Biblio

Cognitive Radio Network makes intelligent use of the spectrum resources. However, spectrum sensing is vulnerable to numerous harmful assaults. To lower the network's performance, hackers attempt to alter the sensed result. In the fusion centre, blockchain technology is used to make broad judgments on spectrum sensing in order to detect and thwart hostile activities. The sensed local results are hashed using the SHA 3 technique. This improves spectrum sensing precision and effectively thwarts harmful attacks. In comparison to other established techniques like equal gain combining, the simulation results demonstrate higher detection probability and sensing precision. Thus, employing Blockchain technology, cognitive radio network security can be significantly enhanced.

This paper investigates the secrecy outage performance of Multiple Input Multiple Output (MIMO) secondary nodes for underlay Cognitive Radio Network (CRN) over α–μ fading channel. Here, the proposed system consists of one active eavesdropper and two primary nodes each with a single antenna. The power of the secondary transmitter depends on the harvested energy from the primary transmitter to save more energy and spectrum. Moreover, a Transmit Antenna Selection (TAS) scheme is adopted at the secondary source, while the Maximal Ratio Combining (MRC) technique is employed at the secondary receiver to optimize the quality of the signal. A lower bound closed-form phrase for the secrecy outage performance is derived to demonstrate the effects of the channel parameters. In addition, numerical results illustrate that the number of source transmit antennas, destination received antenna, and the eavesdropper received antenna have significant effects on improving the secrecy performance.

With the increasing application of micro-nano satellite network, it is extremely vulnerable to the influence of internal malicious nodes in the practical application process. However, currently micro-nano satellite network still lacks effective means of routing security protection. In order to solve this problem, combining with the characteristics of limited energy and computing capacity of micro-nano satellite nodes, this research proposes a secure routing algorithm based on trust value. First, the trust value of the computing node is synthesized, and then the routing path is generated by combining the trust value of the node with the AODV routing algorithm. Simulation results show that the proposed MNS-AODV routing algorithm can effectively resist the influence of internal malicious nodes on data transmission, and it can reduce the packet loss rate and average energy consumption.

Scene text detection methods have developed greatly in the past few years. However, due to the limitation of the diversity of the text background of natural scene, the previous methods often failed when detecting more complicated text instances (e.g., super-long text and arbitrarily shaped text). In this paper, a text detection method based on multi -channel bounding box fusion is designed to address the problem. Firstly, the convolutional neural network is used as the basic network for feature extraction, including shallow text feature map and deep semantic text feature map. Secondly, the whole convolutional network is used for upsampling of feature map and fusion of feature map at each layer, so as to obtain pixel-level text and non-text classification results. Then, two independent text detection boxes channels are designed: the boundary box regression channel and get the bounding box directly on the score map channel. Finally, the result is obtained by combining multi-channel boundary box fusion mechanism with the detection box of the two channels. Experiments on ICDAR2013 and ICDAR2015 demonstrate that the proposed method achieves competitive results in scene text detection.

Surveillance videos can capture anomalies in real scenarios and play an important role in security systems. Anomaly events are unpredictable, which reflect the unsupervised nature of the problem. In addition, it is difficult to construct a complete video dataset which contains all normal events. Based on the diversity of normal events, this paper proposes a memory-enhanced unsupervised method for anomaly detection. The proposed method reconstructs video events by combining prototype features and encoded features to detect anomaly events. Furthermore, a memory module is introduced to better store the prototype patterns of normal events. Experimental results in various benchmark datasets demonstrate the effectiveness and robustness of the proposed method.

In the distributed file sharing system, a large number of users share bandwidth, upload resources and store them in a decentralized manner, thus offering both an abundant supply of high-quality resources and high-speed download. However, some users only enjoy the convenient service without uploading or sharing, which is called free riding. Free-riding may discourage other honest users. When free-riding users mount to a certain number, the platform may fail to work. The current available incentive mechanisms, such as reciprocal incentive mechanisms and reputation-based incentive mechanisms, which suffer simple incentive models, inability to achieve incentive circulation and dependence on a third-party trusted agency, are unable to completely solve the free-riding problem.In this paper we build a blockchain-based distributed file sharing platform and design a nested incentive scheme for this platform. The proposed nested incentive mechanism achieves the circulation of incentives in the platform and does not rely on any trusted third parties for incentive distribution, thus providing a better solution to free-riding. Our distributed file sharing platform prototype is built on the current mainstream blockchain. Nested incentive scheme experiments on this platform verify the effectiveness and superiority of our incentive scheme in solving the free-riding problem compared to other schemes.

We focus on resilience towards covert attacks on Cyber-Physical Systems (CPS). We define the new k-steerability and l-monitorability control-theoretic concepts. k-steerability reflects the ability to act on every individual plant state variable with at least k different groups of functionally diverse input signals. l-monitorability indicates the ability to monitor every individual plant state variable with £ different groups of functionally diverse output signals. A CPS with k-steerability and l-monitorability is said to be (k, l)-resilient. k and l, when both greater than one, provide the capability to mitigate the impact of covert attacks when some signals, but not all, are compromised. We analyze the influence of k and l on the resilience of a system and the ability to recover its state when attacks are perpetrated. We argue that the values of k and l can be augmented by combining redundancy and diversity in hardware and software techniques that apply the moving target paradigm.

To build the fifth generation (5G) mobile network, the sharing structure in the 5G network adopted in industries has gained great research interesting. However, in this structure data are shared among diversity networks, which introduces the threaten of network security, such as covert timing channels. To eliminate the covert timing channel, we propose to inject noise into the covert timing channel. By analyzing the modulation method of covert timing channels, we design the jamming strategy on the covert channel. According to the strategy, the interference algorithm of the covert timing channel is designed. Since the interference algorithm depends heavily on the memory, we construct a distributing jammer. Experiments results show that these covert time channel can be blocked under the distributing jammer.

With the development of location technology, location-based services greatly facilitate people's life . However, due to the location information contains a large amount of user sensitive informations, the servicer in location-based services published location data also be subject to the risk of privacy disclosure. In particular, it is more easy to lead to privacy leaks without considering the attacker's semantic background knowledge while the publish sparse location data. So, we proposed semantic k-anonymity privacy protection method to against above problem in this paper. In this method, we first proposed multi-user compressing sensing method to reconstruct the missing location data . To balance the availability and privacy requirment of anonymity set, We use semantic translation and multi-view fusion to selected non-sensitive data to join anonymous set. Experiment results on two real world datasets demonstrate that our solution improve the quality of privacy protection to against semantic attacks.

This paper investigates the secrecy outage performance of a dual-hop decode-and-forward (DF) mixed radio-frequency/underwater optical wireless communication (RF/UOWC) system. We consider a one-antenna source node ( S), communicating with one legitimate destination node (D) via a multi-antenna DF relay (R) node. In this context, the relay node receives the incoming signal from S via an RF link, which is subject to Rayleigh fading, then performes selection-combining (SC) followed by decoding and then re-encoding for transmission to the destination over a UOWC link, subject to mixture Exponential-Gamma fading. Under the assumption of eavesdroppers attempting to intercept the S-R (RF side), a closed-form expression for the secrecy outage probability is derived. Our analytical results are corroborated through computer simulations, which verifies their validity.

This paper investigates the secrecy outage performance of an energy harvesting-based dual-hop amplify-and-forward (AF) mixed radio-frequency/underwater optical wireless communication (RF/UOWC) system. A single-antenna source node (S) is considered, communicating with one legitimate destination node (D) with the aid of a multi-antenna AF relay (R) device. In this setup, the relay node receives the incoming signal from S via an RF link, which is subject to Nakagami-m fading, then performs maximal-ratio-combining (MRC) followed by a fixed-gain amplification, before transmitting it to the destination via a UOWC link, subject to mixture Exponential-Gamma fading. Assuming the presence of a malicious eavesdropper attempting to intercept the S- R hop, a tight approximate expression for the secrecy outage probability is retrieved. The derived results provide useful insights into the influence of key system parameters on the secrecy outage performance. Our analytical results are corroborated through computer simulations, which verifies their validity.

In this article, we study the transmission secrecy performance of primary user in overlay cognitive wireless networks, in which an untrusted energy-limited secondary cooperative user assists the primary transmission to exchange for the spectrum resource. In the network, the information can be simultaneously transmitted through the direct and relay links. For the enhancement of primary transmission security, a maximum ratio combining (MRC) scheme is utilized by the receiver to exploit the two copies of source information. For the security analysis, we firstly derive the tight lower bound expression for secrecy outage probability (SOP). Then, three asymptotic expressions for SOP are also expressed to further analyze the impacts of the transmit power and the location of secondary cooperative node on the primary user information security. The findings show that the primary user information secrecy performance enhances with the improvement of transmit power. Moreover, the smaller the distance between the secondary node and the destination, the better the primary secrecy performance.

Moving Target Defence (MTD) has been recently proposed and is an emerging proactive approach which provides an asynchronous defensive strategies. Unlike traditional security solutions that focused on removing vulnerabilities, MTD makes a system dynamic and unpredictable by continuously changing attack surface to confuse attackers. MTD can be utilized in cloud computing to address the cloud's security-related problems. There are many literature proposing MTD methods in various contexts, but it still lacks approaches to evaluate the effectiveness of proposed MTD method. In this paper, we proposed a combination of Shuffle and Diversity MTD techniques and investigate on the effects of deploying these techniques from two perspectives lying on two groups of security metrics (i) system risk: which is the cloud providers' perspective and (ii) attack cost and return on attack: which are attacker's point of view. Moreover, we utilize a scalable Graphical Security Model (GSM) to enhance the security analysis complexity. Finally, we show that combining MTD techniques can improve both aforementioned two groups of security metrics while individual technique cannot.

Recently, the IoT (internet of things) still does not have global policies and standards to govern the interaction and the development of applications. There are huge of security issues relevant to the application layer of IoT becoming very urgent. On the other hand, it is important for addressing the development of security algorithm to protect the IoT system from malicious attack. The service requesters must pay attention to the data how will be used, who and when to apply, even they must have tools to control what data want to be disclosed. In this article, a fusion diversity scheme adopting MRC (maximum ratio combining) scheme with TM (trust management) security algorithm is proposed. In MRC stage, specified parameters first extracted and before combined with the control information they weighted by one estimation value. The fused information forward to the upper layer of IoT technologies in succession after the combination is completed. The simulation results from experiments deployed with physical assessment show that the security has more reliability after the MRC scheme fused into the TM procedure.

In this work, Automatic-Repeat-Request (ARQ) and Maximal Ratio Combination (MRC), have been jointly exploited to enhance the confidentiality of wireless services requested by a legitimate user (Bob) against an eavesdropper (Eve). The obtained security performance is analyzed using Packet Error Rate (PER), where the exact PER gap between Bob and Eve is determined. PER is proposed as a new practical security metric in cross layers (Physical/MAC) security design since it reflects the influence of upper layers mechanisms, and it can be linked with Quality of Service (QoS) requirements for various digital services such as voice and video. Exact PER formulas for both Eve and Bob in i.i.d Rayleigh fading channel are derived. The simulation and theoretical results show that the employment of ARQ mechanism and MRC on a signal level basis before demodulation can significantly enhance data security for certain services at specific SNRs. However, to increase and ensure the security of a specific service at any SNR, adaptive modulation is proposed to be used along with the aforementioned scheme. Analytical and simulation studies demonstrate orders of magnitude difference in PER performance between eavesdroppers and intended receivers.

Massive MIMO and tight cooperation between transmission nodes are expected to become an integral part of a future 5G radio system. As part of an overall interference mitigation scheme substantial gains in coverage, spectral as well as energy efficiency have been reported. One of the main limitations for massive MIMO and coordinated multi-point (CoMP) systems is the aging of the channel state information at the transmitter (CSIT), which can be overcome partly by state of the art channel prediction techniques. For a clean slate 5G radio system, we propose to integrate channel prediction from the scratch in a flexible manner to benefit from future improvements in this area. As any prediction is unreliable by nature, further improvements over the state of the art are needed for a convincing solution. In this paper, we explain how the basic ingredients of 5G like base stations with massive MIMO antenna arrays, and multiple UE antennas can help to stretch today's limits with an approximately 10 dB lower normalized mean square error (NMSE) of the predicted channel. In combination with the novel introduced concept of artificially mutually coupled antennas, adding super-directivity gains to virtual beamforming, robust and accurate prediction over 10 ms with an NMSE of -20 dB up to 15 km/h at 2.6 GHz RF frequency could be achieved. This result has been achieved for measured channels without massive MIMO, but a comparison with ray-traced channels for the same scenario is provided as well.