Biblio

The architecture and functioning of the electricity markets are rapidly evolving in favour of solutions based on real-time data sharing and decentralised, distributed, renewable energy generation. Peer-to-peer (P2P) energy markets allow two individuals to transact with one another without the need of intermediaries, reducing the load on the power grid during peak hours. However, such a P2P energy market is prone to various cyber attacks. Blockchain technology has been proposed to implement P2P energy trading to support this change. One of the most crucial components of blockchain technology in energy trading is the consensus mechanism. It determines the effectiveness and security of the blockchain for energy trading. However, most of the consensus used in energy trading today are traditional consensus such as Proof-of-Work (PoW) and Practical Byzantine Fault Tolerance (PBFT). These traditional mechanisms cannot be directly adopted in P2P energy trading due to their huge computational power, low throughput, and high latency. Therefore, we propose the Block Alliance Consensus (BAC) mechanism based on Hashgraph. In a massive P2P energy trading network, BAC can keep Hashgraph's throughput while resisting Sybil attacks and supporting the addition and deletion of energy participants. The high efficiency and security of BAC and the blockchain-based energy trading platform are verified through experiments: our improved BAC has an average throughput that is 2.56 times more than regular BFT, 5 times greater than PoW, and 30% greater than the original BAC. The improved BAC has an average latency that is 41% less than BAC and 81% less than original BFT. Our energy trading blockchain (ETB)'s READ performance can achieve the most outstanding throughput of 1192 tps at a workload of 1200 tps, while WRITE can achieve 682 tps at a workload of 800 tps with a success rate of 95% and 0.18 seconds of latency.

Modular exponentiation (ME) is a complex operation for several public-key cryptosystems (PKCs). Moreover, ME is expensive for resource-constrained devices in terms of computation time and energy consumption, especially when the exponent is large. ME is defined as the task of raising an integer x to power k and reducing the result modulo some integer n. Several methods to calculate ME have been proposed. In this paper, we present the efficient ME methods. We then implement the methods using different security levels of RSA keys on a Raspberry Pi. Finally, we give the fast ME method.

Nowadays, data is essential in several fields, such as science, finance, medicine, and transportation, which means its value continues to rise. Relational databases are vulnerable to copyright threats when transmitted and shared as a carrier of data. The watermarking technique is seen as a partial solution to the problem of securing copyright ownership. However, most of them are currently restricted to numerical attributes in relational databases, limiting their versatility. Furthermore, they modify the source data to a large extent, failing to keep the characteristics of the original database, and they are susceptible to solid malicious attacks. This paper proposes a new robust reversible watermarking technique, Fields Based Inserting Position Tuples algorithm (FBIPT), for relational databases. FBIPT does not modify the original database directly; instead, it inserts some position tuples based on three Fields―Group Field, Feature Field, and Control Field. Field information can be calculated by numeric attributes and any attribute that can be transformed into binary bits. FBIPT technique retains all the characteristics of the source database, and experimental results prove the effectiveness of FBIPT and show its highly robust performance compared to state-of-the-art watermarking schemes.

Memory-based vulnerabilities are becoming more and more common in low-power and low-cost devices in IOT. We study several low-level vulnerabilities that lead to memory corruption in C and C++ programs, and how to use stack corruption and format string attack to exploit these vulnerabilities. Automatic methods for resisting memory attacks, such as stack canary and address space layout randomization ASLR, are studied. These methods do not need to change the source program. However, a return-oriented programming (ROP) technology can bypass them. Control flow integrity (CFI) can resist the destruction of ROP technology. In fact, the security design is holistic. Finally, we summarize the rules of security coding in embedded devices, and propose two novel methods of software anomaly detection process for IOT devices in the future.

Financial technology (Fintech) is an amalgamation of financial management using a technology system. Fintech has become a public concern because this service provides many service features to make it easier from the financial side, such as being used in cooperative financial institutions, banking and insurance. This paper will analyze the opportunities and challenges of Fintech sharia in Indonesia. By exploring the existing literature, this article will try to answer that question. This research is carried out using a literature review approach and comparative qualitative method which will determined the results of the SWOT analysis of sharia financial technology in indonesia. It is needed to mitigate risk of funding in a peer to peer method in overcoming the security of funds and data from investors, firstly companies can perform transparency on the clarity of investor funds. This is done as one of the facilities provided to investors in the Fintech application. In the future, it is hoped that in facing competition, sharia-based fintech companies must be able to provide targeted services through the socialization of sharia fintech to the public, both online and offline. Investors are expected to be more careful before investing in choosing Fintech Peer to Peer (P2P) Lending services by checking the list of Fintech lending and lending companies registered and found by the Financial Services Authority (OJK).

JPEG-XS offers low complexity image compression for applications with constrained but reasonable bit-rate, and low latency. Our paper explores the deployment of JPEG-XS on lossy packet networks. To preserve low latency, Forward Error Correction (FEC) is envisioned as the protection mechanism of interest. Although the JPEG-XS codestream is not scalable in essence, we observe that the loss of a codestream fraction impacts the decoded image quality differently, depending on whether this codestream fraction corresponds to codestream headers, to coefficient significance information, or to low/high frequency data. Hence, we propose a rate-distortion optimal unequal error protection scheme that adapts the redundancy level of Reed-Solomon codes according to the rate of channel losses and the type of information protected by the code. Our experiments demonstrate that, at 5% loss rates, it reduces the Mean Squared Error by up to 92% and 65%, compared to a transmission without and with optimal but equal protection, respectively.

Identity based digital signature is an important research topic of public key cryptography, which can effectively guarantee the authentication, integrity and unforgeability of data. In this paper, a new fuzzy multi-identity based signature scheme is proposed. It is proved that the scheme is existentially unforgeable against adaptively chosen message attack, and the security of the signature scheme can be reduced to CDH assumption. The storage cost and the communication overhead are small, therefore the new fuzzy multi-identity based signature (FMIBS) scheme can be implemented efficiently.

At present, the research on the network security problem of underwater wireless sensors is still few, and since the underwater environment is exposed, passive security defense technology is not enough to deal with unknown security threats. Aiming at this problem, this paper proposes an offensive and defensive game model from the finite rationality of the network attack and defense sides, combined with evolutionary game theory. The replicated dynamic equation is introduced to analyze the evolution trend of strategies under different circumstances, and the selection algorithm of optimal strategy is designed, which verifies the effectiveness of this model through simulation and provides guidance for active defense technology.

Due to the simplicity of implementation and high threat level, SQL injection attacks are one of the oldest, most prevalent, and most destructive types of security attacks on Web-based information systems. With the continuous development and maturity of artificial intelligence technology, it has been a general trend to use AI technology to detect SQL injection. The selection of the sample set is the deciding factor of whether AI algorithms can achieve good results, but dataset with tagged specific category labels are difficult to obtain. This paper focuses on data augmentation to learn similar feature representations from the original data to improve the accuracy of classification models. In this paper, deep convolutional generative adversarial networks combined with genetic algorithms are applied to the field of Web vulnerability attacks, aiming to solve the problem of insufficient number of SQL injection samples. This method is also expected to be applied to sample generation for other types of vulnerability attacks.

Modern web applications are getting more sophisticated by using frameworks that make development easy, but pose challenges for security analysis tools. New analysis techniques are needed to handle such frameworks that grow in number and popularity. In this paper, we describe Gelato that addresses the most crucial challenges for a security-aware client-side analysis of highly dynamic web applications. In particular, we use a feedback-driven and state-aware crawler that is able to analyze complex framework-based applications automatically, and is guided to maximize coverage of security-sensitive parts of the program. Moreover, we propose a new lightweight client-side taint analysis that outperforms the state-of-the-art tools, requires no modification to browsers, and reports non-trivial taint flows on modern JavaScript applications. Gelato reports vulnerabilities with higher accuracy than existing tools and achieves significantly better coverage on 12 applications of which three are used in production.

By broadcasting false Global Navigation Satellite System (GNSS) signals, spoofing attacks will induce false position and time fixes within the victim receiver. In this article, we propose a Sparse Decomposition (SD)-based spoofing detection algorithm in the acquisition process, which can be applied in a single-antenna receiver. In the first step, we map the Fast Fourier transform (FFT)-based acquisition result in a two-dimensional matrix, which is a distorted autocorrelation function when the receiver is under spoof attack. In the second step, the distorted function is decomposed into two main autocorrelation function components of different code phases. The corresponding elements of the result vector of the SD are the code-phase values of the spoofed and the authentic signals. Numerical simulation results show that the proposed method can not only outcome spoofing detection result, but provide reliable estimations of the code phase delay of the spoof attack.

Recent years have witnessed a surge in ransomware attacks. Especially, many a new variant of ransomware has continued to emerge, employing more advanced techniques distributing the payload while avoiding detection. This renders the traditional static ransomware detection mechanism ineffective. In this paper, we present our Hardware Anomaly Realtime Detection - Lightweight (HARD-Lite) framework that employs semi-supervised machine learning method to detect ransomware using low-level hardware information. By using an LSTM network with a weighted majority voting ensemble and exponential moving average, we are able to take into consideration the temporal aspect of hardware-level information formed as time series in order to detect deviation in system behavior, thereby increasing the detection accuracy whilst reducing the number of false positives. Testing against various ransomware across multiple families, HARD-Lite has demonstrated remarkable effectiveness, detecting all cases tested successfully. What's more, with a hierarchical design that distributing the classifier from the user machine that is under monitoring to a server machine, Hard-Lite enables good scalability as well.

Audit systems maintain detailed logs of security-related events on enterprise machines to forensically analyze potential incidents. In principle, these logs should be safely stored in a secure location (e.g., network storage) as soon as they are produced, but this incurs prohibitive slowdown to a monitored machine. Hence, existing audit systems protect batched logs asynchronously (e.g., after tens of seconds), but this allows attackers to tamper with unprotected logs.This paper presents HARDLOG, a practical and effective system that employs a novel audit device to provide fine-grained log protection with minimal performance slowdown. HARDLOG implements criticality-aware log protection: it ensures that logs are synchronously protected in the audit device before an infrequent security-critical event is allowed to execute, but logs are asynchronously protected on frequent non-critical events to minimize performance overhead. Importantly, even on non-critical events, HARDLOG ensures bounded-asynchronous protection: it sends log entries to the audit device within a tiny, bounded delay from their creation using well-known real-time techniques. To demonstrate HARDLOG’S effectiveness, we prototyped an audit device using commodity components and implemented a reference audit system for Linux. Our prototype achieves a bounded protection delay of 15 milliseconds at non-critical events alongside undelayed protection at critical events. We also show that, for diverse real-world programs, HARDLOG incurs a geometric mean performance slowdown of only 6.3%, hence it is suitable for many real-world deployment scenarios.

With the development of embedded systems towards networking and intelligence, the security threats they face are becoming more difficult to prevent. Existing protection methods make it difficult to monitor jump instructions and their target addresses for tampering by attackers at the low hardware implementation overhead and performance overhead. In this paper, a hardware-assisted security monitoring module is designed to monitor the integrity of jump instructions and jump addresses when executing programs. The proposed method has been implemented on the Xilinx Kintex-7 FPGA platform. Experiments show that this method is able to effectively monitor tampering attacks on jump instructions as well as target addresses while the embedded system is executing programs.

In a traditional electronic records management system (ERMS), the legitimacy of the participants’ identities is verified by Certificate Authority (CA) certifications. The authentication process is complicated and takes up lots of memory. To overcome this problem, we construct a hierarchical electronic records management system by using a Hierarchical Identity-Based Cryptosystem (HIBC) to replace CA. However, there exist the threats of malicious behavior from a private key generator (PKG) or an entity in the upper layer because the private keys are generated by a PKG or upper entity in HIBC. Thus, we adopt distributed key generation protocols in HIBC to avoid the threats. Finally, we use blockchain technology in our system to achieve decentralized management.

With the development of network technology, identifying specific traffic has become important in network monitoring and security. However, designing feature sets that can accurately describe network traffic is still an urgent problem. Most of existing researches cannot realize effectively the identification of targets, and don't perform well in the complex and dynamic network environment. Aiming at these problems, we propose a novel method in this paper, which learns correlation features of network traffic based on the hierarchical structure. Firstly, the method learns the spatial-temporal features using convolutional neural networks (CNNs) and the bidirectional long short-term memory networks (Bi-LSTMs), then builds network topology to capture dependency characteristics between sessions and learns the context-related features through the graph attention networks (GATs). Finally, the network traffic session is classified using a fully connected network. The experimental results show that our method can effectively improve the detection ability and achieve a better classification performance overall.

Recently exposed vulnerabilities reveal the necessity to improve the security of branch predictors. Branch predictors record history about the execution of different processes, and such information from different processes are stored in the same structure and thus accessible to each other. This leaves the attackers with the opportunities for malicious training and malicious perception. Physical or logical isolation mechanisms such as using dedicated tables and flushing during context-switch can provide security but incur non-trivial costs in space and/or execution time. Randomization mechanisms incurs the performance cost in a different way: those with higher securities add latency to the critical path of the pipeline, while the simpler alternatives leave vulnerabilities to more sophisticated attacks.This paper proposes HyBP, a practical hybrid protection and effective mechanism for building secure branch predictors. The design applies the physical isolation and randomization in the right component to achieve the best of both worlds. We propose to protect the smaller tables with physically isolation based on (thread, privilege) combination; and protect the large tables with randomization. Surprisingly, the physical isolation also significantly enhances the security of the last-level tables by naturally filtering out accesses, reducing the information flow to these bigger tables. As a result, key changes can happen less frequently and be performed conveniently at context switches. Moreover, we propose a latency hiding design for a strong cipher by precomputing the "code book" with a validated, cryptographically strong cipher. Overall, our design incurs a performance penalty of 0.5% compared to 5.1% of physical isolation under the default context switching interval in Linux.

Existing anomaly detection models show success in detecting abnormal images with generative adversarial networks on the insufficient annotation of anomalous samples. However, existing models cannot accurately identify the anomaly samples which are close to the normal samples. We assume that the main reason is that these methods ignore the diversity of patterns in normal samples. To alleviate the above issue, this paper proposes a novel anomaly detection framework based on generative adversarial network, called ADe-GAN. More concretely, we construct a self-supervised learning task to fully explore the pattern information and latent representations of input images. In model inferring stage, we design a new abnormality score approach by jointly considering the pattern information and reconstruction errors to improve the performance of anomaly detection. Extensive experiments show that the ADe-GAN outperforms the state-of-the-art methods over several real-world datasets.

In this work, we conduct a systematic study on data poisoning attacks to Matrix Factorisation (MF) based Recommender Systems (RS) where a determined attacker injects fake users with false user-item feedback, with an objective to promote a target item by increasing its rating. We explore the capability of a MF based approach to reduce the impact of attack on targeted item in the system. We develop and evaluate multiple techniques to update the user and item feature matrices when incorporating new ratings. We also study the effectiveness of attack under increasing filler items and choice of target item.Our experimental results based on two real-world datasets show that the observations from the study could be used to design a more robust MF based RS.

Robustness verification of neural networks (NNs) is a challenging and significant problem, which draws great attention in recent years. Existing researches have shown that bound propagation is a scalable and effective method for robustness verification, and it can be implemented on GPUs and TPUs to get parallelized. However, the bound propagation methods naturally produce weak bound due to linear relaxations on the neurons, which may cause failure in verification. Although tightening techniques for simple ReLU networks have been explored, they are not applicable for NNs with general activation functions such as Sigmoid and Tanh. Improving robustness verification on these NNs is still challenging. In this paper, we propose a Branch-and-Bound (BaB) style method to address this problem. The proposed BaB procedure improves the weak bound by splitting the input domain of neurons into sub-domains and solving the corresponding sub-problems. We propose a generic heuristic function to determine the priority of neuron splitting by scoring the relaxation and impact of neurons. Moreover, we combine bound optimization with the BaB procedure to improve the weak bound. Experimental results demonstrate that the proposed method gains up to 35% improvement compared to the state-of-art CROWN method on Sigmoid and Tanh networks.

Exploring the efficient vulnerability scanning and detection technology of various tools is one fundamental aim of network security. This network security technique ameliorates the tremendous number of IoT security challenges and the threats they face daily. However, among various tools, Shodan Eye scanning technology has proven to be very helpful for network administrators and security personnel to scan, detect and analyze vulnerable ports and traffic in organizations' networks. This work presents a simulated network scanning activity and manual vulnerability analysis of an internet-connected industrial equipment of two chosen industrial networks (Industry A and B) by running Shodan on a virtually hosted (Oracle Virtual Box)-Linux-based operating system (Kali Linux). The result shows that the shodan eye is a a promising tool for network security and efficient vulnerability research.

Recently, in solving problems of sound radiation by systems of piezoceramic radiators, new approaches have emerged, which make it possible to significantly approximate the design parameters of systems to the actually measured ones. These approaches are associated with taking into account the specific features of these systems performing two functions - the function of converting electrical energy into acoustic energy and the function of forming the latter in the surrounding space. The peculiarity of the first function is the interconnection of the electric, mechanical and acoustic fields during energy conversion. The peculiarity of the second function is the interaction of the radiators in the system during the formation of its acoustic field. The aim of the work is to study the effect of acoustic interaction of cylindrical piezoceramic radiators in the composition of flat systems on their physical fields. Using the method of coupled fields in multiply connected domains, using the addition theorems for cylindrical wave functions, we obtain analytical relations that allow one to calculate the numerical results for the parameters of three interconnected physical fields that ensure the emission of sound by plane systems. Their analysis showed that with the radial symmetry of electrical excitation of cylindrical radiators, the conversion of electrical energy into mechanical energy is carried out on one - zero mode of oscillation. The placement of the radiators in the composition of the flat systems leads to the appearance of the effect of acoustic interaction between them in an external field, due to the multiple exchange of radiated and scattered waves. This effect destroys the radial symmetry of the acoustic loading of a single radiator. The violation of symmetry in the conversion of mechanical energy into acoustic energy leads to the appearance of oscillations that follow the zero mode. As a result, there is an effective redistribution of energy “pumped” into the radiators in the zero mode, between subsequent oscillations of the radiators. In turn, the emergence of new modes changes the acoustic field of a flat system. The results show the need to take into account the above features of the physical fields of the radiators in the composition of flat systems when choosing methods and developing methods for measuring field characteristics.

Chaotic cryptography is structurally related to the concepts of confusion and diffusion in traditional cryptography theory. Chaotic cryptography is formed by the inevitable connection between chaos theory and pure cryptography. In order to solve the shortcomings of the existing research on information encryption security system, this paper discusses the realization technology of information security, the design principles of encryption system and three kinds of chaotic mapping systems, and discusses the selection of development tools and programmable devices. And the information encryption security system based on chaos algorithm is designed and discussed, and the randomness test of three groups of encrypted files is carried out by the proposed algorithm and the AES (Advanced Encryption Standard) algorithm. Experimental data show that the uniformity of P-value value of chaos algorithm is 0.714 on average. Therefore, it is verified that the information encryption security system using chaos algorithm has high security.

Unmanned aerial vehicles (UAVs) and various network entities deployed on the ground can communicate with each other over the Internet of Drones (IoD), a network architecture designed expressly to allow communications between heterogenous entities. Drone technology has a wide range of uses, including on-demand package delivery, traffic and wild life surveillance, inspection of infrastructure and search, rescue and agriculture. However, IoD systems are vulnerable to numerous attacks, The main goal is to develop an all-encompassing security model that can be used to analyze security concerns in various UAV-based systems. With exceptional flexibility and increasing efficiency, trust management is a promising alternative to traditional detection methods. In a heterogeneous environment, it is also compatible with other security mechanisms. In this article, we present a fuzzy logic as an Insider Detection technique which calculate sensor data trust and assessing node behavior. To build confidence throughout the entire IoD, our proposal divides trust into two parts: Data trust and Node trust. This is in contrast to earlier models. Experimental results show that our solution is effective in terms of False positive ratio and Average of end-to-end delay.

This paper use the method of finite element analysis, and comparing and analyzing the split box and the integrated box from two aspects of modal analysis and static analysis. It is concluded that the integrated box has the characteristics of excellent vibration characteristics and high strength tolerance; At the same time, according to the S-N curve of the material and the load spectrum of the box, the fatigue life of the integrated box is 26.24 years by using the fatigue analysis software Fe-safe, which meets the service life requirements; The reliability analysis module PDS is used to calculate the reliability of the box, and the reliability of the integrated box is 96.5999%, which meets the performance requirements.