Biblio

Power communication network is an important infrastructure of power system. For a large number of widely distributed business terminals and communication terminals. The data protection is related to the safe and stable operation of the whole power grid. How to solve the problem that lots of nodes need a large number of keys and avoid the situation that these nodes cannot exchange information safely because of the lack of keys. In order to solve the problem, this paper proposed a segmentation and combination technology based on quantum key to extend the limited key. The basic idea was to obtain a division scheme according to different conditions, and divide a key into several different sub-keys, and then combine these key segments to generate new keys and distribute them to different terminals in the system. Sufficient keys were beneficial to key updating, and could effectively enhance the ability of communication system to resist damage and intrusion. Through the analysis and calculation, the validity of this method in the use of limited quantum keys to achieve the business data secure transmission of a large number of terminal was further verified.

The continuing decrease in feature size of integrated circuits, and the increase of the complexity and cost of design and fabrication has led to outsourcing the design and fabrication of integrated circuits to third parties across the globe, and in turn has introduced several security vulnerabilities. The adversaries in the supply chain can pirate integrated circuits, overproduce these circuits, perform reverse engineering, and/or insert hardware Trojans in these circuits. Developing countermeasures against such security threats is highly crucial. Accordingly, this paper first develops a learning-based trust verification framework to detect hardware Trojans. To tackle Trojan insertion, IP piracy and overproduction, logic locking schemes and in particular stripped functionality logic locking is discussed and its resiliency against the state-of-the-art attacks is investigated.

Internet of Things (IoT) is transforming the way we live today, improving the quality of living standard and growing the world economy by having smart devices around us making decisions and performing our daily tasks and chores. However, securing the IoT system from malicious attacks is a very challenging task. Some of the most common malicious attacks are Denial of service (DoS), and Distributed Denial of service (DDoS) attacks, which have been causing major security threats to all networks and specifically to limited resource IoT devices. As security will always be a primary factor for enabling most IoT applications, developing a comprehensive detection method that effectively defends against DDoS attacks and can provide 100% detection for DDoS attacks in IoT is a primary goal for the future of IoT. The development of such a method requires a deep understanding of the methods that have been used thus far in the detection of DDoS attacks in the IoT environment. In our survey, we try to emphasize some of the most recent Machine Learning (ML) approaches developed for the detection of DDoS attacks in IoT networks along with their advantage and disadvantages. Comparison between the performances of selected approaches is also provided.

The smart grid is a complex cyber-physical system (CPS) that poses challenges related to scale, integration, interoperability, processes, governance, and human elements. The US National Institute of Standards and Technology (NIST) and its government, university and industry collaborators, developed an approach, called CPS Framework, to reasoning about CPS across multiple levels of concern and competency, including trustworthiness, privacy, reliability, and regulatory. The approach uses ontology and reasoning techniques to achieve a greater understanding of the interdependencies among the elements of the CPS Framework model applied to use cases. This paper demonstrates that the approach extends naturally to automated and manual decision-making for smart grids: we apply it to smart grid use cases, and illustrate how it can be used to analyze grid topologies and address concerns about the smart grid. Smart grid stakeholders, whose decision making may be assisted by this approach, include planners, designers and operators.

Authorship attribution is the problem of studying an anonymous text and finding the corresponding author in a set of candidate authors. In this paper, we propose a method based on N-grams model for the problem of authorship attribution. Several measures are used to assign an anonymous text to an author. The different variants of the proposed method are implemented and validated on PAN benchmarks. The numerical results are encouraging and demonstrate the benefit of the proposed idea.

Searchable symmetric encryption (SSE) scheme allows a data owner to perform search queries over encrypted documents using symmetric cryptography. SSE schemes are useful in cloud storage and data outsourcing. Most of the SSE schemes in existing literature have been proved to leak a substantial amount of information that can lead to an inference attack. This paper presents, a novel leakage resilient searchable symmetric encryption with periodic updation (LRSSEPU) scheme that minimizes extra information leakage, and prevents an untrusted cloud server from performing document mapping attack, query recovery attack and other inference attacks. In particular, the size of the keyword vector is fixed and the keywords are periodically permuted and updated to achieve minimum leakage. Furthermore, our proposed LRSSEPU scheme provides authentication of the query messages and restricts an adversary from performing a replay attack, forged query attack and denial of service attack. We employ a combination of identity-based cryptography (IBC) with symmetric key cryptography to reduce the computation cost and communication overhead. Our scheme is lightweight and easy to implement with very little communication overhead.

With the increasing diversity of Android malware, the effectiveness of conventional defense mechanisms are at risk. This situation has endorsed a notable interest in the improvement of the exactitude and scalability of malware detection for smart devices. In this study, we have proposed an effective deep learning-based malware detection model for competent and improved ransomware detection in Android environment by looking at the algorithm of Long Short-Term Memory (LSTM). The feature selection has been done using 8 different feature selection algorithms. The 19 important features are selected through simple majority voting process by comparing results of all feature filtration techniques. The proposed algorithm is evaluated using android malware dataset (CI-CAndMal2017) and standard performance parameters. The proposed model outperforms with 97.08% detection accuracy. Based on outstanding performance, we endorse our proposed algorithm to be efficient in malware and forensic analysis.

Internet of Things is the newfound information architecture based on the Internet that develops interactions between objects and services in a secure and reliable environment. As the availability of many smart devices rises, secure and scalable mass storage systems for aggregate data is required in IoTs applications. In this paper, we propose a new method for storing aggregate data in IoTs by use of ( t, n) -threshold secret sharing scheme in the cloud storage. In this method, original data is divided into t blocks that each block is considered as a share. This method is scalable and traceable, i.e., new data can be inserted or part of original data can be deleted, without changing shares, also cloud service providers' fault in sending invalid shares are detectable.

In recent years, sentiment analysis has been aware within software engineering domain. While automated sentiment analysis has long been suffering from doubt of accuracy, the tool performance is unstable when being applied on datasets other than the original dataset for evaluation. Researchers also have the disagreements upon if machine learning algorithms perform better than conventional lexicon and rule based approaches. In this paper, we looked into the factors in datasets that may affect the evaluation performance, also evaluated the popular machine learning algorithms in sentiment analysis, then proposed a novel structure for automated sentiment tool combines advantages from both approaches.

Federated learning (shorted as FL) recently proposed by Google is a privacy-preserving method to integrate distributed data trainers. FL is extremely useful due to its ensuring privacy, lower latency, less power consumption and smarter models, but it could fail if multiple trainers abort training or send malformed messages to its partners. Such misbehavior are not auditable and parameter server may compute incorrectly due to single point failure. Furthermore, FL has no incentive to attract sufficient distributed training data and computation power. In this paper, we propose FLChain to build a decentralized, public auditable and healthy FL ecosystem with trust and incentive. FLChain replace traditional FL parameter server whose computation result must be consensual on-chain. Our work is not trivial when it is vital and hard to provide enough incentive and deterrence to distributed trainers. We achieve model commercialization by providing a healthy marketplace for collaborative-training models. Honest trainer can gain fairly partitioned profit from well-trained model according to its contribution and the malicious can be timely detected and heavily punished. To reduce the time cost of misbehavior detecting and model query, we design DDCBF for accelerating the query of blockchain-documented information. Finally, we implement a prototype of our work and measure the cost of various operations.

Software-Defined Network's (SDN) core working depends on the centralized controller which implements the control plane. With the help of this controller, security threats like Distributed Denial of Service (DDoS) attacks can be identified easily. A DDoS attack is usually instigated on servers by sending a huge amount of unwanted traffic that exhausts its resources, denying their services to genuine users. Earlier research work has been carried out to mitigate DDoS attacks at the switch and the host level. Mitigation at switch level involves identifying the switch which sends a lot of unwanted traffic in the network and blocking it from the network. But this solution is not feasible as it will also block genuine hosts connected to that switch. Later mitigation at the host level was introduced wherein the compromised hosts were identified and blocked thereby allowing genuine hosts to send their traffic in the network. Though this solution is feasible, it will block the traffic from the genuine applications of the compromised host as well. In this paper, we propose a new way to identify and mitigate the DDoS attack at the application level so that only the application generating the DDoS traffic is blocked and other genuine applications are allowed to send traffic in the network normally.

Research into the established area of ITS is evolving into the Internet of Vehicles (IoV), itself a fast-moving research area, fuelled in part by rapid changes in computing and communication technologies. Using pseudonym certificate is a popular way to address privacy issues in IoV. Therefore, the certificate management scheme is considered as a feasible technique to manage system and maintain the lifecycle of certificate. In this paper, we propose an efficient pseudonym certificate management scheme in IoV. The Blockchain concept is introduced to simplify the network structure and distributed maintenance of the Certificate Revocation List (CRL). The proposed scheme embeds part of the certificate revocation functions within the security and privacy applications, aiming to reduce the communication overhead and shorten the processing time cost. Extensive simulations and analysis show the effectiveness and efficiency of the proposed scheme, in which the Blockchain structure costs fewer network resources and gives a more economic solution to against further cybercrime attacks.

Millions of news are being exchanged daily among people. With the appearance of the Internet, the way of broadcasting news has changed and become faster, however it caused many problems. For instance, the increase in the speed of broadcasting news leads to an increase in the speed of fake news creation. Fake news can have a huge impression on societies. Additionally, the existence of a central entity, such as news agencies, could lead to fraud in the news broadcasting process, e.g. generating fake news and publishing them for their benefits. Since Blockchain technology provides a reliable decentralized network, it can be used to publish news. In addition, Blockchain with the help of decentralized applications and smart contracts can provide a platform in which fake news can be detected through public participation. In this paper, we proposed a new method for sharing and analyzing news to detect fake news using Blockchain, called SANUB. SANUB provides features such as publishing news anonymously, news evaluation, reporter validation, fake news detection and proof of news ownership. The results of our analysis show that SANUB outperformed the existing methods.

Neural Network is used in many applications and guarding its performance against faults is a research challenge. Self-healing neural network is a promising concept for achieving reliability, which is the ability to detect and fix a fault in the system automatically. Most of the current self-healing neural network are based on replication of hardware nodes which causes significant area overhead. The proposed self-healing approach results in a modest area overhead and it is suitable for complex neural network. The proposed method is based on a shared operation and a spare node in each layer which compensates for any faulty node in the layer. Each faulty node will be compensated by its neighbor node, and the neighbor node performs the faulty node as well as its own operations sequentially. In the case the neighbor is faulty, the spare node will compensate for it. The proposed method is implemented using VHDL and the simulation results are obtained using Altira 10 GX FPGA for a different number of nodes. The area overhead is very small for a complex network. The reliability of the proposed method is studied and compared with the traditional neural network.

In this paper we present a method based on linear programming that facilitates reliable safety verification of hybrid dynamical systems over the infinite time horizon subject to perturbation inputs. The verification algorithm applies the probably approximately correct (PAC) learning framework and consequently can be regarded as statistically formal verification in the sense that it provides formal safety guarantees expressed using error probabilities and confidences. The safety of hybrid systems in this framework is verified via the computation of so-called PAC barrier certificates, which can be computed by solving a linear programming problem. Based on scenario approaches, the linear program is constructed by a family of independent and identically distributed state samples. In this way we can conduct verification of hybrid dynamical systems that existing methods are not capable of dealing with. Some preliminary experiments demonstrate the performance of our approach.

The fast advancement in the last two decades proposed a new challenge in security. In addition, the methods used to secure information are drawing more attention and under intense investigation by researchers around the globe. However, securing data is a very hard task, due to the escalation of threat levels. Several technologies and techniques developed and used to secure data throughout communication or by direct access to the information as an example encryption techniques and authentication techniques. A most recent development methods used to enhance security is by using human biometric characteristics such as thumb, hand, eye, cornea, and DNA; to enforce the security of a system toward higher level, human DNA is a promising field and human biometric characteristics can enhance the security of any system using biometric features for authentication. Furthermore, the proposed methods does not fulfil or present the ultimate solution toward tightening the system security. However, one of the proposed solutions enroll a technique to encrypt the biometric characteristic using a well-known cryptosystem technique. In this paper, an overview presented on the benefits of incorporating a human DNA based security systems and the overall effect on how such systems enhance the security of a system. In addition, an algorithm is proposed for practical application and the implementation discussed briefly.

The concept of a decoy process is a new development of defensive deception beyond traditional honeypots. Decoy processes can be exceptionally effective in detecting malware, directly upon contact or by redirecting malware to decoy I/O. A key requirement is that they resemble their real counterparts very closely to withstand adversarial probes by threat actors. To be usable, decoy processes need to consume only a small fraction of the resources consumed by their real counterparts. Our contribution in this paper is twofold. We attack the resource utilization consistency of decoy processes provided by a neural network with a heatmap training mechanism, which we find to be insufficiently trained. We then devise machine learning over control flow graphs that improves the heatmap training mechanism. A neural network retrained by our work shows higher accuracy and defeats our attacks without a significant increase in its own resource utilization.

Internet of Things (IoT) has an immense potential for a plethora of applications ranging from healthcare automation to defence networks and the power grid. The security of an IoT network is essentially paramount to the security of the underlying computing and communication infrastructure. However, due to constrained resources and limited computational capabilities, IoT networks are prone to various attacks. Thus, safeguarding the IoT network from adversarial attacks is of vital importance and can be realised through planning and deployment of effective security controls; one such control being an intrusion detection system. In this paper, we present a novel intrusion detection scheme for IoT networks that classifies traffic flow through the application of deep learning concepts. We adopt a newly published IoT dataset and generate generic features from the field information in packet level. We develop a feed-forward neural networks model for binary and multi-class classification including denial of service, distributed denial of service, reconnaissance and information theft attacks against IoT devices. Results obtained through the evaluation of the proposed scheme via the processed dataset illustrate a high classification accuracy.

The presented method of encoding and steganographic embedding of a series of bits for the hidden message was first developed by modifying the digital platform (bases) of the elements of the image container. Unlike other methods, steganographic coding and embedding is accomplished by changing the elements of the image fragment, followed by the formation of code structures for the established structure of the digital representation of the structural elements of the image media image. The method of estimating quantitative indicators of embedded critical data is presented. The number of bits of the container for the developed method of steganographic coding and embedding of critical information is estimated. The efficiency of the presented method is evaluated and the comparative analysis of the value of the embedded digital data in relation to the method of weight coefficients of the discrete cosine transformation matrix, as well as the comparative analysis of the developed method of steganographic coding, compared with the Koch and Zhao methods to determine the embedded data resistance against attacks of various types. It is determined that for different values of the quantization coefficient, the most critical are the built-in containers of critical information, which are built by changing the part of the digital video data platform depending on the size of the digital platform and the number of bits of the built-in container.

This article is an introduction to a well known problem on the ring Fq[e] where e3=e2: Fully homomorphic encryption scheme. In this paper, we introduce a new diagram of encryption based on the conjugate problem on Fq[e] , (ESR(Fq[e])).

ASA systems (firewall, IDS, IPS) are probable to become communication bottlenecks in networks with growing network bandwidths. To alleviate this issue, we suggest to use Application-aware mechanism based on Deep Packet Inspection (DPI) to bypass chosen traffic around firewalls. The services of Internet video sharing gained importance and expanded their share of the multimedia market. The Internet video should meet strict service quality (QoS) criteria to make the broadcasting of broadcast television a viable and comparable level of quality. However, since the Internet video relies on packet communication, it is subject to delays, transmission failures, loss of data and bandwidth restrictions that may have a catastrophic effect on the quality of multimedia.

Continuous growth in sensor data and other temporal sequence data necessitates efficient retrieval and similarity search support on these big time series datasets. However, finding exact similarity results, especially at the granularity of subsequences, is known to be prohibitively costly for large data sets. In this paper, we thus propose an efficient framework for solving this exact subsequence similarity match problem, called TINN (TIme series Nearest Neighbor search). Exploiting the range interval diversity properties of time series datasets, TINN captures similarity at two levels of abstraction, namely, relationships among subsequences within each long time series and relationships across distinct time series in the data set. These relationships are compactly organized in an augmented relationship graph model, with the former relationships encoded in similarity vectors at TINN nodes and the later captured by augmented edge types in the TINN Graph. Query processing strategy deploy novel pruning techniques on the TINN Graph, including node skipping, vertical and horizontal pruning, to significantly reduce the number of time series as well as subsequences to be explored. Comprehensive experiments on synthetic and real world time series data demonstrate that our TINN model consistently outperforms state-of-the-art approaches while still guaranteeing to retrieve exact matches.

Trusted Execution Environments (TEEs) provide hardware support to isolate the execution of sensitive operations on mobile phones for improved security. However, they are not always available to use for application developers. To provide a consistent user experience to those who have and do not have a TEE-enabled device, we could get help from Open-TEE, an open-source GlobalPlatform (GP)-compliant software TEE emulator. However, Open-TEE does not offer any of the security properties hardware TEEs have. In this paper, we propose WhiteBox-TEE which integrates white-box cryptography with Open-TEE to provide better security while still remaining complaint with GP TEE specifications. We discuss the architecture, provisioning mechanism, implementation highlights, security properties and performance issues of WhiteBox-TEE and propose possible revisions to TEE specifications to have better use of white-box cryptography in software-only TEEs.

The huge volume, variety, and velocity of big data have empowered Machine Learning (ML) techniques and Artificial Intelligence (AI) systems. However, the vast portion of data used to train AI systems is sensitive information. Hence, any vulnerability has a potentially disastrous impact on privacy aspects and security issues. Nevertheless, the increased demands for high-quality AI from governments and companies require the utilization of big data in the systems. Several studies have highlighted the threats of big data on different platforms and the countermeasures to reduce the risks caused by attacks. In this paper, we provide an overview of the existing threats which violate privacy aspects and security issues inflicted by big data as a primary driving force within the AI/ML workflow. We define an adversarial model to investigate the attacks. Additionally, we analyze and summarize the defense strategies and countermeasures of these attacks. Furthermore, due to the impact of AI systems in the market and the vast majority of business sectors, we also investigate Standards Developing Organizations (SDOs) that are actively involved in providing guidelines to protect the privacy and ensure the security of big data and AI systems. Our far-reaching goal is to bridge the research and standardization frame to increase the consistency and efficiency of AI systems developments guaranteeing customer satisfaction while transferring a high degree of trustworthiness.

This work presents a new method of encrypting and decrypting speech based on a chaotic key generator. The proposed scheme takes advantage of the best features of chaotic systems. In the proposed method, the input speech signal is converted into an image which is ciphered by an encryption function using a chaotic key matrix generated from a fractional-order chaotic map. Based on a deadbeat observer, the exact synchronization of system used is established, and the decryption is performed. Different analysis are applied for analyzing the effectiveness of the encryption system. The obtained results confirm that the proposed system offers a higher level of security against various attacks and holds a strong key generation mechanism for satisfactory speech communication.