Biblio

Mortality from lung cancer has ranked high among cancers for many years. Early detection of lung cancer is critical for disease prevention, cure, and mortality rate reduction. Many existing detection methods on lung nodules can achieve high sensitivity but meanwhile introduce an excessive number of false-positive proposals, which is clinically unpractical. In this paper, we propose the multi-head detection and spatial attention network, shortly MHSnet, to address this crucial false-positive issue. Specifically, we first introduce multi-head detectors and skip connections to capture multi-scale features so as to customize for the variety of nodules in sizes, shapes, and types. Then, inspired by how experienced clinicians screen CT images, we implemented a spatial attention module to enable the network to focus on different regions, which can successfully distinguish nodules from noisy tissues. Finally, we designed a lightweight but effective false-positive reduction module to cut down the number of false-positive proposals, without any constraints on the front network. Compared with the state-of-the-art models, our extensive experimental results show the superiority of this MHSnet not only in the average FROC but also in the false discovery rate (2.64% improvement for the average FROC, 6.39% decrease for the false discovery rate). The false-positive reduction module takes a further step to decrease the false discovery rate by 14.29%, indicating its very promising utility of reducing distracted proposals for the downstream tasks relied on detection results.

Cyberattack on power system brings new challenges on the development of modern power system. Hackers may implement false data injection attack (FDIA) to cause unstable operating conditions of the power system. However, data from different power internet of things usually contains a lot of redundancy, making it difficult for current efficient discriminant model to precisely identify FDIA. To address this problem, we propose a deep learning network-based data fusion model to handle features from measurement data in power system. Proposed model includes a data enrichment module and a data fusion module. We firstly employ feature engineering technique to enrich features from power system operation in time dimension. Subsequently, a long short-term memory based autoencoder (LSTM-AE) is designed to efficiently avoid feature space explosion problem during data enriching process. Extensive experiments are performed on several classical attack detection models over the load data set from IEEE 14-bus system and simulation results demonstrate that fused data from proposed model shows higher detection accuracy with respect to the raw data.

In recent years, data security incidents caused by insider threats in distributed file systems have attracted the attention of academia and industry. The most common way to detect insider threats is based on user profiles. Through analysis, we realize that based on existing user profiles are not efficient enough, and there are many false positives when a stable user profile has not yet been formed. In this work, we propose personalized user profiles and design an insider threat detection framework, which can intelligently detect insider threats for securing distributed file systems in real-time. To generate personalized user profiles, we come up with a time window-based clustering algorithm and a weighted kernel density estimation algorithm. Compared with non-personalized user profiles, both the Recall and Precision of insider threat detection based on personalized user profiles have been improved, resulting in their harmonic mean F1 increased to 96.52%. Meanwhile, to reduce the false positives of insider threat detection, we put forward operation recommendations based on user similarity to predict new operations that users will produce in the future, which can reduce the false positive rate (FPR). The FPR is reduced to 1.54% and the false positive identification rate (FPIR) is as high as 92.62%. Furthermore, to mitigate the risks caused by inaccurate authorization for users, we present user tags based on operation content and permission. The experimental results show that our proposed framework can detect insider threats more effectively and precisely, with lower FPR and high FPIR.

New malware increasingly adopts novel fileless techniques to evade detection from antivirus programs. Process injection is one of the most popular fileless attack techniques. This technique makes malware more stealthy by writing malicious code into memory space and reusing the name and port of the host process. It is difficult for traditional security software to detect and intercept process injections due to the stealthiness of its behavior. We propose a novel framework called ProcGuard for detecting process injection behaviors. This framework collects sensitive function call information of typical process injection. Then we perform a fine-grained analysis of process injection behavior based on the function call chain characteristics of the program, and we also use the improved RCNN network to enhance API analysis on the tampered memory segments. We combine API analysis with deep learning to determine whether a process injection attack has been executed. We collect a large number of malicious samples with process injection behavior and construct a dataset for evaluating the effectiveness of ProcGuard. The experimental results demonstrate that it achieves an accuracy of 81.58% with a lower false-positive rate compared to other systems. In addition, we also evaluate the detection time and runtime performance loss metrics of ProcGuard, both of which are improved compared to previous detection tools.

With the explosive growth of the internet of things (IoT) devices, there are amount of data requirements and computing tasks. Fog computing network that could provide computing, caching and communication resources closer to IoT devices (ID) is considered as a potential solution to deal with the vast computing tasks. To improve the performance of the fog computing network while ensuring data security, blockchain technology is enabled and a location-based reliable sharding (LRS) algorithm is proposed, which jointly considers the optimal number of shards, the geographical location of fog nodes (FNs), and the number of nodes in each shard. Firstly, the reliable sharding result is based on the reputation values of FNs, which are related to the decision information and historical reputation value of FNs in the consensus process. Moreover, a reputation based PBFT consensus algorithm is adopted to accelerate the consensus process. Furthermore, the normalized entropy is used to estimate the proportion of malicious nodes and optimize the number of shards. Finally, simulation results show the effectiveness of the proposed scheme.

The smooth operation of metering equipment is inseparable from the monitoring and analysis of equipment alarm events by automated metering systems. With the generation of big data in power metering and the increasing demand for information security of metering systems in the power industry, how to use big data and protect data security at the same time has become a hot research field. In this paper, we propose a hybrid expert model based on federated learning to deal with the problem of alarm information analysis and identification. The hybrid expert system can divide the metering warning problem into multiple sub-problems for processing, which greatly improves the recognition and prediction accuracy. The experimental results show that our model has high accuracy in judging and identifying equipment faults.

Cryptocurrencies such as Bitcoin and Ethereum achieve decentralized payment by maintaining a globally distributed and append-only ledger. Recently, several researchers have sought to achieve privacy-preserving auditing, which is a crucial function for scenarios that require regulatory compliance, for decentralized payment systems. However, those proposed schemes usually cost much time for the cooperation between the auditor and the user due to leveraging complex cryptographic tools such as zero-knowledge proof. To tackle the problem, we present T-PPA, a privacy-preserving decentralized payment system, which provides customizable and efficient auditability by leveraging trusted execution environments (TEEs). T-PPA demands the auditor construct audit programs based on request and execute them in the TEE to protect the privacy of transactions. Then, identity-based encryption (IBE) is employed to construct the separation of power between the agency nodes and the auditor and to protect the privacy of transactions out of TEE. The experimental results show that T-PPA can achieve privacy-preserving audits with acceptable overhead.

The proliferation of real-time cyber-physical systems (CPS) is making profound changes to our daily life. Many real-time CPSs are security and safety-critical because of their continuous interactions with the physical world. While the general perception is that the security protection mechanism deployment is often absent in real-time embedded systems, there is no existing empirical study that measures the adoption of these mechanisms in the ecosystem. To bridge this gap, we conduct a measurement study for real-time embedded firmware from both a security perspective and a real-time perspective. To begin with, we collected more than 16 terabytes of embedded firmware and sampled 1,000 of them for the study. Then, we analyzed the adoption of security protection mechanisms and their potential impacts on the timeliness of real-time embedded systems. Besides, we measured the scheduling algorithms supported by real-time embedded systems since they are also security-critical.

Embedded systems involve an integration of a large number of intellectual property (IP) blocks to shorten chip's time to market, in which, many IPs are acquired from the untrusted third-party suppliers. However, existing IP trust verification techniques cannot provide an adequate security assurance that no hardware Trojan was implanted inside the untrusted IPs. Hardware Trojans in untrusted IPs may cause processor program execution failures by tampering instruction code and return address. Therefore, this paper presents a secure RISC-V embedded system by integrating a Security Monitoring Unit (SMU), in which, instruction integrity monitoring by the fine-grained program basic blocks and function return address monitoring by the shadow stack are implemented, respectively. The hardware-assisted SMU is tested and validated that while CPU executes a CoreMark program, the SMU does not incur significant performance overhead on providing instruction security monitoring. And the proposed RISC-V embedded system satisfies good balance between performance overhead and resource consumption.

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.

Archival services are one of the main functions of an information security management system for archival management, and the conversion and updating of archival intelligence services is an important means to meet the increasing diversity and wisdom of the age of intelligence. The purpose of this paper is to study an information security management system for archival management based on embedded artificial intelligence. The implementation of an embedded control management system for intelligent filing cabinets is studied. Based on a configurable embedded system security model, the access control process and the functional modules of the system based on a secure call cache are analysed. Software for wireless RF communication was designed, and two remote control options were designed using CAN technology and wireless RF technology. Tests have shown that the system is easy to use, feature-rich and reliable, and can meet the needs of different users for regular control of file room management.

Electronic control units (ECU) have been widely used in modern resource-constrained automotive systems, com-municating through the controller area network (CAN) bus. However, they are still facing man-in-the-middle attacks in CAN bus due to the absence of a more effective authenti-cation/encryption mechanism. In this paper, to defend against the attacks more effectively, we propose a unified lightweight authenticated encryption that integrates recent prevalent cryp-tography standardization Isap and Ascon.First, we reuse the common permutation block of ISAP and Asconto support authenticated encryption and encryption/decryption. Second, we provide a flexible and independent switch between authenticated encryption and encryption/decryption to support specific application requirements. Third, we adopt standard CAESAR hardware API as the interface standard to support compatibility between different interfaces or platforms. Experimental results show that our proposed unified lightweight authenticated encryption can reduce 26.09% area consumption on Xilinx Artix-7 FPGA board compared with the state-of-the-arts. In addition, the encryption overhead of the proposed design for transferring one CAN data frame is \textbackslashmathbf10.75 \textbackslashmu s using Asconand \textbackslashmathbf72.25 \textbackslashmu s using ISAP at the frequency of 4 MHz on embedded devices.

A randomly-selective encryption (RSE) algorithm is proposed for HEVC video bitstream in this paper. It is a pioneer algorithm with high efficiency and security. The encryption process is completely independent of video compression process. A randomly-selective sequence (RSS) based on the RC4 algorithm is designed to determine the extraction position in the video bitstream. The extracted bytes are encrypted by AES-CTR to obtain the encrypted video. Based on the high efficiency video coding (HEV C) bitstream, the simulation and analysis results show that the proposed RSE algorithm has low time complexity and high security, which is a promising tool for video cryptographic applications.

Technology is transforming rapidly. Security during data transmission is an increasingly critical and essential factor for the integrity and confidentiality of data in the financial domain, such as e-commerce transactions and bank transactions, etc. We cannot overestimate the importance of encryption/decryption of information in the digital economy. The need to strengthen and secure the digital economy is urgent. Cryptography maintains the security and integrity of data kept on computers and data communicated over the internet using encryption/decryption. A new concept in cryptography named DNA cryptography has attracted the interest of information security professionals. The DNA cryptography method hides data using a DNA sequence, with DNA encryption converting binary data into the DNA sequence. Deoxy Ribonucleic Acid (DNA) is a long polymer strand having nitrogen bases adenine (A), thymine (T), cytosine (C), and guanine (G), which play an important role in plain text encoding and decoding. DNA has high storage capacity, fast processing, and high computation capacity, and is more secure than other cryptography algorithms. DNA cryptography supports both symmetric and asymmetric cryptography. DNA cryptography can encrypt numeric values, English language and unicast. The main aim of this paper is to explain different aspects of DNA cryptography and how it works. We also compare different DNA algorithms/methods proposed in a previous paper, and implement DNA cryptography using one-time pad (OTP) and amino acid sequence using java language. OTP is used for symmetric key generation and the DNA sequence is converted to an amino acid sequence to create confusion.

Encryption defined as change information process (which called plaintext) into an unreadable secret format (which called ciphertext). This ciphertext could not be easily understood by somebody except authorized parson. Decryption is the process to converting ciphertext back into plaintext. Deoxyribonucleic Acid (DNA) based information ciphering techniques recently used in large number of encryption algorithms. DNA used as data carrier and the modern biological technology is used as implementation tool. New encryption algorithm based on DNA is proposed in this paper. The suggested approach consists of three steps (conventional, stream cipher and DNA) to get high security levels. The character was replaced by shifting depend character location in conventional step, convert to ASCII and AddRoundKey was used in stream cipher step. The result from second step converted to DNA then applying AddRoundKey with DNA key. The evaluation performance results proved that the proposed algorithm cipher the important data with high security levels.

To prevent all sorts of attacks, the technology of security service function chains (SFC) is proposed in recent years, it becomes an attractive research highlights. Dynamic orchestration algorithm can create SFC according to the resource usage of network security functions. The current research on creating SFC focuses on a single domain. However in reality the large and complex networks are divided into security domains according to different security levels and managed separately. Therefore, we propose a cross-security domain dynamic orchestration algorithm to create SFC for network security functions based on ant colony algorithm(ACO) and consider load balancing, shortest path and minimum delay as optimization objectives. We establish a network security architecture based on the proposed algorithm, which is suitable for the industrial vertical scenarios, solves the deployment problem of the dynamic orchestration algorithm. Simulation results verify that our algorithm achieves the goal of creating SFC across security domains and demonstrate its performance in creating service function chains to resolve abnormal traffic flows.

In this paper, the distributed security control problem of connected vehicle systems (CVSs) is investigated under denial of service (DoS) attacks and uncertain dynamics. DoS attacks usually block communication channels, resulting in the vehicle inability to receive data from the neighbors. In severe cases, it will affect the control performance of CVSs and even cause vehicle collision and life threats. In order to keep the vehicle platoon stable when the DoS attacks happen, we introduce a random characteristic to describe the impact of the packet loss behavior caused by them. Dependent on the length of the lost packets, we propose a security platoon control protocol to deal with it. Furthermore, the security platoon control problem of CVSs is transformed into a stable problem of Markov jump systems (MJSs) with uncertain parameters. Next, the Lyapunov function method and linear matrix inequations (LMI) are used to analyze the internal stability and design controller. Finally, several simulation results are presented to illustrate the effectiveness of the proposed method.

This contribution provides the implementation of a digital twin-based assistance system to be used in future control rooms. By applying parameter estimation methods, the dynamic model in the digital twin is an accurate representation of the physical system. Therefore, a dynamic security assessment (DSA) that is highly dependent on a correctly parameterized dynamic model, can give more reliable information to a system operator in the control room. The assistance system is studied on the Cigré TB 536 benchmark system with an obscured set of machine parameters. Through the proposed parameter estimation approach the original parameters could be estimated, changing, and increasing the statement of the DSA in regard to imminent instabilities.

The safety of the power system is inherently vital, due to the high risk of the electronic power system. In the wave of digitization in recent years, many power systems have been digitized to a certain extent. Under this circumstance, network security is particularly important, in order to ensure the normal operation of the power system. However, with the development of the Internet, network security issues are becoming more and more serious. Among all kinds of network attacks, the Distributed Denial of Service (DDoS) is a major threat. Once, attackers used huge volumes of traffic in short time to bring down the victim server. Now some attackers just use low volumes of traffic but for a long time to create trouble for attack detection. There are many methods for DDoS detection, but no one can fully detect it because of the huge volumes of traffic. In order to better detect DDoS and make sure the safety of electronic power system, we propose a novel detection method based on neural network. The proposed model and its service are deployed to the edge cloud, which can improve the real-time performance for detection. The experiment results show that our model can detect attacks well and has good real-time performance.

The object detection tasks based on edge computing have received great attention. A common concern hasn't been addressed is that edge may be unreliable and uploads the incorrect data to cloud. Existing works focus on the consistency of the transmitted data by edge. However, in cases when the inputs and the outputs are inherently different, the authenticity of data processing has not been addressed. In this paper, we first simply model the tampering detection. Then, bases on the feature insertion and game theory, the tampering detection and economic incentives mechanism (TDEI) is proposed. In tampering detection, terminal negotiates a set of features with cloud and inserts them into the raw data, after the cloud determines whether the results from edge contain the relevant information. The honesty incentives employs game theory to instill the distrust among different edges, preventing them from colluding and thwarting the tampering detection. Meanwhile, the subjectivity of nodes is also considered. TDEI distributes the tampering detection to all edges and realizes the self-detection of edge results. Experimental results based on the KITTI dataset, show that the accuracy of detection is 95% and 80%, when terminal's additional overhead is smaller than 30% for image and 20% for video, respectively. The interference ratios of TDEI to raw data are about 16% for video and 0% for image, respectively. Finally, we discuss the advantage and scalability of TDEI.

Edge detection is the key and difficult point of machine vision and image processing technology. The traditional edge detection algorithm is sensitive to noise and it is difficult to accurately extract the edge of the image, so the effect of image processing is not ideal. To solve this problem, people in the industry use the structural element features of morphological edge detection operator to extract the edge features of the image by carefully designing and combining the structural elements of different sizes and directions, so as to effectively ensure the integrity of edge information in all directions and eliminate large noise at the same time. This paper first introduces the traditional edge detection algorithms, then summarizes the edge detection algorithms based on mathematical morphology in recent years, finds that the selection of multi-scale and multi-directional structural elements is an important research direction, and finally discusses the development trend of mathematical morphology edge detection technology.

Frauds in supply chain finance not only result in substantial loss for financial institutions (e.g., banks, trust company, private funds), but also are detrimental to the reputation of the ecosystem. However, such frauds are hard to detect due to the complexity of the operating environment in supply chain finance such as involvement of multiple parties under different agreements. Traditional instruments of financial institutions are time-consuming yet insufficient in countering fraudulent supply chain financing. In this study, we propose a novel blockchain-driven framework for deterring fraud in supply chain finance. Specifically, we use inventory financing in jewelry supply chain as an illustrative scenario. The blockchain technology enables secure and trusted data sharing among multiple parties due to its characteristics of immutability and traceability. Consequently, information on manufacturing, brand license, and warehouse status are available to financial institutions in real time. Moreover, we develop a novel rule-based fraud check module to automatically detect suspicious fraud cases by auditing documents shared by multiple parties through a blockchain network. To validate the effectiveness of the proposed framework, we employ agent-based modeling and simulation. Experimental results show that our proposed framework can effectively deter fraudulent supply chain financing as well as improve operational efficiency.

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.

Nowadays, although it is much more convenient to obtain news with social media and various news platforms, the emergence of all kinds of fake news has become a headache and urgent problem that needs to be solved. Currently, the fake news recognition algorithm for fake news mainly uses GCN, including some other niche algorithms such as GRU, CNN, etc. Although all fake news verification algorithms can reach quite a high accuracy with sufficient datasets, there is still room for improvement for unsupervised learning and semi-supervised. This article finds that the accuracy of the GCN method for fake news detection is basically about 85% through comparison with other neural network models, which is satisfactory, and proposes that the current field lacks a unified training dataset, and that in the future fake news detection models should focus more on semi-supervised learning and unsupervised learning.

A digital signature is a type of asymmetric cryptography that is used to ensure that the recipient receives the actual received message from the intended sender. Problems that often arise conventionally when requiring letter approval from the authorized official, and the letter concerned is very important and urgent, often the process of giving the signature is hampered because the official concerned is not in place. With these obstacles, the letter that should be distributed immediately becomes hampered and takes a long time in terms of signing the letter. The purpose of this study is to overcome eavesdropping and data exchange in sending data using Digital Signature as authentication of data authenticity and minimizing fake signatures on letters that are not made and authorized by relevant officials based on digital signatures stored in the database. This research implements the Rivest Shamir Adleman method. (RSA) as outlined in an application to provide authorization or online signature with Digital Signature. The results of the study The application of the Rivest Shamir Adleman (RSA) algorithm can run on applications with the Digital Signature method based on ISO 9126 testing by expert examiners, and the questionnaire distributed to users and application operators obtained good results from an average value of 79.81 based on the scale table ISO 9126 conversion, the next recommendation for encryption does not use MD5 but uses Bcrypt secure database to make it stronger.