Biblio

SM9 is an identity-based cryptography algorithm published by the State Cryptography Administration of China. With SM9, a user's private key for signing is generated by a central system called key generation center (KGC). When the owner of the private key wants to shirk responsibility by denying that the signature was generated by himself, he can claim that the operator of KGC forged the signature using the generated private key. To address this issue, in this paper, two schemes of SM9 digital signature with non-repudiation are proposed. With the proposed schemes, the user's private key for signing is collaboratively generated by two separate components, one of which is deployed in the private key service provider's site while the other is deployed in the user's site. The private key can only be calculated in the user's site with the help of homomorphic encryption. Therefore, only the user can obtain the private key and he cannot deny that the signature was generated by himself. The proposed schemes can achieve the non-repudiation of SM9 digital signature.

In this paper, a new intrusion detection mechanism is proposed based on Trust and Packet Loss Rate at Sensor Node in WSNs. To find the true malicious nodes, the proposed mechanism performs a deep analysis on the packet loss. Two independent metrics such as buffer capacity metric and residual energy metric are considered for packet loss rate evaluation. Further, the trust evaluation also considers the basic communication interactions between sensor nodes. Based on these three metrics, a new composite metric called Packet Forwarding Probability (PFP) is derived through which the malicious nodes are identified. Simulation experiments are conducted over the proposed mechanism and the performance is evaluated through False Positive Rate (FPR) and Malicious Detection Rate (MDR). The results declare that the proposed mechanism achieves a better performance compared to the conventional approaches.

Steganography has made great progress over the past few years due to the advancement of deep convolutional neural networks (DCNN), which has caused severe problems in the network security field. Ensuring the accuracy of steganalysis is becoming increasingly difficult. In this paper, we designed a two-channel generative adversarial network (TGAN), inspired by the idea of adversarial training that is based on our previous work. The TGAN consisted of three parts: The first hiding network had two input channels and one output channel. For the second extraction network, the input was a hidden image embedded with the secret image. The third detecting network had two input channels and one output channel. Experimental results on two independent image data sets showed that the proposed TGAN performed well and had better detecting capability compared to other algorithms, thus having important theoretical significance and engineering value.

Coreference resolution is an important task in the field of natural language processing. Most existing methods usually utilize word-level representations, ignoring massive information from the texts. To address this issue, we investigate how to improve Chinese coreference resolution by using span-level semantic representations. Specifically, we propose a model which acquires word and character representations through pre-trained Skip-Gram embeddings and pre-trained BERT, then explicitly leverages span-level information by performing bidirectional LSTMs among above representations. Experiments on CoNLL-2012 shared task have demonstrated that the proposed model achieves 62.95% F1-score, outperforming our baseline methods.

IoT devices provide a rich data source that is not available in the past, which is valuable for a wide range of intelligence applications, especially deep neural network (DNN) applications that are data-thirsty. An established DNN model provides useful analysis results that can improve the operation of IoT systems in turn. The progress in distributed/federated DNN training further unleashes the potential of integration of IoT and intelligence applications. When a large number of IoT devices are deployed in different physical locations, distributed training allows training modules to be deployed to multiple edge data centers that are close to the IoT devices to reduce the latency and movement of large amounts of data. In practice, these IoT devices and edge data centers are usually owned and managed by different parties, who do not fully trust each other or have conflicting interests. It is hard to coordinate them to provide end-to-end integrity protection of the DNN construction and application with classical security enhancement tools. For example, one party may share an incomplete data set with others, or contribute a modified sub DNN model to manipulate the aggregated model and affect the decision-making process. To mitigate this risk, we propose a novel blockchain based end-to-end integrity protection scheme for DNN applications integrated with an IoT system in the edge computing environment. The protection system leverages a set of cryptography primitives to build a blockchain adapted for edge computing that is scalable to handle a large number of IoT devices. The customized blockchain is integrated with a distributed/federated DNN to offer integrity and authenticity protection services.

With the introduction of new technologies such as cloud computing and big data, the security issues of industrial cyber-physical systems (ICPSs) have become more complicated. Meanwhile, a lot of current security research lacks adaptation to industrial system upgrades. In this paper, an edge-cloud synergy framework for security decision-making is proposed, which takes advantage of the huge convenience and advantages brought by cloud computing and edge computing, and can make security decisions on a global perspective. Under this framework, a combination of Bayesian network-based risk assessment and stochastic game model-based security decision-making is proposed to generate an optimal defense strategy to minimize system losses. This method trains models in the clouds and infers at the edge computing nodes to achieve rapid defense strategy generation. Finally, a case study on the hardware-in-the-loop simulation platform proves the feasibility of the approach.

The Internet of Things (IoT) as a demanding technology require the best features of information security for effective development of the IoT based smart city and technological activity. There are huge number of recent security threats searching for some loopholes which are ready to exploit any network. Against the back-drop of recent rapidly growing technological advancement of IoT, security-threats have become a critical challenge which demand responsive and continuous action. As privacy and security exhibit an ever-present flourishing issue, so loopholes detection and analysis are indispensable process in the network. This paper presents Block chain based security analysis of data generated from IoT devices to prevent malicious attacks and intrusion in the IoT network.

Nowadays, Internet Of Everything (IOE) has demanded for a wide range of applications areas. IOE is started to replaces an Internet Of things (IOT). IOE is a combination of massive number of computing elements and sensors, people, processes and data through the Internet infrastructure. Device to Device communication and interfacing of Wireless Sensor network with IOE can makes any system as a Smart System. With the increased the use of Internet and Internet connected devices has opportunities for hackers to launch attacks on unprecedented scale and impact. The IOE can serve the varied security in the various sectors like manufacturing, agriculture, smart grid, payments, IoT gateways, healthcare and industrial ecosystems. To secure connections among people, process, data, and things, is a major challenge in Internet of Everything.. This paper focuses on various security Issues and Authentication Schemes in the IOE systems.

Intelligenta transportation is an important part of urban development. The core of realizing intelligent transportation is to master the urban road condition. This system processes the video of dashcam based on the Panoptic Segmentation network and adds a tracking module based on the comparison of front and rear frames and KM algorithm. The system mainly includes the following parts: embedded device, Panoptic Feature Pyramid Network, cloud server and Web site.

Attribute reduction algorithms in rough set theory can be classified into two groups, i.e. heuristics algorithms and computational intelligence algorithms. The former has good search efficiency but it can not find the global optimal reduction. Conversely, the latter is possible to find global optimal reduction but usually suffers from premature convergence. To address this problem, this paper proposes a two-stage algorithm for finding high quality reduction. In first stage, a classical differential evolution algorithm is employed to rapidly approach the optimal solution. When the premature convergence is detected, a local search algorithm which is intuitively a forward-backward heuristics is launched to improve the quality of the reduction. Experiments were performed on six UCI data sets and the results show that the proposed algorithm can outperform the existing computational intelligence algorithms.

the significant area in the growing field of internet security and IIoT connectivity is the way that forensic investigators will conduct investigation process with devices connected to industrial sensors. This part of process is known as IIoT digital forensics and investigation. The main research on IIoT digital forensic investigation has been done, but the current investigation process has revealed and identified major security issues need to be addressed. In parallel, major security issues faced by traditional forensic investigators dealing with IIoT connectivity and data security. This paper address the issues of the challenges and major security issues identified by review conducted in the prospective and emphasizes on the aforementioned security and challenges.

Big data provides a way to handle and analyze large amount of data or complex set. It provides a systematic extraction also. In this paper a hybrid security analysis based on intelligent adaptive learning in big data has been discussed with the current trends. This paper also explores the possibility of cloud computing collaboration with big data. The advantages along with the impact for the overall platform evaluation has been discussed with the traditional trends. It has been useful in the analysis and the exploration of future research. This discussion also covers the computational variability and the connotation in terms of data reliability, availability and management in big data with data security aspects.

This research aims to implement different modular exponentiation algorithms and evaluate the average complexity and compare it to the theoretical value. We use the library GMP to implement seven modular exponentiation algorithms. They are Left-to-right Square and Multiply, Right-to-left Square and Multiply, Left-to-right Signed Digit Square, and Multiply Left-to-right Square and Multiply Always Right-to-left Square and Multiply Always, Montgomery Ladder and Joye Ladder. For some exponent bit length, we choose 1024 bits and execute each algorithm on many exponent values and count the average numbers of squares and the average number of multiplications. Whenever relevant, our programs will check the consistency relations between the registers at the end of the exponentiation.

The current trend of IoT user is toward the use of services and data externally due to voluminous processing, which demands resourceful machines. Instead of relying on the cloud of poor connectivity or a limited bandwidth, the IoT user prefers to use a cloudlet-based fog computing. However, the choice of cloudlet is solely dependent on its trust and reliability. In practice, even though a cloudlet possesses a required trusted platform module (TPM), we argue that the presence of a TPM is not enough to make the cloudlet trustworthy as the TPM supports only the primitive security of the bootstrap. Besides uncertainty in security, other uncertain conditions of the network (e.g. network bandwidth, latency and expectation time to complete a service request for cloud-based services) may also prevail for the cloudlets. Therefore, in order to evaluate the trust value of multiple cloudlets under uncertainty, this paper broadly proposes the empirical process for evaluation of trust. This will be followed by a measure of trust-based reputation of cloudlets through computational intelligence such as fuzzy logic and ant colony optimization (ACO). In the process, fuzzy logic-based inference and membership evaluation of trust are presented. In addition, ACO and its pheromone communication across different colonies are being modeled with multiple cloudlets. Finally, a measure of affinity or popular trust and reputation of the cloudlets is also proposed. Together with the context of application under multiple cloudlets, the computationally intelligent approaches have been investigated in terms of performance. Hence the contribution is subjected towards building a trusted cloudlet-based fog platform.

In the deep learning tasks, we can design different network models to address different tasks (classification, detection, segmentation). But traditional deep learning networks simply increase the depth and breadth of the network. This leads to a higher complexity of the model. We propose Adaptively Weighted Channel Feature Network of Mixed Convolution Kernel(SKENet). SKENet extract features from different kernels, then mixed those features by elementwise, lastly do sigmoid operator on channel features to get adaptive weightings. We did a simple classification test on the CIFAR10 amd CIFAR100 dataset. The results show that SKENet can achieve a better result in a shorter time. After that, we did an object detection experiment on the VOC dataset. The experimental results show that SKENet is far ahead of the SKNet[20] in terms of speed and accuracy.

To solve the problems of weak linkage ability and low intellectualization of strategy allocation in existing network security devices, a new method of cognitive linkage of network security equipment is proposed by learning from human brain. Firstly, the basic connotation and cognitive cycle of cognitive linkage are expounded. Secondly, the main functions of cognitive linkage are clarified. Finally, the cognitive linkage system model is constructed, and the information process flow of cognitive linkage is described. Cognitive linkage of network security equipment provides a new way to effectively enhance the overall protection capability of network security equipment.

Biometrics manages the computerized acknowledgment of people dependent on natural and social attributes. The example acknowledgment framework perceives an individual by deciding the credibility of a particular conduct normal for person. The primary rule of biometric framework is recognizable proof and check. A biometric confirmation framework use fingerprints, face, hand geometry, iris, and voice, mark, and keystroke elements of a person to recognize an individual or to check a guaranteed character. Biometrics authentication is a form of identification and access control process which identify individuals in packs that are under reconnaissance. Biometric security system increase in the overall security and individuals no longer have to deal with lost ID Cards or forgotten passwords. It helps much organization to see everyone is at a certain time when something might have happened that needs reviewed. The current issues in biometric system with individuals and many organization facing are personal privacy, expensive, data's may be stolen.

As nuclear power plant Instrumentation and Control(I&C) systems have turned into digital systems, the possibility of cyber-attacks has increased. To protect the nuclear power plant from cyber-attacks, digital assets are classified and managed as critical digital assets which have safety, security and emergency preparedness functions. However, critical digital assets represent 70-80% of total digital assets, and applying and managing the same security control is inefficient. Therefore, this paper presents the criteria for identifying digital assets that are classified as vital digital assets that can directly affect the serious accidents of nuclear power plants.

The number of phishing attacks has increased in Latin America, exceeding the operational skills of cybersecurity analysts. The cognitive security application proposes the use of bigdata, machine learning, and data analytics to improve response times in attack detection. This paper presents an investigation about the analysis of anomalous behavior related with phishing web attacks and how machine learning techniques can be an option to face the problem. This analysis is made with the use of an contaminated data sets, and python tools for developing machine learning for detect phishing attacks through of the analysis of URLs to determinate if are good or bad URLs in base of specific characteristics of the URLs, with the goal of provide realtime information for take proactive decisions that minimize the impact of an attack.

IoT based technology are drastically accelerating the informationization development of the power grid system of China that consists of a huge number of power terminal devices interconnected by the network of electric power IoT. However, the networked power terminal equipment oriented cyberspace security has continually become a challenging problem as network attack is continually varying and evolving. In this paper, we concentrate on the security risk of power terminal equipment and their vulnerability based on ATP attack detection and defense. We first analyze the attack mechanism of APT security attack based on power terminal equipment. Based on the analysis of the security and attack of power IoT terminal device, an ontology-based knowledge representation method of power terminal device and its vulnerability is proposed.

Tactical Data Links (TDL) and Computer Science meet usually when it comes to interoperability andimplementation. However looking at it from an IT security perspective, some interesting issues occur. These become more relevant the more military hard-and software is built using commercial of the shelf (COTS) systems, that are usually implemented using standard Internet technology and software development patterns. This paper looks at Link 16, Link 11 and VMF security considerations and how compatible they are to current IT security standards. Typical security issues are discussed and concepts to mitigate them presented, which however need to be analysed for their suitability to TDL.

The evolution of smart automobiles and vehicles within the Internet of Things (IoT) - particularly as that evolution leads toward a proliferation of completely autonomous vehicles - has sparked considerable interest in the subject of vehicle/automotive security. While the attack surface is wide, there are patterns of exploitable vulnerabilities. In this study we reviewed, classified according to their attack surface and evaluated some of the common vehicle and infrastructure attack vectors identified in the literature. To remediate these attack vectors, specific technical recommendations have been provided as a way towards secure deployments of smart automobiles and transportation infrastructures.

In today's IIoT world, most of the IoT platform providers like Microsoft, Amazon and Google are focused towards connecting devices and extract data from the devices and send the data to the Cloud for analytics. Only there are few companies concentrating on Security measures implemented on Edge Node. Gartner estimates that by 2020, more than 25 percent of all enterprise attackers will make use of the Industrial IoT. As Cyber Security Threat is getting more important, it is essential to ensure protection of data both at rest and at motion. The reflex of Cyber Security in the Industrial IoT Domain is much more severe when compared to the Consumer IoT Segment. The new bottleneck in this are security services which employ computationally intensive software operations and system services [1]. Resilient services consume considerable resources in a design. When such measures are added to thwart security attacks, the resource requirements grow even more demanding. Since the standard IIoT Gateways and other sub devices are resource constrained in nature the conventional design for security services will not be applicable in this case. This paper proposes an intelligent architectural paradigm for the Constrained IIoT Gateways that can efficiently identify the Cyber-Attacks in the Industrial IoT domain.

This paper introduces a biologically inspired associative data model and structure for finding nearest neighbors and similar patterns. The method can be used as an alternative to the classical approaches to accelerate the search for such patterns using various priorities for attributes according to the Sebestyen measure. The presented structure, together with algorithms developed in this paper can be useful in various computational intelligence tasks like pattern matching, recognition, clustering, classification, multi-criterion search etc. This approach is particularly useful for the on-line operation of associative neural network graphs. Graphs that dynamically develop their structure during learning on training data. The results of experiments show that the associative approach can substantially accelerate the nearest neighbor search and that associative structures can also be used as a model for KNN tasks. Finally, this paper presents how the associative structures can be used to self-organize data and represent knowledge about them in the associative way, which yields new search approaches described in this paper.

Deep Learning is an area of Machine Learning research, which can be used to manipulate large amount of information in an intelligent way by using the functionality of computational intelligence. A deep learning system is a fully trainable system beginning from raw input to the final output of recognized objects. Feature selection is an important aspect of deep learning which can be applied for dimensionality reduction or attribute reduction and making the information more explicit and usable. Deep learning can build various learning models which can abstract unknown information by selecting a subset of relevant features. This property of deep learning makes it useful in analysis of highly complex information one which is present in intrusive data or information flowing with in a web system or a network which needs to be analyzed to detect anomalies. Our approach combines the intelligent ability of Deep Learning to build a smart Intrusion detection system.