Biblio

Phishing is an act of technology-based deception that targets individuals to obtain information. To minimize the number of phishing attacks, factors that influence the ability to identify phishing attempts must be examined. The present study aimed to determine how individual differences relate to performance on a phishing task. Undergraduate students completed a questionnaire designed to assess impulsivity, trust, personality characteristics, and Internet/security habits. Participants performed an email task where they had to discriminate between legitimate emails and phishing attempts. Researchers assessed performance in terms of correctly identifying all email types (overall accuracy) as well as accuracy in identifying phishing emails (phishing accuracy). Results indicated that overall and phishing accuracy each possessed unique trust, personality, and impulsivity predictors, but shared one significant behavioral predictor. These results present distinct predictors of phishing susceptibility that should be incorporated in the development of anti-phishing technology and training.

This paper describes a prototype of a novel Permanent Magnetic Elastomer (PME) sheet based skin sensor for robotic applications. Its working principle is to use a Hall effect transducer to measure the change of magnetic field. PME is a polymer that has Neodymium particles distributed inside it, after strong magnetization for anisotropy, the PME acquires strong remanent magnetization that can be comparable to that of a permanent magnet, in this work, we made improvement of the strength of the magnetic field of PME, so it achieved magnetic strength as high as 25 mT when there is no deformation. When external forces apply on the sensor, the deformation of PME causes a change in the magnetic field due to the change in the alignment of the magnetic particles. Compared with other soft magnetic sensors that employ similar technology, we implemented linear regression method to simplify the calibration, so we focus on the point right above the magnetometer. An MLX90393 chip is installed at the bottom of the PME as the magnetometer. Experimental results show that it can measure forces from 0.01–10 N. Calibration is confirmed effective even for shear directions when the surface of PME is less than 15 x 15 mm.

Metaverse opens up a new social networking paradigm where people can experience a real interactive feeling without physical space constraints. Social interactions are gradually evolving from text combined with pictures and videos to 3-dimensional virtual reality, making the social experience increasingly physical, implying that more metaverse applications with immersive experiences will be developed in the future. However, the increasing data dimensionality and volume for new metaverse applications present a significant challenge in data acquisition, security, and sharing. Furthermore, metaverse applications require high capacity and ultrareliability for the wireless system to guarantee the quality of user experience, which cannot be addressed in the current fifth-generation system. Therefore, reaching the metaverse is dependent on the revolution in the sixth-generation (6G) wireless communication, which is expected to provide low-latency, high-throughput, and secure services. This article provides a comprehensive view of metaverse applications and investigates the fundamental technologies for the 6G toward metaverse.

Model checking is one of the most commonly used technique in formal verification. However, the exponential scale state space renders exhaustive state enumeration inefficient even for a moderate System on Chip (SoC) design. In this paper, we propose a method that leverages symbolic execution to accelerate state space search and pinpoint security vulnerabilities. We automatically convert the hardware design to functionally equivalent C++ code and utilize the KLEE symbolic execution engine to perform state exploration through heuristic search. To reduce the search space, we symbolically represent essential input signals while making non-critical inputs concrete. Experiment results have demonstrated that our method can precisely identify security vulnerabilities at significantly lower computation cost.

An accurate state estimation (SE) considering increased uncertainty by the high penetration of renewable energy systems (RESs) is more and more important to enhance situational awareness, and the optimal and resilient operation of the renewable-rich power grids. However, it is anticipated that adversaries who plan to manipulate the target power grid will generate attacks that inject inaccurate data to the SE using the vulnerabilities of the devices and networks. Among potential attack types, false data injection attack (FDIA) is gaining popularity since this can bypass bad data detection (BDD) methods implemented in the SE systems. Although numerous FDIA detection methods have been recently proposed, the uncertainty of system configuration that arises by the continuously increasing penetration of RESs has been been given less consideration in the FDIA algorithms. To address this issue, this paper proposes a new FDIA detection scheme that is applicable to renewable energy-rich power grids. A deep learning framework is developed in particular by synergistically constructing a Bidirectional Long Short-Term Memory (Bi-LSTM) with modern smart grid characteristics. The developed framework is evaluated on the IEEE 14-bus system integrating several RESs by using several attack scenarios. A comparison of the numerical results shows that the proposed FDIA detection mechanism outperforms the existing deep learning-based approaches in a renewable energy-rich grid environment.

With the rapid development of blockchain technology, it provides a new technical solution for secure storage of data and trusted computing. However, in the actual application of data traceability, blockchain technology has an obvious disadvantage: the large amount of data stored in the blockchain system will lead to a long response time for users to query data. Higher query delay severely restricts the development of block chain technology in the traceability system. In order to solve this problem, we propose an efficient, secure and low storage overhead blockchain query scheme. Specifically, we design an index structure independent of Merkle tree to support efficient intra-block query, and create new fields in the block header to optimize inter-block query. Compared with several existing schemes, our scheme ensures the security of data. Finally, we simulate and evaluate our proposed scheme. The results show that the proposed scheme has better execution efficiency while reducing additional overhead.

With the advent of the era of Internet of Things (IoT), the increasing data volume leads to storage outsourcing as a new trend for enterprises and individuals. However, data breaches frequently occur, bringing significant challenges to the privacy protection of the outsourced data management system. There is an urgent need for efficient and secure data sharing schemes for the outsourced data management infrastructure, such as the cloud. Therefore, this paper designs a dual-server-based data sharing scheme with data privacy and high efficiency for the cloud, enabling the internal members to exchange their data efficiently and securely. Dual servers guarantee that none of the servers can get complete data independently by adopting secure two-party computation. In our proposed scheme, if the data is destroyed when sending it to the user, the data will not be restored. To prevent the malicious deletion, the data owner adds a random number to verify the identity during the uploading procedure. To ensure data security, the data is transmitted in ciphertext throughout the process by using searchable encryption. Finally, the black-box leakage analysis and theoretical performance evaluation demonstrate that our proposed data sharing scheme provides solid security and high efficiency in practice.

The security of manycore systems has become increasingly critical. In system-on-chips (SoCs), Hardware Trojans (HTs) manipulate the functionalities of the routing components to saturate the on-chip network, degrade performance, and result in the leakage of sensitive data. Existing HT detection techniques, including runtime monitoring and state-of-the-art learning-based methods, are unable to timely and accurately identify the implanted HTs, due to the increasingly dynamic and complex nature of on-chip communication behaviors. We propose AGAPE, a novel Generative Adversarial Network (GAN)-based anomaly detection and mitigation method against HTs for secured on-chip communication. AGAPE learns the distribution of the multivariate time series of a number of NoC attributes captured by on-chip sensors under both HT-free and HT-infected working conditions. The proposed GAN can learn the potential latent interactions among different runtime attributes concurrently, accurately distinguish abnormal attacked situations from normal SoC behaviors, and identify the type and location of the implanted HTs. Using the detection results, we apply the most suitable protection techniques to each type of detected HTs instead of simply isolating the entire HT-infected router, with the aim to mitigate security threats as well as reducing performance loss. Simulation results show that AGAPE enhances the HT detection accuracy by 19%, reduces network latency and power consumption by 39% and 30%, respectively, as compared to state-of-the-art security designs.

A “tripartite and bilateral” dynamic game model was constructed to study the impact of space deterrence on the challenger's military strategy in a military conflict. Based on the signal game theory, the payment matrices and optimal strategies of the sheltering side and challenging side were analyzed. In a theoretical framework, the indicators of the effectiveness of the challenger's response to space deterrence and the influencing factors of the sheltering's space deterrence were examined. The feasibility and effective means for the challenger to respond to the space deterrent in a “tripartite and bilateral” military conflict were concluded.

Robot Operating System 2 (ROS2) is the latest release of a framework for enabling robot applications. Data Distribution Service (DDS) middleware is used for communication between nodes in a ROS2 cluster. The DDS middleware provides a distributed discovery system, message definitions and serialization, and security. In ROS2, the DDS middleware is accessed through an abstraction layer, making it easy to switch from one implementation to another. The existing middleware implementations differ in a number of ways, e.g., in how they are supported in ROS2, in their support for the security features, their ease of use, their performance, and their interoperability. In this work, the focus is on the ease of use, interoperability, and security features aspects of ROS2 DDS middleware. We compare the ease of installation and ease of use of three different DDS middleware, and test the interoperability of different middleware combinations in simple deployment scenarios. We highlight the difference that enabling the security option makes to interoperability, and conduct performance experiments that show the effect that turning on security has on the communication performance. Our results provide guidelines for choosing and deploying DDS middleware on a ROS2 cluster.

The main objective of this research is to increase security awareness against phishing attacks in the education sector by teaching users about phishing URLs. The educational media was made based on references from several previous studies that were used as basic references. Development of antiphishing game framework educational media using the extended DPE framework. Participants in this study were vocational and college students in the technology field. The respondents included vocational and college students, each with as many as 30 respondents. To assess the level of awareness and understanding of phishing, especially phishing URLs, participants will be given a pre-test before playing the game, and after completing the game, the application will be given a posttest. A paired t-test was used to answer the research hypothesis. The results of data analysis show differences in the results of increasing identification of URL phishing by respondents before and after using educational media of the anti-phishing game framework in increasing security awareness against URL phishing attacks. More serious game development can be carried out in the future to increase user awareness, particularly in phishing or other security issues, and can be implemented for general users who do not have a background in technology.

Edge computing is a prospective notion for expanding the potential of cloud computing. It is vital to maintaining a decent atmosphere free of all forms of security and breaches in order to continue utilizing computer services. The security concerns surrounding the edge computing environment has been impeded as a result of the security issues that surround the area. Many researchers have looked into edge computing security issues, however, not all have thoroughly studied the needs. Security requirements are the goals that specify the capabilities and operations that a process that is carried out by a system in order to eliminate various security flaws. The purpose of this study is to give a complete overview of the many different artificial intelligence technologies that are now being utilized for edge computing security with the intention of aiding research in the future in locating research potential. This article analyzed the most recent research and shed light on the following topics: state-of-the-art techniques used to combat security threats, technological trends used by the method, metrics utilize to assess the techniques' ability, and opportunities of research for future researchers in the area of artificial intelligence for edge computing security.

Network security is a prominent topic that is gaining international attention. Distributed Denial of Service (DDoS) attack is often regarded as one of the most serious threats to network security. Software Defined Network (SDN) decouples the control plane from the data plane, which can meet various network requirements. But SDN can also become the object of DDoS attacks. This paper proposes an automated DDoS attack mitigation method that is based on the programmability of the Ryu controller and the features of the OpenFlow switch flow tables. The Mininet platform is used to simulate the whole process, from SDN traffic generation to using a K-Nearest Neighbor model for traffic classification, as well as identifying and mitigating DDoS attack. The packet counts of the victim's malicious traffic input port are significantly lower after the mitigation method is implemented than before the mitigation operation. The purpose of mitigating DDoS attack is successfully achieved.

Common Vulnerabilities and Exposures (CVE) databases contain information about vulnerabilities of software products and source code. If individual elements of CVE descriptions can be extracted and structured, then the data can be used to search and analyze CVE descriptions. Herein we propose a method to label each element in CVE descriptions by applying Named Entity Recognition (NER). For NER, we used BERT, a transformer-based natural language processing model. Using NER with machine learning can label information from CVE descriptions even if there are some distortions in the data. An experiment involving manually prepared label information for 1000 CVE descriptions shows that the labeling accuracy of the proposed method is about 0.81 for precision and about 0.89 for recall. In addition, we devise a way to train the data by dividing it into labels. Our proposed method can be used to label each element automatically from CVE descriptions.

At present, the ciphertext-policy attribute based encryption (CP-ABE) has been widely used in different fields of data sharing such as cross-border paperless trade, digital government and etc. However, there still exist some challenges including single point of failure, key abuse and key unaccountable issues in CP-ABE. To address these problems. We propose an accountable CP-ABE mechanism based on block chain system. First, we establish two authorization agencies MskCA and AttrVN(Attribute verify Network),where the MskCA can realize master key escrow, and the AttrVN manages and validates users' attributes. In this way, our system can avoid the single point of failure and improve the privacy of user attributes and security of keys. Moreover, in order to realize auditability of CP-ABE key parameter transfer, we introduce the did and record parameter transfer process on the block chain. Finally, we theoretically prove the security of our CP-ABE. Through comprehensive comparison, the superiority of CP-ABE is verified. At the same time, our proposed schemes have some properties such as fast decryption and so on.

Aiming at the big data security and privacy protection issues in the smart grid, the current key technologies for big data security and privacy protection in smart grids are sorted out, and a privacy-protecting smart grid association rule is proposed according to the privacy-protecting smart grid big data analysis and mining technology route The mining plan specifically analyzes the risk factors in the operation of the new power grid, and discusses the information security of power grid users from the perspective of the user, focusing on the protection of privacy and security, using safe multi-party calculation of the support and confidence of the association rules. Privacy-protecting smart grid big data mining enables power companies to improve service quality to 7.5% without divulging customer private information.

Blind identification of channel codes is crucial in intelligent communication and non-cooperative signal processing, and it plays a significant role in wireless physical layer security, information interception, and information confrontation. Previous researches show a high computation complexity by manual feature extractions, in addition, problems of indisposed accuracy and poor robustness are to be resolved in a low signal-to-noise ratio (SNR). For solving these difficulties, based on deep residual shrinkage network (DRSN), this paper proposes a novel recognizer by deep learning technologies to blindly distinguish the type and the parameter of channel codes without any prior knowledge or channel state, furthermore, feature extractions by the neural network from codewords can avoid intricate calculations. We evaluated the performance of this recognizer in AWGN, single-path fading, and multi-path fading channels, the results of the experiments showed that the method we proposed worked well. It could achieve over 85 % of recognition accuracy for channel codes in AWGN channels when SNR is not lower than 4dB, and provide an improvement of more than 5% over the previous research in recognition accuracy, which proves the validation of the proposed method.