Biblio

Nowadays, developing an intelligent system for segmenting the moving object from the background is essential task for video surveillance applications. Recently, a deep learning segmentation algorithm composed of encoder CNN, a Feature Pooling Module and a decoder CNN called FgSegNET\_S has been proposed. It is capable to train the model using few training examples. FgSegNET\_S is relying only on the spatial information while it is fundamental to include temporal information to distinguish if an object is moving or not. In this paper, an improved version known as (T\_FgSegNET\_S) is proposed by using the subtracted images from the initial background as input. The proposed approach is trained and evaluated using two publicly available infrared datasets: remote scene infrared videos captured by medium-wave infrared (MWIR) sensors and the Grayscale Thermal Foreground Detection (GTFD) dataset. The performance of network is evaluated using precision, recall, and F-measure metrics. The experiments show improved results, especially when compared to other state-of-the-art methods.

With the advent of the era of big data, information technology has been rapidly developed and the application of computers has been popularized. However, network technology is a double-edged sword. While providing convenience, it also faces many problems, among which there are many hidden dangers of network information security. Based on this, based on the era background of big data, the network information security analysis, explore the main network security problems, and elaborate computer information network security matters needing attention, to strengthen the network security management, and put forward countermeasures, so as to improve the level of network security.

{With the rapid development of cyberspace, cyber security incidents are increasing, and the means and types of network attacks are becoming more and more complex and refined, which brings greater challenges to security risk control. First, the knowledge graph technology is used to construct a cyber security knowledge graph based on ontology to realize multi-source heterogeneous security big data fusion calculation, and accurately express the complex correlation between different security entities. Furthermore, for cyber security risk control, a key node assessment method for security risk diffusion is proposed. From the perspectives of node communication correlation and topological level, the calculation method of node communication importance based on improved PageRank Algorithm and based on the improved K-shell Algorithm calculates the importance of node topology are studied, and then organically combine the two calculation methods to calculate the importance of different nodes in security risk defense. Experiments show that this method can evaluate the importance of nodes more accurately than the PageRank algorithm and the K-shell algorithm.

Based on the success of terrestrial Internet of Things (IoT), research has started on Underwater IoT (UIoT). The UIoT describes global network of connected underwater things that interact with water environment and communicate with terrestrial network through the underwater communication technologies. For UIoT device, it is important to choose the channel before transmission. This paper deals with UIoT communication technologies and ontology based path selection mechanism for UIoT.

Internet of Battlefield Things (IoBTs) are well positioned to take advantage of recent technology trends that have led to the development of low-power neural accelerators and low-cost high-performance sensors. However, a key challenge that needs to be dealt with is that despite all the advancements, edge devices remain resource-constrained, thus prohibiting complex deep neural networks from deploying and deriving actionable insights from various sensors. Furthermore, deploying sophisticated sensors in a distributed manner to improve decision-making also poses an extra challenge of coordinating and exchanging data between the nodes and server. We propose an architecture that abstracts away these thorny deployment considerations from an end-user (such as a commander or warfighter). Our architecture can automatically compile and deploy the inference model into a set of distributed nodes and server while taking into consideration of the resource availability, variation, and uncertainties.

Cloud computing adoption is on the rise. Many organizations have decided to shift their workload to the cloud to benefit from the scalability, resilience, and cost reduction characteristics. Mobile Cloud Computing (MCC) is an emerging computing paradigm that also provides many advantages to mobile users. Mobile devices function on wireless internet connectivity, which entails issues of limited bandwidth and network congestion. Hence, the primary focus of Web applications in MCC is on improving performance by quickly fulfilling customer's requests to improve service satisfaction. This paper investigates a new approach to caching data in these applications using Redis, an in-memory data store, to enhance Quality of Service. We highlight the two implementation approaches of fetching the data of an application either directly from the database or from the cache. Our experimental analysis shows that, based on performance metrics such as response time, throughput, latency, and number of hits, the caching approach achieves better performance by speeding up the data retrieval by up to four times. This improvement is of significant importance in mobile devices considering their limitation of network bandwidth and wireless connectivity.

The purpose of using the Internet has changed from "connecting to computers" to "acquiring content". So, the ICN (Information Centric Network) has been proposed to fit this purpose. In this research, we focus on the architecture of NDN (named data networking). The NFD (NDN forwarding daemon) is a network forwarder that implements the NDN protocol. The ndnSIM is a simulator of NDN. From ndnSIM version 2.8, a part of content store implementation has been removed from the simulator and it becomes to use content store implementation of NFD. In this poster, we select two caching functions, probabilistic caching and expired deletion, which are removed from ndnSIM 2.8 and not included in NFD. We port these functions to NFD for a more practical implementation. Under a certain network, we were able to confirm that previous and ported functions provided equivalent functions. It was also possible to simulate in version ndnSIM 2.8 using the ported functions.

This article analyzes the possibilities of using cognitive approaches in forming expert assessments for solving information security problems. The experts use the contextual approach by A.Yu. Khrennikov’s as a basic model for the mathematical description of the quantum decision-making method. In the cognitive view, expert assessments are proposed to be considered as conditional probabilities with regard to the fulfillment of a set of certain conditions. However, the conditions in this approach are contextual, but not events like in Boolean algebra.

Purpose: Smart apps and wearables devices are an increasingly used way in healthcare to monitor a range of functions associated with certain health conditions. Even if in the present there are some devices and applications developed, there is no sufficient evidence of the use of such wearables devices in the detection of some disorders such as depression. Thus, through this paper, we want to address this need and present a quantitative research to determine the user requirements for developing a smart device that can detect depression. Material and Methods: To determine the user requirements for developing a system to detect depression we developed a questionnaire which was applied to 205 participants. Results and conclusions: Such a system addressed to detect depression is of interest among the respondents. The most essential parameters to be monitored refer to sleep quality, level of stress, circadian rhythm, and heart rate. Also, the developed system should prioritize reliability, privacy, security, and ease of use.

Due to the continuous improvement of deep learning, saliency object detection based on deep learning has been a hot topic in computational vision. The Fully Convolutional Neural Network (FCNS) has become the mainstream method in salient target measurement. In this article, we propose a new end-to-end multi-level feature fusion module(MCFB), success-fully achieving the goal of extracting rich multi-scale global information by integrating semantic and detailed information. In our module, we obtain different levels of feature maps through convolution, and then cascade the different levels of feature maps, fully considering our global information, and get a rough saliency image. We also propose an optimization module upon our base module to further optimize the feature map. To obtain a clearer boundary, we use a self-defined loss function to optimize the learning process, which includes the Intersection-over-Union (IoU) losses, Binary Cross-Entropy (BCE), and Structural Similarity (SSIM). The module can extract global information to a greater extent while obtaining clearer boundaries. Compared with some existing representative methods, this method has achieved good results.

With the development of the Internet of Things, embedded devices have become increasingly frequent in people's daily use. However, with the influx of a huge amount of heterogeneous embedded devices, its security has become an important issue. To face with such problems, remote attestation is undoubtedly a suitable security technology. Nevertheless, traditional remote attestation is limited to verifying the performance of devices as large and heterogeneous devices enter daily life. Therefore, this paper proposes a many-to-one swarm attestation and recovery scheme. Besides, the reputation mechanism and Merkel tree measurement method are introduced to reduce the attestation and recovery time of the scheme, and greatly reducing the energy consumption.

We propose and numerically demonstrate a novel secure key distribution scheme based on the chaos synchronization of two semiconductor lasers (SLs) subject to symmetrical double chaotic injections, which are outputted by two mutually-coupled semiconductor lasers. The results show that high quality chaos synchronization can be observed between two local SLs with suitable injection strength and identical injection time delays for Alice and Bob. On the basis of satisfactory chaos synchronization and a post-processing technology, identical secret keys for Alice and Bob are successfully generated with bit error ratio (BER) below the HD-FEC threshold of $^\textrm-3\$$\$.

Modern smart grid systems are heavily dependent on Information and Communication Technology, and this dependency makes them prone to cyber-attacks. The occurrence of a cyber-attack has increased in recent years resulting in substantial damage to power systems. For a reliable and stable operation, cyber protection, control, and detection techniques are becoming essential. Automated detection of cyberattacks with high accuracy is a challenge. To address this, we propose a two-layer hierarchical machine learning model having an accuracy of 95.44 % to improve the detection of cyberattacks. The first layer of the model is used to distinguish between the two modes of operation - normal state or cyberattack. The second layer is used to classify the state into different types of cyberattacks. The layered approach provides an opportunity for the model to focus its training on the targeted task of the layer, resulting in improvement in model accuracy. To validate the effectiveness of the proposed model, we compared its performance against other recent cyber attack detection models proposed in the literature.

To increase and maintain web application security, developers could use some different methods, one of them is static code analysis. This method could find security vulnerabilities inside a source code without the need of running the program. It could also be automated by using tools, which considered more efficient than manual reviews. One specific method which is commonly used in static code analysis is taint analysis. Taint analysis usually utilizes source code modeling to prepare the code for analysis process to detect any untrusted data flows into security sensitives computations. While this kind of analysis could be very helpful, static code analysis tool for Laravel-based web application is still quite rare, despite its popularity. Therefore, in this research, we want to know how static code (taint) analysis could be utilized to detect security vulnerabilities and how the projects (Laravel-based) should be modeled in order to facilitate this analysis. We then developed a static analysis tool, which models the application’s source code using AST and dictionary to be used as the base of the taint analysis. The tool first parsed the route file of Laravel project to get a list of controller files. Each file in that list would be parsed in order to build the source code representation, before actually being analyzed using taint analysis method. The experiments was done using this tool shows that the tools (with taint analysis) could detect 13 security vulnerabilities from 6 Laravel-based projects with one False Negative. An ineffective sanitizer was the suspected cause of this False Negative. This also shows that proposed modeling technique could be helpful in facilitating taint analysis in Laravel-based projects. For future development and studies, this tool should be tested with more Laravel and even other framework based web application with a wider range of security vulnerabilities.

Current cryptographic solutions used in information technologies today like Transport Layer Security utilize algorithms with underlying computationally difficult problems to solve. With the ongoing research and development of quantum computers, these same computationally difficult problems become solvable within reasonable (polynomial) time. The emergence of large-scale quantum computers would put the integrity and confidentiality of today’s data in jeopardy. It then becomes urgent to develop, implement, and test a new suite of cybersecurity measures against attacks from a quantum computer. This paper explores, understands, and evaluates this new category of cryptosystems as well as the many tradeoffs among them. All the algorithms submitted to the National Institute of Standards and Technology (NIST) for standardization can be categorized into three major categories, each relating to the new underlying hard problem: namely error code correcting, algebraic lattices (including ring learning with errors), and supersingular isogenies. These new mathematical hard problems have shown to be resistant to the same type of quantum attack. Utilizing hardware clock cycle registers, the work sets up the benchmarks of the four Round 3 NIST algorithms in two environments: cloud computing and embedded system. As expected, there are many tradeoffs and advantages in each algorithm for applications. Saber and Kyber are exceedingly fast but have larger ciphertext size for transmission over a wire. McEliece key size and key generation are the largest drawbacks but having the smallest ciphertext size and only slightly decreased performance allow a use case where key reuse is prioritized. NTRU finds a middle ground in these tradeoffs, being better than McEliece performance wise and better than Kyber and Saber in ciphertext size allows for a use case of highly varied environments, which need to value speed and ciphertext size equally. Going forward, the benchmarking system developed could be applied to digital signature, another vital aspect to a cryptosystem.

This paper examines the fast approaching highly secure and content centric data sharing architecture Named Data Networking. The content name plays the key role in NDN. Most of the users are interested only in the content or information and thereby the host centric internet architecture is losing its importance. Different naming conventions and caching strategies used in Named Data Networking based applications have been discussed in this study. The convergence of NDN with the vehicular networks and the ongoing studies in it will make the path to Intelligent Transportation system more optimized and efficient. It describes the future internet and this idea has taken root in most of the upcoming IOT applications which are going to conquer every phase of life. Though it is in its infancy stage of development, NDN will soon take over traditional IP Architecture.

As modern vehicles are complex IoT devices with intelligence capable to connect to an external infrastructure and use Vehicle-to-Everything (V2X) communication, there is a need to secure the communication to avoid being a target for cyber-attacks. Also, the organs of the car (sensors, communication, and control) each could have a vulnerability, that leads to accidents or potential deaths. Manufactures of cars have a huge responsibility to secure the safety of their costumers and should not skip the important security research, instead making sure to implement important security measures, which makes your car less likely to be attacked. This paper covers the relevant attacks and threats to modern vehicles and presents a security analysis with potential countermeasures. We discuss the future of modern and autonomous vehicles and conclude that more countermeasures must be taken to create a future and safe concept.

3D reconstruction has piqued the interest of many disciplines, and many researchers have spent the last decade striving to improve on latest automated three-dimensional reconstruction systems. Three Dimensional models can be utilized to tackle a wide range of visualization problems as well as other activities. In this paper, we have implemented a method of Digital Surface Map (DSM) generation from Aerial images using Conditional Generative Adversarial Networks (c-GAN). We have used Seg-net architecture of Convolutional Neural Network (CNN) to segment the aerial images and then the U-net generator of c-GAN generates final DSM. The dataset we used is ISPRS Potsdam-Vaihingen dataset. We also review different stages if 3D reconstruction and how Deep learning is now being widely used to enhance the process of 3D data generation. We provide binary cross entropy loss function graph to demonstrate stability of GAN and CNN. The purpose of our approach is to solve problem of DSM generation using Deep learning techniques. We put forth our method against other latest methods of DSM generation such as Semi-global Matching (SGM) and infer the pros and cons of our approach. Finally, we suggest improvements in our methods that might be useful in increasing the accuracy.

The Transient Public Key Infrastructure or T-PKI is introduced in this paper that allows a transactional approach to attestation, where a Trusted Platform Module (TPM) can stay anonymous to a verifier. In cloud computing and IoT environments, attestation is a critical step in ensuring that the environment is untampered with. With attestation, the verifier would be able to ascertain information about the TPM (such as location, or other system information) that one may not want to disclose. The addition of the Direct Anonymous Attestation added to TPM 2.0 would potentially solve this problem, but it uses the traditional RSA or ECC based methods. In this paper, a Lattice-based approach is used that is both quantum safe, and not dependent on creating a new key pair in order to increase anonymity.

Deep learning (DL) is an emerging technology that is being used in many areas due to its effectiveness. One of its major applications is attack detection and prevention of backdoor attacks. Sampling-based measurement approaches in the software-defined network of an Internet of Things (IoT) network often result in low accuracy, high overhead, higher memory consumption, and low attack detection. This study aims to review and analyse papers on DL-based network prediction techniques against the problem of Distributed Denial of service attack (DDoS) in a secure software network. Techniques and approaches have been studied, that can effectively predict network traffic and detect DDoS attacks. Based on this review, major components are identified in each work from which an overall system architecture is suggested showing the basic processes needed. Major findings are that the DL is effective against DDoS attacks more than other state of the art approaches.

With the paradigm shift to cloud-based operations, reliable and secure access to and transfer of data between differing security domains has never been more essential. A Cross Domain Solution (CDS) is a guarded interface which serves to execute the secure access and/or transfer of data between isolated and/or differing security domains defined by an administrative security policy. Cross domain security requires trustworthiness at the confluence of the hardware and software components which implement a security policy. Security components must be relied upon to defend against widely encompassing threats – consider insider threats and nation state threat actors which can be both onsite and offsite threat actors – to information assurance. Current implementations of CDS systems use suboptimal Trusted Computing Bases (TCB) without any formal verification proofs, confirming the gap between blind trust and trustworthiness. Moreover, most CDSs are exclusively operated by Department of Defense agencies and are not readily available to the commercial sectors, nor are they available for independent security verification. Still, more CDSs are only usable in physically isolated environments such as Sensitive Compartmented Information Facilities and are inconsistent with the paradigm shift to cloud environments. Our purpose is to address the question of how trustworthiness can be implemented in a remotely deployable CDS that also supports availability and accessibility to all sectors. In this paper, we present a novel CDS system architecture which is the first to use a formally verified TCB. Additionally, our CDS model is the first of its kind to utilize a computation-isolation approach which allows our CDS to be remotely deployable for use in cloud-based solutions.

We suggest using Fully Homomorphic Encryption (FHE) to be used, not only to keep the privacy of information but also, to verify computations with no additional significant overhead, using only part of the variables length for verification. This method supports the addition of encrypted values as well as multiplication of encrypted values by the addition of their logarithmic representations and is based on a separation between hardware functionalities. The computer/server performs blackbox additions and is based on the separation of server/device/hardware, such as the enclave, that may deal with additions of logarithmic values and exponentiation. The main idea is to restrict the computer operations and to use part of the variable for computation verification (computation fingerprints) and the other for the actual calculation. The verification part holds the FHE value, of which the calculated result is known (either due to computing locally once or from previously verified computations) and will be checked against the returned FHE value. We prove that a server with bit computation granularity can return consistent encrypted wrong results even when the public key is not provided. For the case of computer word granularity the verification and the actual calculation parts are separated, the verification part (the consecutive bits from the LSB to the MSB of the variables) is fixed across all input vectors. We also consider the case of Single Instruction Multiple Data (SIMD) where the computation fingerprints index in the input vectors is fixed across all vectors.

This work investigates entropy-based prevention of Distributed Denial-of-Service (DDoS) attacks for Software Defined Networks (SDN). The experiments are conducted on a virtual SDN testbed setup within Mininet, a Linux-based network emulator. An arms race iterates on the SDN testbed between offense, launching botnet-based DDoS attacks with progressive sophistications, and defense who is deploying SDN controls with emerging technologies from other faucets of cyber engineering. The investigation focuses on the transmission control protocol’s synchronize flood attack that exploits vulnerabilities in the three-way TCP handshake protocol, to lock up a host from serving new users.The defensive strategy starts with a common packet filtering-based design from the literature to mitigate attacks. Utilizing machine learning algorithms, SDNs actively monitor all possible traffic as a collective dataset to detect DDoS attacks in real time. A constant upgrade to a stronger defense is necessary, as cyber/network security is an ongoing front where attackers always have the element of surprise. The defense further invests on entropy methods to improve early detection of DDoS attacks within the testbed environment. Entropy allows SDNs to learn the expected normal traffic patterns for a network as a whole using real time mathematical calculations, so that the SDN controllers can sense the distributed attack vectors building up before they overwhelm the network.This work reveals the vulnerabilities of SDNs to stealthy DDoS attacks and demonstrates the effectiveness of deploying entropy in SDN controllers for detection and mitigation purposes. Future work includes provisions to use these entropy detection methods, as part of a larger system, to redirect traffic and protect networks dynamically in real time. Other types of DoS, such as ransomware, will also be considered.

An oblivious signature is a digital signature with some property. The oblivious signature scheme has two parties, the signer and the receiver. First, the receiver can choose one and get one of n valid signatures without knowing the signer’s private key. Second, the signer does not know which signature is chosen by the receiver. In this paper, we propose the oblivious signature which is combined with blind signature and zero-knowledge set membership. The property of blind signature makes sure that the signer does not know the message of the signature by the receiver chosen, on the other hand, the property of the zero-knowledge set membership makes sure that the message of the signature by the receiver chosen is one of the set original messages.

In order to solve the problem of difficult verification of query results in searchable encryption, we used the idea of Shamir-secret sharing, combined with game theory, to construct a randomly verifiable multi-cloud server searchable encryption scheme to achieve the correctness of the query results in the cloud storage environment verify. Firstly, we using the Shamir-secret sharing technology, the encrypted data is stored on each independent server to construct a multi-cloud server model to realize the secure distributed storage and efficient query of data. Secondly, combined with game theory, a game tree of query server and verification server is constructed to ensure honesty while being efficient, and solve the problem of difficulty in returning search results to verify under the multi-cloud server model. Finally, security analysis and experimental analysis show that this solution effectively protects data privacy while significantly reducing retrieval time.