Biblio

Artificial intelligence (AI) has been successfully employed in industries for decades, beginning with the invention of expert systems in the 1960s and continuing through the present ubiquity of deep learning. Data-driven AI solutions have grown increasingly common as a means of supporting ever-more complicated industrial processes owing to the accessibility of affordable computer and storage infrastructure. Despite recent optimism, implementing AI to smart industrial applications still offers major difficulties. The present paper gives an executive summary of AI methodologies with an emphasis on deep learning before detailing unresolved issues in AI safety, data privacy, and data quality — all of which are necessary for completely automated commercial AI systems.

Vulnerability assessment is an important process for network security. However, most commonly used vulnerability assessment methods still rely on expert experience or rule-based automated scripts, which are difficult to meet the security requirements of increasingly complex network environment. In recent years, although scientists and engineers have made great progress on artificial intelligence in both theory and practice, it is a challenging to manufacture a mature high-quality intelligent products in the field of network security, especially in penetration testing based vulnerability assessment for enterprises. Therefore, in order to realize the intelligent penetration testing, Vul.AI with its rich experience in cyber attack and defense for many years has designed and developed a set of intelligent penetration and attack simulation system Ai.Scan, which is based on attack chain, knowledge graph and related evaluation algorithms. In this paper, the realization principle, main functions and application scenarios of Ai.Scan are introduced in detail.

In the process of compiling the power-cut window period of the power grid equipment maintenance plan, problems such as omission of constraints are prone to occur due to excessive reliance on manual experience. In response to these problems, this paper proposes a method for mining key features of the power-cut window based on grey relational analysis. Through mining and analysis of the historical operation data of the power grid, the operation data of new energy, and the historical power-cut information of equipment, the indicators that play a key role in the arrangement of the outage window period of the equipment maintenance plan are found. Then use the key indicator information to formulate the window period. By mining the relationship between power grid operation data and equipment power outages, this paper can give full play to the big data advantages of the power grid, improve the accuracy and efficiency of the power-cut window period.

This paper proposes a magnetic actuator using a partially magnetized FePt thick film as a permanent magnet and membrane material for bi-directional micropumps. The magnetized areas act as flux sources, while the magnetized and unmagnetized areas play a role of the membrane part. The mechanical and magnetic characterization results show FePt has a large tensile strength and a lower Young’s modulus than Si crystal, and a comparable remanence to NdFeB. A magnetic pattern transfer technique with a post thermal demagnetization is proposed and experimentally verified to magnetize the FePt partially. Using the proposed magnetic actuator with partially magnetized FePt film is beneficial to simplify the complicated structure and fabrication process of the bi-directional magnetic micropump besides other magnetic MEMS devices.

Healthcare sectors such as hospitals, nursing homes, medical offices, and hospice homes encountered several obstacles due to the outbreak of Covid-19. Wearing a mask, social distancing and sanitization are some of the most effective methods that have been proven to be essential to minimize the virus spread. Lately, medical executives have been appointed to monitor the virus spread and encourage the individuals to follow cautious instructions that have been provided to them. To solve the aforementioned challenges, this research study proposes an autonomous medical assistance robot. The proposed autonomous robot is completely service-based, which helps to monitor whether or not people are wearing a mask while entering any health care facility and sanitizes the people after sending a warning to wear a mask by using the image processing and computer vision technique. The robot not only monitors but also promotes social distancing by giving precautionary warnings to the people in healthcare facilities. The robot can assist the health care officials carrying the necessities of the patent while following them for maintaining a touchless environment. With thorough simulative testing and experiments, results have been finally validated.

With the increasing number of distributed energy sources and the growing demand for free exchange of energy, Energy internet (EI) is confronted with great challenges of persistent connection, stable transmission, real-time interaction, and security. The new definition of metaverse in the EI field is proposed as a potential solution for these challenges by establishing a massive and comprehensive fusion 3D network, which can be considered as the advanced stage of EI. The main characteristics of the metaverse such as reality to virtualization, interaction, persistence, and immersion are introduced. Specifically, we present the key enabling technologies of the metaverse including virtual reality, artificial intelligence, blockchain, and digital twin. Meanwhile, the potential applications are presented from the perspectives of immersive user experience, virtual power station, management, energy trading, new business, device maintenance. Finally, some challenges of metaverse in EI are concluded.

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.

We show that the most common flavors of noisy leakage can be simulated in the information-theoretic setting using a single query of bounded leakage, up to a small statistical simulation error and a slight loss in the leakage parameter. The latter holds true in particular for one of the most used noisy-leakage models, where the noisiness is measured using the conditional average min-entropy (Naor and Segev, CRYPTO’09 and SICOMP’12). Our reductions between noisy and bounded leakage are achieved in two steps. First, we put forward a new leakage model (dubbed the dense leakage model) and prove that dense leakage can be simulated in the information-theoretic setting using a single query of bounded leakage, up to small statistical distance. Second, we show that the most common noisy-leakage models fall within the class of dense leakage, with good parameters. Third, we prove lower bounds on the amount of bounded leakage required for simulation with sub-constant error, showing that our reductions are nearly optimal. In particular, our results imply that useful general simulation of noisy leakage based on statistical distance and mutual information is impossible. We also provide a complete picture of the relationships between different noisy-leakage models. Our result finds applications to leakage-resilient cryptography, where we are often able to lift security in the presence of bounded leakage to security in the presence of noisy leakage, both in the information-theoretic and in the computational setting. Remarkably, this lifting procedure makes only black-box use of the underlying schemes. Additionally, we show how to use lower bounds in communication complexity to prove that bounded-collusion protocols (Kumar, Meka, and Sahai, FOCS’19) for certain functions do not only require long transcripts, but also necessarily need to reveal enough information about the inputs.

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.

This paper studies Distributed Denial of Service (DDoS) attack detection by adopting the Deep Neural Network (DNN) model in Software Defined Networking (SDN). We first deploy the flow collector module to collect the flow table entries. Considering the detection efficiency of the DNN model, we also design some features manually in addition to the features automatically obtained by the flow table. Then we use the preprocessed data to train the DNN model and make a prediction. The overall detection framework is deployed in the SDN controller. The experiment results illustrate DNN model has higher accuracy in identifying attack traffic than machine learning algorithms, which lays a foundation for the defense against DDoS attack.

CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) is a widely used technology to distinguish real users and automated users such as bots. However, the advance of AI technologies weakens many CAPTCHA tests and can induce security concerns. In this paper, we propose a user-friendly text-based CAPTCHA generation method named Robust Text CAPTCHA (RTC). At the first stage, the foregrounds and backgrounds are constructed with font and background images respectively sampled from font and image libraries, and they are then synthesized into identifiable pseudo adversarial CAPTCHAs. At the second stage, we utilize a highly transferable adversarial attack designed for text CAPTCHAs to better obstruct CAPTCHA solvers. Our experiments cover comprehensive models including shallow models such as KNN, SVM and random forest, as well as various deep neural networks and OCR models. Experiments show that our CAPTCHAs have a failure rate lower than one millionth in general and high usability. They are also robust against various defensive techniques that attackers may employ, including adversarially trained CAPTCHA solvers and solvers trained with collected RTCs using manual annotation. Codes available at https://github.com/RulinShao/RTC.

Internet of Things (IoT) is widely present nowadays, from businesses to connected houses, and more. IoT is considered a part of the Internet of the future and will comprise billions of intelligent communication. These devices transmit data from sensors to entities like servers to perform suitable responses. The problem of securing these data from cyberattacks increases due to the sensitive information it contains. In addition, studies have shown that most of the time data transiting in IoT devices does not apply encrypted communication. Thus, anyone has the ability to listen to or modify the information. Encrypting communications seems mandatory to secure networks and data transiting from sensors to servers. In this paper, we propose an approach to secure the transmission and the storage of data in IoT using Elliptic Curve Cryptography (ECC). The proposed method offers a high level of security at a reasonable computational cost. Indeed, we present an adequate architecture that ensures the use of a state-of-the-art cryptography algorithm to encrypt sensitive data in IoT.

Over the years, false news has polluted the online media landscape across the world. In this “post-truth” era, the narratives created by false news have now come into fruition through dismantled democracies, disbelief in science, and hyper-polarized societies. Despite increased efforts in fact-checking & labeling, strengthening detection systems, de-platforming powerful users, promoting media literacy and awareness of the issue, false news continues to be spread exponentially. This study models the behaviors of both the victims of false news and the platform in which it is spread— through the system dynamics methodology. The model was used to develop a policy design by evaluating existing and proposed solutions. The results recommended actively countering confirmation bias, restructuring social networking sites’ recommendation algorithms, and increasing public trust in news organizations.

Code-reuse attacks (including ROP/JOP) severely threaten computer security. Control-flow integrity (CFI), which can restrict control flow in legal scope, is recognised as an effective defence mechanism against code-reuse attacks. Hardware-based CFI uses Instruction Set Architecture (ISA) extensions with additional hardware modules to implement CFI and achieve better performance. However, hardware-based fine-grained CFI adds new instructions to the ISA, which can not be executed on old processors and breaks the compatibility of programs. Some coarse-grained CFI designs, such as Intel IBT, maintain the compatibility of programs but can not provide enough security guarantees.To balance the security and compatibility of hardware CFI, we propose Transparent Forward CFI (TFCFI). TFCFI implements hardware-based fine-grained CFI designs without changing the ISA. The software modification of TFCFI utilizes address information and hint instructions in RISC-V as transparent labels to mark the program. The hardware module of TFCFI monitors the control flow during execution. The program modified by TFCFI can be executed on old processors without TFCFI. Benefiting from transparent labels, TFCFI also solves the destination equivalence problem. The experiment on FPGA shows that TFCFI incurs negligible performance overhead (1.82% on average).

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.

This paper proposes a vehicle violation determination system based on improved YOLOv5 algorithm, which performs vehicle violation determination on a single unit at a single intersection, and displays illegal photos and license plates of illegal vehicles on the webpage. Using the network structure of YOLOv5, modifying the vector output of the Head module, and modifying the rectangular frame detection of the target object to quadrilateral detection, the system can identify vehicles and lane lines with more flexibilities.

The major aim of the study is to predict the type of crime that is going to happen based on the crime hotspot detected for the given crime data with engineered spatial features. crime dataset is filtered to have the following 2 crime categories: crime against society, crime against person. Crime hotspots are detected by using the Novel Hierarchical density based Spatial Clustering of Application with Noise (HDBSCAN) Algorithm with the number of clusters optimized using silhouette score. The sample data consists of 501 crime incidents. Future types of crime for the given location are predicted by using the Support Vector Machine (SVM) and Convolutional Neural Network (CNN) algorithms (N=5). The accuracy of crime prediction using Support Vector Machine classification algorithm is 94.01% and Convolutional Neural Network algorithm is 79.98% with the significance p-value of 0.033. The Support Vector Machine algorithm is significantly better in accuracy for prediction of type of crime than Convolutional Neural Network (CNN).

Edge detection based embedding techniques are famous for data security and image quality preservation. These techniques use diverse edge detectors to classify edge and non-edge pixels in an image and then implant secrets in one or both of these classes. Image with conceived data is called stego image. It is noticeable that none of such researches tries to reform the original image from the stego one. Rather, they devote their concentration to extract the hidden message only. This research presents a solution to the raised reversibility problem. Like the others, our research, first, applies an edge detector e.g., canny, in a cover image. The scheme next collects \$n\$-LSBs of each of edge pixels and finally, concatenates them with encrypted message stream. This method applies a lossless compression algorithm to that processed stream. Compression factor is taken such a way that the length of compressed stream does not exceed the length of collected LSBs. The compressed message stream is then implanted only in the edge pixels by \$n\$-LSB substitution method. As the scheme does not destroy the originality of non-edge pixels, it presents better stego quality. By incorporation the mechanisms of encryption, concatenation, compression and \$n\$-LSB, the method has enriched the security of implanted data. The research shows its effectiveness while implanting a small sized message.

Cybersecurity is important in the field of information technology. One most recent pressing issue is information security. When we think of cybersecurity, the first thing that comes to mind is cyber-attacks, which are on the rise, such as Ransomware. Various governments and businesses take a variety of measures to combat cybercrime. People are still concerned about ransomware, despite numerous cybersecurity precautions. In ransomware, the attacker encrypts the victim’s files/data and demands payment to unlock the data. Cybersecurity is a collection of tools, regulations, security guards, security ideas, guidelines, risk management, activities, training, insurance, best practices, and technology used to secure the cyber environment, organization, and user assets. This paper analyses ransomware attacks, techniques for dealing with these attacks, and future challenges.

Scan-based test methodology is one of the most popular test techniques in VLSI circuits. This methodology increases the testability which in turn improves the fault coverage. For this purpose, the technique uses a chain of scan cells. This becomes a source of attack for an attacker who can observe / control the internal states and use the information for malicious purposes. Hence, security becomes the main concern in the Integrated Circuit (IC) domain since scan chains are the main reason for leakage of confidential information during testing phase. These leakages will help attackers in reverse engineering. Measures against such attacks have to be taken by encrypting the data which flows through the scan chains. Lightweight ciphers can be used for scan chain encryption. In this work, encryption of scan data is done for ISCAS-89 benchmarks and the performance and security properties are evaluated. Lightweight stream and block ciphers are used to perform scan encryption. A comparative analysis between the two techniques is performed in par with the functions related to design cost and security properties.

This paper analyzes the problems existing in the existing emergency management technology system in China from various perspectives, and designs the construction of intelligent emergency system in combination with the development of new generation of Internet of Things, big data, cloud computing and artificial intelligence technology. The overall design is based on scientific and technological innovation to lead the reform of emergency management mechanism and process reengineering to build an intelligent emergency technology system characterized by "holographic monitoring, early warning, intelligent research and accurate disposal". To build an intelligent emergency management system that integrates intelligent monitoring and early warning, intelligent emergency disposal, efficient rehabilitation, improvement of emergency standards, safety and operation and maintenance construction.

Based on the campus wireless IPv6 network system, using WiFi contactless sensing and positioning technology and action recognition technology, this paper designs a new campus security early warning system. The characteristic is that there is no need to add new monitoring equipment. As long as it is the location covered by the wireless IPv6 network, personnel quantity statistics and personnel body action status display can be realized. It plays an effective monitoring supplement to the places that cannot be covered by video surveillance in the past, and can effectively prevent campus violence or other emergencies.

Data poisoning is a type of adversarial attack on training data where an attacker manipulates a fraction of data to degrade the performance of machine learning model. There are several known defensive mechanisms for handling offline attacks, however defensive measures for online learning, where data points arrive sequentially, have not garnered similar interest. In this work, we propose a defense mechanism to minimize the degradation caused by the poisoned training data on a learner's model in an online setup. Our proposed method utilizes an influence function which is a classic technique in robust statistics. Further, we supplement it with the existing data sanitization methods for filtering out some of the poisoned data points. We study the effectiveness of our defense mechanism on multiple datasets and across multiple attack strategies against an online learner.

The Internet of Things devices is rapidly becoming widespread, as are IoT services. Their achievement has not gone unnoticed, as threats as well as attacks towards IoT devices as well as services continue to grow. Cyber attacks are not unique to IoT, however as IoT becomes more ingrained in our lives as well as communities, it is imperative to step up as well as take cyber defense seriously. As a result, there is a genuine need to protect IoT, which necessitates a thorough understanding of the dangers and attacks against IoT infrastructure. The purpose of this study is to define threat types, as well as to assess and characterize intrusions and assaults against IoT devices as well as services

Cloud Computing revolutionize the usage of Internet of Things enabled devices integrated via Internet. Providing everything in an outsourced fashion, Cloud also lends infrastructures such as storage. Though cloud makes it easy for us to store and access the data faster and easier, yet there exist various security and privacy risks. Such issues if not handled may become more threatening as it could even disclose the privacy of an individual/ organization. Strengthening the security of data is need of the hour. The work proposes a novel architecture enhancing the security of Cloud data in an IoT integrated environment. In order to enhance the security, systematic use of a modified hybrid mechanism based on DNA code and Elliptic Curve Cryptography along with Third Party Audit is proposed. The performance of the proposed mechanism has been analysed. The results ensures that proposed IoT Cloud architecture performs better while providing strong security which is the major aspect of the work.