Biblio

With the rapid development of Internet Technology in recent years, the demand for security support for complex applications is becoming stronger and stronger. Intel Software Guard Extensions (Intel SGX) is created as an extension of Intel Systems to enhance software security. Intel SGX allows application developers to create so-called enclave. Sensitive application code and data are encapsulated in Trusted Execution Environment (TEE) by enclave. TEE is completely isolated from other applications, operating systems, and administrative programs. Enclave is the core structure of Intel SGX Technology. Enclave supports multi-threading. Thread Control Structure (TCS) stores special information for restoring enclave threads when entering or exiting enclave. Each execution thread in enclave is associated with a TCS. This paper analyzes and verifies the possible security risks of enclave under concurrent conditions. It is found that in the case of multithread concurrency, a single enclave cannot resist flooding attacks, and related threads also throw TCS exception codes.

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.

Databases are at the heart of modern applications and any threats to them can seriously endanger the safety and functionality of applications relying on the services offered by a DBMS. It is therefore pertinent to identify key risks to the secure operation of a database system. This paper identifies the key risks, namely, SQL injection, weak audit trails, access management issues and issues with encryption. A malicious actor can get help from any of these issues. It can compromise integrity, availability and confidentiality of the data present in database systems. The paper also identifies various means and ways to defend against these issues and remedy them. This paper then proceeds to identify from the literature, the potential solutions to these ameliorate the threat from these vulnerabilities. It proposes the usage of encryption to protect the data from being breached and leveraging encrypted databases such as CryptoDB. Better access control norms are suggested to prevent unauthorized access, modification and deletion of the data. The paper also recommends ways to prevent SQL injection attacks through techniques such as prepared statements.

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.

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.

Protection of private and sensitive information is the most alarming issue for security providers in surveillance videos. So to provide privacy as well as to enhance secrecy in surveillance video without affecting its efficiency in detection of violent activities is a challenging task. Here a steganography based algorithm has been proposed which hides private information inside the surveillance video without affecting its accuracy in criminal activity detection. Preprocessing of the surveillance video has been performed using Tunable Q-factor Wavelet Transform (TQWT), secret data has been hidden using Discrete Wavelet Transform (DWT) and after adding payload to the surveillance video, detection of criminal activities has been conducted with maintaining same accuracy as original surveillance video. UCF-crime dataset has been used to validate the proposed framework. Feature extraction is performed and after feature selection it has been trained to Temporal Convolutional Network (TCN) for detection. Performance measure has been compared to the state-of-the-art methods which shows that application of steganography does not affect the detection rate while preserving the perceptual quality of the surveillance video.

Classifying and predicting the accuracy of intrusion detection on cybercrime by comparing machine learning methods such as Innovative Decision Tree (DT) with Support Vector Machine (SVM). By comparing the Decision Tree (N=20) and the Support Vector Machine algorithm (N=20) two classes of machine learning classifiers were used to determine the accuracy. The decision Tree (99.19%) has the highest accuracy than the SVM (98.5615%) and the independent T-test was carried out (=.507) and shows that it is statistically insignificant (p\textgreater0.05) with a confidence value of 95%. by comparing Innovative Decision Tree and Support Vector Machine. The Decision Tree is more productive than the Support Vector Machine for recognizing intruders with substantially checked, according to the significant analysis.

Improving the accuracy of intruders in innovative Intrusion detection by comparing Machine Learning classifiers such as Random Forest (RF) with Support Vector Machine (SVM). Two groups of supervised Machine Learning algorithms acquire perfection by looking at the Random Forest calculation (N=20) with the Support Vector Machine calculation (N=20)G power value is 0.8. Random Forest (99.3198%) has the highest accuracy than the SVM (9S.56l5%) and the independent T-test was carried out (=0.507) and shows that it is statistically insignificant (p \textgreater0.05) with a confidence value of 95% by comparing RF and SVM. Conclusion: The comparative examination displays that the Random Forest is more productive than the Support Vector Machine for identifying the intruders are significantly tested.

Aim: Object Detection is one of the latest topics in today’s world for detection of real time objects using Deep Belief Networks. Methods & Materials: Real-Time Object Detection is performed using Deep Belief Networks (N=24) over Convolutional Neural Networks (N=24) with the split size of training and testing dataset 70% and 30% respectively. Results: Deep Belief Networks has significantly better accuracy (81.2%) compared to Convolutional Neural Networks (47.7%) and attained significance value of p = 0.083. Conclusion: Deep Belief Networks achieved significantly better object detection than Convolutional Neural Networks for identifying real-time objects in traffic surveillance.

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.

This paper proposes a RSSI-based routing protocol for smart pole mesh networks equipped with multiple IEEE 802.11p/WAVE radios. In the IEEE 802.11p based multi-radio multi-channel environments, the performance of traditional mesh routing protocols is severely degraded because of metric measurement overhead. The periodic probe messages for measuring the quality of each channel incurs a large overhead due to the channel switching delay. To solve such an overhead problem, we introduce a routing metric that estimates expected transmission time and proposes a light-weight channel allocation algorithm based on RSSI value only. We evaluate the performance of the proposed solution through simulation experiments with NS-3. Simulation results show that it can improve the network performance in terms of latency and throughput, compared to the legacy WCETT routing scheme.

Autonomous vehicles (AVs) are capable of making driving decisions autonomously using multiple sensors and a complex autonomous driving (AD) software. However, AVs introduce numerous unique security challenges that have the potential to create safety consequences on the road. Security mechanisms require a benchmark suite and an evaluation framework to generate comparable results. Unfortunately, AVs lack a proper benchmarking framework to evaluate the attack and defense mechanisms and quantify the safety measures. This paper introduces BenchAV – a security benchmark suite and evaluation framework for AVs to address current limitations and pressing challenges of AD security. The benchmark suite contains 12 security and performance metrics, and an evaluation framework that automates the metric collection process using Carla simulator and Robot Operating System (ROS).

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.

Under the new situation of China's new infrastructure and digital transformation and upgrading, large IT companies such as the United States occupy the market of key information infrastructure components in important fields such as power and energy in China, which makes the risk of key information infrastructure in China's power enterprises become more and more prominent. In the power Internet of Things environment where everything is connected, the back doors and loopholes of basic software and hardware caused by the supply chain risks of key information infrastructure have broken through the foundation of power cyber-security and information security defense, and the security risk management of power key information infrastructure cyber-security has become urgent. Therefore, this paper studies the construction of the cyber-security management framework of key information infrastructure suitable for electric power enterprises, and defines the security risk assessment norms of each link of equipment access to the network. Implement the national cyber-security requirements, promote the cyber-security risk controllable assessment service of key information infrastructure, improve the security protection level of power grid information system from the source, and promote the construction and improvement of the network and information security system of power industry.

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.

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

With the rapid development of information science and technology, the role of the Internet in daily life is becoming more and more important, but while bringing speed and convenience to the experience, network security issues are endless, and fighting cybercrime will be an eternal topic. In recent years, new types of cyberattacks have made defense and analysis difficult. For example, the memory of network attacks makes some key array evidence only temporarily exist in physical memory, which puts forward higher requirements for attack detection. The traditional memory forensic analysis method for persistent data is no longer suitable for a new type of network attack analysis. The continuous development of memory forensics gives people hope. This paper proposes a network attack detection model based on memory forensic analysis to detect whether the system is under attack. Through experimental analysis, this model can effectively detect network attacks with low overhead and easy deployment, providing a new idea for network attack detection.

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.

In this cyber era, the number of cybercrime problems grows significantly, impacting network communication security. Some factors have been identified, such as malware. It is a malicious code attack that is harmful. On the other hand, a botnet can exploit malware to threaten whole computer networks. Therefore, it needs to be handled appropriately. Several botnet activity detection models have been developed using a classification approach in previous studies. However, it has not been analyzed about selecting features to be used in the learning process of the classification algorithm. In fact, the number and selection of features implemented can affect the detection accuracy of the classification algorithm. This paper proposes an analysis technique for determining the number and selection of features developed based on previous research. It aims to obtain the analysis of using features. The experiment has been conducted using several classification algorithms, namely Decision tree, k-NN, Naïve Bayes, Random Forest, and Support Vector Machine (SVM). The results show that taking a certain number of features increases the detection accuracy. Compared with previous studies, the results obtained show that the average detection accuracy of 98.34% using four features has the highest value from the previous study, 97.46% using 11 features. These results indicate that the selection of the correct number and features affects the performance of the botnet detection model.

In the context of cybersecurity systems, trust is the firm belief that a system will behave as expected. Trustworthiness is the proven property of a system that is worthy of trust. Therefore, trust is ephemeral, i.e. trust can be broken; trustworthiness is perpetual, i.e. trustworthiness is verified and cannot be broken. The gap between these two concepts is one which is, alarmingly, often overlooked. In fact, the pressure to meet with the pace of operations for mission critical cross domain solution (CDS) development has resulted in a status quo of high-risk, ad hoc solutions. Trustworthiness, proven through formal verification, should be an essential property in any hardware and/or software security system. We have shown, in "vCDS: A Virtualized Cross Domain Solution Architecture", that developing a formally verified CDS is possible. virtual CDS (vCDS) additionally comes with security guarantees, i.e. confidentiality, integrity, and availability, through the use of a formally verified trusted computing base (TCB). In order for a system, defined by an architecture description language (ADL), to be considered trustworthy, the implemented security configuration, i.e. access control and data protection models, must be verified correct. In this paper we present the first and only security auditing tool which seeks to verify the security configuration of a CDS architecture defined through ADL description. This tool is useful in mitigating the risk of existing solutions by ensuring proper security enforcement. Furthermore, when coupled with the agile nature of vCDS, this tool significantly increases the pace of system delivery.

A botnet is a new type of attack method developed and integrated on the basis of traditional malicious code such as network worms and backdoor tools, and it is extremely threatening. This course combines deep learning and neural network methods in machine learning methods to detect and classify the existence of botnets. This sample does not rely on any prior features, the final multi-class classification accuracy rate is higher than 98.7%, the effect is significant.

Facial emotion recognition helps feed the growth of the future artificial intelligence with the development of emotion recognition, learning, and analysis of different angles of a human face and head pose. The world's recent pandemic gave rise to the rapid installment of facial recognition for fewer applications, while emotion recognition is still within the experimental boundaries. The current challenges encountered with facial emotion recognition (FER) are the difference between background noises. Since today's world shows us that humans soon need robotics in the most significant role of human perception, attention, memory, decision-making, and human-robot interaction (HRI) needs employees. By merging the head pose as a combination towards the FER to boost the robustness in understanding emotions using the convolutional neural networks (CNN). The stochastic gradient descent with a comprehensive model is adopted by applying multi-task learning capable of implicit parallelism, inherent and better global optimizer in finding better network weights. After executing a multi-task learning model using two independent datasets, the experiment with the FER and head pose learning multi-views co-regularization frameworks were subsequently merged with validation accuracy.

Facial expression intensity, which quantifies the degree of facial expression, has been proposed. It is calculated based on how much facial feature values change compared to an expressionless face. The estimation has two aspects. One is to classify facial expressions, and the other is to estimate their intensity. However, it is difficult to do them at the same time. There- fore, in this work, the estimation of intensity and the classification of expression are separated. We suggest an explicit method and an implicit method. In the explicit one, a classifier determines which types of expression the inputs are, and each regressor determines its intensity. On the other hand, in the implicit one, we give zero values or non-zero values to regressors for each type of facial expression as ground truth, depending on whether or not an input image is the correct facial expression. We evaluated the two methods and, as a result, found that they are effective for facial expression recognition.

In recent years, radar automatic target recognition (RATR) technology based on high-resolution range profile (HRRP) has received extensive attention in various fields. However, insufficient data on non-cooperative targets seriously affects recognition performance of this technique. For HRRP target recognition under few-shot condition, we proposed a novel gaussian deep belief network based on model-agnostic meta-learning (GDBN-MAML). In the proposed method, GDBN allowed real-value data to be transmitted over the entire network, which effectively avoided feature loss due to binarization requirements of conventional deep belief network (DBN) for data. In addition, we optimized the initial parameters of GDBN by multi-task learning based on MAML. In this way, the number of training samples required by the model for new recognition tasks could be reduced. We applied the proposed method to the HRRP recognition experiments of 3 types of 3D simulated aircraft models. The experimental results showed that the proposed method had higher recognition accuracy and generalization performance under few-shot condition compared with conventional deep learning methods.