libc functions and gadgets can be found and reused, and furthermore, the more likely the attack is successfully crafted.

The reuse of libraries in software development has become prevalent for improving development efficiency and software quality. However, security vulnerabilities of reused libraries propagated through software project dependency pose a severe security threat, but they have not yet been well studied. In this paper, we present the first large-scale empirical study of project dependencies with respect to security vulnerabilities. We developed PDGraph, an innovative approach for analyzing publicly known security vulnerabilities among numerous project dependencies, which provides a new perspective for assessing security risks in the wild. As a large-scale software collection in dependency, we find 337,415 projects and 1,385,338 dependency relations. In particular, PDGraph generates a project dependency graph, where each node is a project, and each edge indicates a dependency relationship. We conducted experiments to validate the efficacy of PDGraph and characterized its features for security analysis. We revealed that 1,014 projects have publicly disclosed vulnerabilities, and more than 67,806 projects are directly dependent on them. Among these, 42,441 projects still manifest 67,581 insecure dependency relationships, indicating that they are built on vulnerable versions of reused libraries even though their vulnerabilities are publicly known. During our eight-month observation period, only 1,266 insecure edges were fixed, and corresponding vulnerable libraries were updated to secure versions. Furthermore, we uncovered four underlying dependency risks that can significantly reduce the difficulty of compromising systems. We conducted a quantitative analysis of dependency risks on the PDGraph.

Both the Internet of Things (IoT) and Information Centric Networking (ICN) have gathered a lot of attention from both research and industry in recent years. While ICN has proved to be beneficial in many situations, it is not widely deployed outside research projects, also not addressing needs of IoT application programming interfaces (APIs). On the other hand, today's IoT solutions are built on top of the host-centric communication model associated with the usage of the Internet Protocol (IP). This paper contributes a discussion on the need of an integration of a specific form of IoT APIs, namely WebSocket based streaming APIs, into an ICN. Furthermore, different access models are discussed and requirements are derived from real world APIs. Finally, the design of an ICN-style extension is presented using one of the examined APIs.

Information-Centric Networking (ICN) is among the promising architecture that can drive the need and versatility towards the future generation (xG) needs. In the future, support for network communication relies on the area of telemedicine, autonomous vehicles, and disaster recovery. In the disaster recovery case, there is a high possibility where the communication path is severed. Multicast communication and DTN-friendly route algorithm are becoming suitable options to send a packet message to get a faster response and to see any of the nodes available for service, this approach could give burden to the core network. Also, during disaster cases, many people would like to communicate, receive help, and find family members. Flooding the already disturbed/severed network will further reduce communication performance efficiency even further. Thus, this study takes into consideration prioritization factors to allow networks to process and delivering priority content. For this purpose, the proposed technique introduces the Routable Prefix Identifier (RP-ID) that takes into account the prioritization factor to enable optimization in Delay Tolerant ICN communication.

IoT applications are changing our daily lives. These innovative applications are supported by new communication technologies and protocols. Particularly, the information-centric network (ICN) paradigm is well suited for many IoT application scenarios that involve large-scale wireless sensor networks (WSNs). Even though the ICN approach can significantly reduce the network traffic by optimizing the process of information recovery from network nodes, it is also possible to apply data aggregation strategies. This paper proposes an unsupervised machine learning-based data aggregation strategy for multi-hop information-centric WSNs. The results show that the proposed algorithm can significantly reduce the ICN data traffic while having reduced information degradation.

This paper integrates Software-Defined Networking (SDN) and Information -Centric Networking (ICN) framework to enable low latency-based stateful routing and caching management by leveraging a novel forwarding and caching strategy. The framework is implemented in a clean- slate environment that does not rely on the TCP/IP principle. It utilizes Pending Interest Tables (PIT) instead of Forwarding Information Base (FIB) to perform data dissemination among peers in the proposed IC-SDN framework. As a result, all data exchanged and cached in the system are organized in chunks with the same interest resulting in reduced packet overhead costs. Additionally, we propose an efficient caching strategy that leverages in- network caching and naming of contents through an IC-SDN controller to support off- path caching. The testbed evaluation shows that the proposed IC-SDN implementation achieves an increased throughput and reduced latency compared to the traditional information-centric environment, especially in the high load scenarios.

Congestion control is one of the essential keys to enhance network efficiency so that the network can perform well even in the case of packet drop. This problem is even more challenging in Information-Centric Networking (ICN), a typical Future Internet design, which employs the packet flooding policy for forwarding the information. To diminish the high traffic load due to the huge number of packets in the era of the Internet of Things (IoT), this paper proposes an effective caching and forwarding algorithm to diminish the congestion rate of the IoT wireless sensor in ICN. The proposed network system utilizes accumulative popularity-based delay transmission time for forwarding strategy and includes the consecutive chunks-based segment caching scheme. The evaluation results using ndnSIM, a widely-used ns-3 based ICN simulator, demonstrated that the proposed system can achieve less interest packet drop rate, more cache hit rate, and higher network throughput, compared to the relevant ICN-based benchmarks. These results prove that the proposed ICN design can achieve higher network efficiency with a lower congestion rate than that of the other related ICN systems using IoT sensors.

The Internet of Things (IoT) paradigm, with its highly distributed and interconnected architecture, is gaining ground in Industry 4.0 and in critical infrastructures like the eHealth sector, the Smart Grid, Intelligent Power Plants and Smart Mobility. In these critical sectors, the preservation of metrological characteristics and their traceability is a strong legal requirement, just like cyber-security, since it offers the ground for liability. Any vulnerability in the system in which the metrological network is embedded can endanger human lives, the environment or entire economies. This paper presents a framework comprised of a methodology and some tools for the governance of the metrological chain. The proposed methodology combines the RAMI 4.0 model, which is a Reference Architecture used in the field of Industrial Internet of Things (IIoT), with the the Reference Model for Information Assurance & Security (RMIAS), a framework employed to guarantee information assurance and security, merging them with the well established paradigms to preserve calibration and referability of metrological instruments. Thus, metrological traceability and cyber-security are taken into account straight from design time, providing a conceptual space to achieve security by design and to support the maintenance of the metrological chain over the entire system lifecycle. The framework lends itself to be completely automatized with Model Checking to support automatic detection of non conformity and anomalies at run time.

Recent advancements of spatial audio technologies to enhance human’s emotional and immersive experiences are gathering attention. Many studies are clarifying the neural mechanisms of acoustic spatial perception; however, they are limited to the evaluation of mechanisms using basic sound stimuli. Therefore, it remains challenging to evaluate the experience of actual music contents and to verify the effects of higher-order neurophysiological responses including a sense of immersive and realistic experience. To investigate the effects of spatial audio experience, we verified the psychophysiological responses of immersive spatial audio experience using sound field synthesis (SFS) technology. Specifically, we evaluated alpha power as the central nervous system activity, heart rate/heart rate variability and skin conductance as the autonomic nervous system activity during an acoustic experience of an actual music content by comparing stereo and SFS conditions. As a result, statistically significant differences (p \textbackslashtextless 0.05) were detected in the changes in alpha wave power, high frequency wave power of heart rate variability (HF), and skin conductance level (SCL) among the conditions. The results of the SFS condition showed enhanced the changes in alpha power in the frontal and parietal regions, suggesting enhancement of emotional experience. The results of the SFS condition also suggested that close objects are grouped and perceived on the basis of the spatial proximity of sounds in the presence of multiple sound sources. It is demonstrating that the potential use of SFS technology can enhance emotional and immersive experiences by spatial acoustic expression.

Since a training system using VR can reproduce an actual training environment, training systems have been studied in commercial fields such as medical care and construction. This immersive experience in a virtual space can have a great effect on learning a second language. In this paper, we propose an immersive learning system that learns phrases used in the customer service industry in the customer service experience. We asked the subjects to experience the system, measured the effects of learning, and evaluated the system. Evaluating the learning effect of phrases used in customer service English on 8 students, all student achieved good learning results. Besides, to evaluate the usability of the system, the VR system was evaluated by performing SSQ to measure VR sickness shows this system doesn't cause virtual sickness, SUS to measure usability shows this system evaluation is higher than average system, and IPQ to measure presence in an immersive space shows this system gives average virtual reality experience.

In the theoretical part of the paper, the scope of application of pseudorandom numbers and methods of their generation, as well as methods of statistical testing of pseudorandom sequences (PS) are considered. In the practical part of the work, the quality of PS obtained by Mersenne Twister [1] generator and the cryptographic generator of the RNGCryptoServiceProvider class of the. NET platform is evaluated. Based on the conducted research, the results of testing are obtained, which show that the quality of pseudorandom sequences generated by the cryptographic random number generator is higher than PS generated by Mersenne Twister. Additionally, based on statistical analysis by NIST and TestU01, a study is conducted in an attempt to establish the statistical indistinguishability of sets of empty- and stegocontainers created using a two-dimensional associative masking mechanism [2-4] based on a gamma of at least 500 KB in length. Research work was carried out under the guidance of R.F. Gibadullin, Associate Professor of the Department of Computer Systems of Kazan National Research Technical University named after A.N.Tupolev-KAI.

We tackle the problem where a server owns a trained Machine Learning (ML) model and a client/user has an unclassified query that he wishes to classify in secure and private fashion using the server’s model. During the process the server learns nothing, while the user learns only his final classification and nothing else. Since several ML classification algorithms, such as deep neural networks, support vector machines-SVM (and hyperplane decisions in general), Logistic Regression, Naïve Bayes, etc., can be expressed in terms of matrix operations, initially we propose novel secure matrix operations as our building blocks. On top of them we build our secure and private ML classification algorithms under strict security and privacy requirements. As our underlying cryptographic primitives are shown to be resilient to quantum computer attacks, our algorithms are also suitable for the post-quantum world. Our theoretical analysis and extensive experimental evaluations show that our secure matrix operations, hence our secure ML algorithms build on top of them as well, outperform the state of the art schemes in terms of computation and communication costs. This makes our algorithms suitable for devices with limited resources that are often found in Industrial IoT (Internet of Things)

Quantum entanglement gives rise to a range of non-classical effects, which are extensively exploited in quantum computing and quantum communication. Therefore, detection and quantification of entanglement as well as preparation of highly entangled quantum states remain the fundamental objectives in these fields. Much attention has been devoted to the studies of graph states, which play a role of a central resource in quantum error correction, quantum cryptography and practical quantum metrology in the presence of noise.We examine multi-qubit graph states generated by the action of controlled phase shift operators on a separable quantum state of a system, in which all the qubits are in arbitrary identical states. Analytical expression is obtained for the geometric measure of entanglement of a qubit with other qubits in graph states represented by arbitrary graphs. We conclude that this quantity depends on the degree of the vertex corresponding to the qubit, the absolute values of the parameter of the phase shift gate and the parameter of the initial state the gate is acting on. Moreover, the geometric measure of entanglement of certain types of graph states is quantified on the IBM’s quantum computer ibmq\_athens based on the measurements of the mean spin. Namely, we consider states associated with the native connectivity of ibmq\_athens, the claw and the complete graphs. Appropriate protocols are proposed to prepare these states on the quantum computer. The results of quantum computations verify our theoretical findings [1].

A new method for judging degree sequence is shown by means of perfect ice-flower systems made by operators - stars (particular complete bipartite graphs), and moreover this method can be used to build up degree sequences and perfect ice-flower systems. Graphic lattice, graph-graphic lattice, caterpillar-graphic lattice and topological coding lattice are defined. We establish some connections between traditional lattices and graphic lattices trying to provide new techniques for Lattice-based cryptosystem and post-quantum cryptography, and trying to enrich the theoretical knowledge of topological coding.

Recently, most of the processes are being computerized, due to the development of information and communication technology. In proportion to this, cyber-attacks are also increasing, and state-sponsored cyber-attacks are becoming a great threat to the country. These attacks are often composed of stages and proceed step-by-step, so for defense, it is necessary to predict the next action and perform appropriate mitigation. To this end, the paper proposes a mitigation-based performance evaluation method. We developed the new true positive which can have a value between 0 and 1 according to the mitigation. The experiment result and case studies show that the proposed method can effectively measure forecasting results under cyber security defense system.

We propose a localized oscillation amplitude monitoring (OAM) method for the mitigation of cyber threats directed at the wide area control (WAC) system used to coordinate control of Flexible AC Transmission Systems (FACTS) for power oscillation damping (POD) of active power flow on inter-area tie lines. The method involves monitoring the inter-area tie line active power oscillation amplitude over a sliding window. We use system instability - inferred from oscillation amplitudes growing instead of damping - as evidence of an indication of a malfunction in the WAC of FACTS, possibly indicative of a cyber attack. Monitoring the presence of such a growth allows us to determine whether any destabilizing behaviors appear after the WAC system engages to control the POD. If the WAC signal increases the oscillation amplitude over time, thereby diminishing the POD performance, the FACTS falls back to POD using local measurements. The proposed method does not require an expansive system-wide view of the network. We simulate replay, control integrity, and timing attacks for a test system and present results that demonstrate the performance of the OAM method for mitigation.

Security and privacy threat modeling is commonly applied to systematically identify and address design-level security and privacy concerns in the early stages of architecture and design. Identifying and resolving these threats should remain a continuous concern during the development lifecycle. Especially with contemporary agile development practices, a single-shot upfront analysis becomes quickly outdated. Despite it being explicitly recommended by experts, existing threat modeling approaches focus largely on early development phases and provide limited support during later implementation phases.In this paper, we present an integrated threat analysis toolchain to support automated, continuous threat elicitation, assessment, and mitigation as part of a continuous integration pipeline in the GitLab DevOps platform. This type of automation allows for continuous attention to security and privacy threats during development at the level of individual commits, supports monitoring and managing the progress in addressing security and privacy threats over time, and enables more advanced and fine-grained analyses such as assessing the impact of proposed changes in different code branches or merge/pull requests by analyzing the changes to the threat model.

We propose a stability monitoring approach for the mitigation of cyber threats directed at the wide area control (WAC) system used for coordinated control of Flexible AC Transmission Systems (FACTS) used for power oscillation damping (POD) of active power flow on inter-area tie lines. The approach involves monitoring the modes of the active power oscillation on an inter-area tie line using the Matrix Pencil (MP) method. We use the stability characteristics of the observed modes as a proxy for the presence of destabilizing cyber threats. We monitor the system modes to determine whether any destabilizing modes appear after the WAC system engages to control the POD. If the WAC signal exacerbates the POD performance, the FACTS falls back to POD using local measurements. The proposed approach does not require an expansive system-wide view of the network. We simulate replay, control integrity, and timing attacks for a test system and present results that demonstrate the performance of the SM approach for mitigation.

Fake news, frequently making use of tampered photos, has currently emerged as a global epidemic, mainly due to the widespread use of social media as a present alternative to traditional news outlets. This development is often due to the swiftly declining price of advanced cameras and phones, which prompts the simple making of computerized pictures. The accessibility and usability of picture-altering softwares make picture-altering or controlling processes significantly simple, regardless of whether it is for the blameless or malicious plan. Various investigations have been utilized around to distinguish this sort of controlled media to deal with this issue. This paper proposes an efficient technique of copy-move forgery detection using the deep learning method. Two deep learning models such as Buster Net and VGG with FPN are used here to detect copy move forgery in digital images. The two models' performance is evaluated using the CoMoFoD dataset. The experimental result shows that VGG with FPN outperforms the Buster Net model for detecting forgery in images with an accuracy of 99.8% whereas the accuracy for the Buster Net model is 96.9%.

A tamper-resistant logical operation method based on integrated nanophotonics is proposed focusing on electromagnetic side-channel attacks. In the proposed method, only the phase of each optical signal is modulated depending on its logical state, which keeps the power of optical signals in optical logic circuits constant. This provides logic-gate-level tamper resistance which is difficult to achieve with CMOS circuits. An optical implementation method based on electronically-controlled phase shifters is then proposed. The electrical part of proposed circuits achieves 300 times less instantaneous current change, which is proportional to intensity of the leaked electromagnetic wave, than a CMOS logic gate.