Biblio

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

In most PUF-based authentication schemes, a central server is usually engaged to verify the response of the device’s PUF to challenge bit-streams. However, the server availability may be intermittent in practice. To tackle such an issue, this paper proposes a new protocol for supporting distributed authentication while avoiding vulnerability to information leakage where CRPs could be retrieved from hacked devices and collectively used to model the PUF. The main idea is to provision for scrambling the challenge bit-stream in a way that is dependent on the verifier. The scrambling pattern varies per authentication round for each device and independently across devices. In essence, the scrambling function becomes node- and packetspecific and the response received by two verifiers of one device for the same challenge bit-stream could vary. Thus, neither the scrambling function can be reverted, nor the PUF can be modeled even by a collusive set of malicious nodes. The validation results using data of an FPGA-based implementation demonstrate the effectiveness of our approach in thwarting PUF modeling attacks by collusive actors. We also discuss the approach resiliency against impersonation, Sybil, and reverse engineering attacks.

This paper is concered with the nonlinear cyber physical system (CPS) with uncertain parameters under false data injection (FDI) attacks. The interval type-2 (IT2) fuzzy model is utilized to approximate the nonlinear system, then the nonlinear system can be represented as a convex combination of linear systems. To detect the FDI attacks, a novel robust fuzzy extended state observer with H∞ preformance is proposed, where the fuzzy rules are utilized to the observer to estimate the FDI attacks. Utilizing the observation of the FDI attacks, a security control scheme is proposed in this paper, in which a compensator is designed to offset the FDI attacks. Simulation examples are given to illustrate the effecitveness of the proposed security scheme.

One of the major concerns in the real-time monitoring systems in a smart grid is the Cyber security threat. The false data injection attack is emerging as a major form of attack in Cyber-Physical Systems (CPS). A False data Injection Attack (FDIA) can lead to severe issues like insufficient generation, physical damage to the grid, power flow imbalance as well as economical loss. The recent advancements in machine learning algorithms have helped solve the drawbacks of using classical detection techniques for such attacks. In this article, we propose to use Autoencoders (AE’s) as a novel Machine Learning approach to detect FDI attacks without any major modifications. The performance of the method is validated through the analysis of the simulation results. The algorithm achieves optimal accuracy owing to the unsupervised nature of the algorithm.

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

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

In the world of information technology and the Internet, which has become a part of human life today and is constantly expanding, Attention to the users' requirements such as information security, fast processing, dynamic and instant access, and costs savings has become essential. The solution that is proposed for such problems today is a technology that is called cloud computing. Today, cloud computing is considered one of the most essential distributed tools for processing and storing data on the Internet. With the increasing using this tool, the need to schedule tasks to make the best use of resources and respond appropriately to requests has received much attention, and in this regard, many efforts have been made and are being made. To this purpose, various algorithms have been proposed to calculate resource allocation, each of which has tried to solve equitable distribution challenges while using maximum resources. One of these calculation methods is the DRF algorithm. Although it offers a better approach than previous algorithms, it faces challenges, especially with time-consuming resource allocation computing. These challenges make the use of DRF more complex than ever in the low number of requests with high resource capacity as well as the high number of simultaneous requests. This study tried to reduce the computations costs associated with the DRF algorithm for resource allocation by introducing a new approach to using this DRF algorithm to automate calculations by machine learning and artificial intelligence algorithms (Autonomic Dominant Resource Fairness or A-DRF).

Legacy programs are normally monolithic (that is, all code runs in a single process and is not partitioned), and a bug in a program may result in the entire program being vulnerable and therefore untrusted. Program partitioning can be used to separate a program into multiple partitions, so as to isolate sensitive data or privileged operations. Manual program partitioning requires programmers to rewrite the entire source code, which is cumbersome, error-prone, and not generic. Automatic program partitioning tools can separate programs according to the dependency graph constructed based on data or programs. However, programmers still need to manually implement remote service interfaces for inter-partition communication. Therefore, in this paper, we propose AutoSlicer, whose purpose is to partition a program more automatically, so that the programmer is only required to annotate sensitive data. AutoSlicer constructs accurate data dependency graphs (DDGs) by enabling execution flow graphs, and the DDG-based partitioning algorithm can compute partition information based on sensitive annotations. In addition, the code refactoring toolchain can automatically transform the source code into sensitive and insensitive partitions that can be deployed on the remote procedure call framework. The experimental evaluation shows that AutoSlicer can effectively improve the accuracy (13%-27%) of program partitioning by enabling EFG, and separate real-world programs with a relatively smaller performance overhead (0.26%-9.42%).

The accelerated move toward adopting the Smart Grid paradigm has resulted in numerous drawbacks as far as security is concerned. Traditional power grids are becoming more vulnerable to cyberattacks as all the control decisions are generated based on the data the Smart Grid generates during its operation. This data can be tampered with or attacked in communication lines to mislead the control room in decision-making. The false data injection attack (FDIA) is one of the most severe cyberattacks on today’s cyber-physical power system, as it has the potential to cause significant physical and financial damage. However, detecting cyberattacks are incredibly challenging since they have no known patterns. In this paper, we launch a random FDIA on IEEE-39 bus system. Later, we propose a Bagging MLP-based autoencoder to detect the FDIAs in the power system and compare the result with a single ML model. The Bagging MLP-based autoencoder outperforms the Isolation forest while detecting FDIAs.

The outsourcing of databases is very popular among IT companies and industries. It acts as a solution for businesses to ensure availability of the data for their users. The solution of outsourcing the database is to encrypt the data in a form where the database service provider can perform relational operations over the encrypted database. At the same time, the associated security risk of data leakage prevents many potential industries from deploying it. In this paper, we present a secure outsourcing database search scheme (BASDB) with the use of a smart contract for search operation over index of encrypted database and storing encrypted relational database in the cloud. Our proposed scheme BASDB is a simple and practical solution for effective search on encrypted relations and is well resistant to information leakage against attacks like search and access pattern leakage.

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

The introduction of the study primarily emphasises the significance of utilising block chain technologies with the possibility of privacy and security benefits from the 5G Network. One may state that the study’s primary focus is on all the advantages of adopting block chain technology to safeguard everyone’s access to crucial data by utilizing intelligent contracts to enhance the 5G network security model on information security operations.Our literature evaluation for the study focuses primarily on the advantages advantages of utilizing block chain technology advance data security and privacy, as well as their development and growth. The whole study paper has covered both the benefits and drawbacks of employing the block chain technology. The literature study part of this research article has, on the contrary hand, also studied several approaches and tactics for using the blockchain technology facilities. To fully understand the circumstances in this specific case, a poll was undertaken. It was possible for the researchers to get some real-world data in this specific situation by conducting a survey with 51 randomly selected participants.

Smart contracts are an attractive aspect of blockchain technology. A smart contract is a piece of executable code that runs on top of the blockchain and is used to facilitate, execute, and enforce agreements between untrustworthy parties without the need for a third party. This paper offers a review of the literature on smart contract applications in intellectual property management. The goal is to look at technology advancements and smart contract deployment in this area. The theoretical foundation of many papers published in recent years is used as a source of theoretical and implementation research for this purpose. According to the literature review we conducted, smart contracts function automatically, control, or document legally significant events and activities in line with the contract agreement's terms. This is a relatively new technology that is projected to deliver solutions for trust, security, and transparency across a variety of areas. An exploratory strategy was used to perform this literature review.

The botnet is a serious network security threat that can cause servers crash, so how to detect the behavior of Botnet has already become an important part of the research of network security. DNS(Domain Name System) request is the first step for most of the mainframe computers controlled by Botnet to communicate with the C&C(command; control) server. The detection of DNS request domain names is an important way for mainframe computers controlled by Botnet. However, the detection method based on fixed rules is hard to take effect for botnet based on DGA(Domain Generation Algorithm) because malicious domain names keep evolving and derive many different generation methods. Contrasted with the traditional methods, the method based on machine learning is a better way to detect it by learning and modeling the DGA. This paper presents a method based on the Naive Bayes model, the XGBoost model, the SVM(Support Vector Machine) model, and the MLP(Multi-Layer Perceptron) model, and tests it with real data sets collected from DGA, Alexa, and Secrepo. The experimental results show the precision score, the recall score, and the F1 score for each model.

This paper presents CaptchaGG, a model for recognizing linear graphical CAPTCHAs. As in the previous society, CAPTCHA is becoming more and more complex, but in some scenarios, complex CAPTCHA is not needed, and usually, linear graphical CAPTCHA can meet the corresponding functional scenarios, such as message boards of websites and registration of accounts with low security. The scheme is based on convolutional neural networks for feature extraction of CAPTCHAs, recurrent neural forests A neural network that is too complex will lead to problems such as difficulty in training and gradient disappearance, and too simple will lead to underfitting of the model. For the single problem of linear graphical CAPTCHA recognition, the model which has a simple architecture, extracting features by convolutional neural network, sequence modeling by recurrent neural network, and finally classification and recognition, can achieve an accuracy of 96% or more recognition at a lower complexity.

We show that a new type of dielectric cavity featuring deep sub-wavelength light confinement allows a significant speedup of all-optical signal processing functionalities, without compromising the energy efficiency. The effect is due to enhanced diffusion dynamics in an unconventional geometry.

Automatic speech recognition (ASR) models are used widely in applications for voice navigation and voice control of domestic appliances. ASRs have been misused by attackers to generate malicious outputs by attacking the deep learning component within ASRs. To assess the security and robustnesss of ASRs, we propose techniques within our framework SPAT that generate blackbox (agnostic to the DNN) adversarial attacks that are portable across ASRs. This is in contrast to existing work that focuses on whitebox attacks that are time consuming and lack portability. Our techniques generate adversarial attacks that have no human audible difference by manipulating the input speech signal using a psychoacoustic model that maintains the audio perturbations below the thresholds of human perception. We propose a framework SPAT with three attack generation techniques based on the psychoacoustic concept and frame selection techniques to selectively target the attack. We evaluate portability and effectiveness of our techniques using three popular ASRs and two input audio datasets using the metrics- Word Error Rate (WER) of output transcription, Similarity to original audio, attack Success Rate on different ASRs and Detection score by a defense system. We found our adversarial attacks were portable across ASRs, not easily detected by a state-of the-art defense system, and had significant difference in output transcriptions while sounding similar to original audio.

Spreading codes are the core of the spread spectrum transmission. In this paper, a novel channel-dependent code allocation procedure for enhancing security in multi-carrier code division multiple access (MC-CDMA) system is proposed and investigated over frequency-selective fading. The objective of the proposed technique is to assign the codes to every subcarrier of active/legitimate receivers (Rxs) based on their channel frequency response (CFR). By that, we ensure security for legitimate Rxs against eavesdropping while preserving mutual confidentiality between the legitimate Rxs themselves. To do so, two assigning modes; fixed assigning mode (FAM) and adaptive assigning mode (AAM), are exploited. The effect of the channel estimation error and the number of legitimate Rxs on the bit error rate (BER) performance is studied. The presented simulations show that AAM provides better security with a complexity trade-off compared to FAM. While the latter is more robust against the imperfection of channel estimation.

In the present era of the internet, image watermarking schemes are used to provide content authentication, security and reliability of various multimedia contents. In this paper image watermarking scheme which utilizes the properties of Integer Wavelet Transform (IWT), Schur decomposition and Singular value decomposition (SVD) based is proposed. In the suggested method, the cover image is subjected to a 3-level Integer wavelet transform (IWT), and the HH3 subband is subjected to Schur decomposition. In order to retrieve its singular values, the upper triangular matrix from the HH3 subband’s Schur decomposition is then subjected to SVD. The watermark image is first encrypted using a chaotic map, followed by the application of a 3-level IWT to the encrypted watermark and the usage of singular values of the LL-subband to embed by manipulating the singular values of the processed cover image. The proposed scheme is tested under various attacks like filtering (median, average, Gaussian) checkmark (histogram equalization, rotation, horizontal and vertical flipping) and noise (Gaussian, Salt & Pepper Noise). The suggested scheme provides strong robustness against numerous attacks and chaotic encryption provides security to watermark.

Smart phones have become the preferred way for Chinese Internet users currently. The mobile phone traffic is large from the operating system. These traffic is mainly generated by the services. In the context of the universal encryption of the traffic, classification identification of mobile encryption services can effectively reduce the difficulty of analytical difficulty due to mobile terminals and operating system diversity, and can more accurately identify user access targets, and then enhance service quality and network security management. The existing mobile encryption service classification methods have two shortcomings in feature selection: First, the DL model is used as a black box, and the features of large dimensions are not distinguished as input of classification model, which resulting in sharp increase in calculation complexity, and the actual application is limited. Second, the existing feature selection method is insufficient to use the time and space associated information of traffic, resulting in less robustness and low accuracy of the classification. In this paper, we propose a feature enhancement method based on adjacent flow contextual features and evaluate the Apple encryption service traffic collected from the real world. Based on 5 DL classification models, the refined classification accuracy of Apple services is significantly improved. Our work can provide an effective solution for the fine management of mobile encryption services.

With the development of technology, mobile phones are an indispensable part of human life. Factors such as brand, internal memory, wifi, battery power, camera and availability of 4G are now modifying consumers' decisions on buying mobile phones. But people fail to link those factors with the price of mobile phones; in this case, this paper is aimed to figure out the problem by using machine learning algorithms like Support Vector Machine, Decision Tree, K Nearest Neighbors and Naive Bayes to train the mobile phone dataset before making predictions of the price level. We used appropriate algorithms to predict smartphone prices based on accuracy, precision, recall and F1 score. This not only helps customers have a better choice on the mobile phone but also gives advice to businesses selling mobile phones that the way to set reasonable prices with the different features they offer. This idea of predicting prices level will give support to customers to choose mobile phones wisely in the future. The result illustrates that among the 4 classifiers, SVM returns to the most desirable performance with 94.8% of accuracy, 97.3 of F1 score (without feature selection) and 95.5% of accuracy, 97.7% of F1 score (with feature selection).

The experimental results demonstrated that, With the development of cloud computing, more and more people use cloud computing to do all kinds of things. However, for cloud computing, the most important thing is to ensure the stability of user data and improve security at the same time. From an analysis of the experimental results, it can be found that Cloud computing makes extensive use of technical means such as computing virtualization, storage system virtualization and network system virtualization, abstracts the underlying physical facilities into external unified interfaces, maps several virtual networks with different topologies to the underlying infrastructure, and provides differentiated services for external users. By comparing and analyzing the experimental results, it is clear that virtualization technology will be the main way to solve cloud computing security. Virtualization technology introduces a virtual layer between software and hardware, provides an independent running environment for applications, shields the dynamics, distribution and differences of hardware platforms, supports the sharing and reuse of hardware resources, provides each user with an independent and isolated computer environment, and facilitates the efficient and dynamic management and maintenance of software and hardware resources of the whole system. Applying virtualization technology to cloud security reduces the hardware cost and management cost of "cloud security" enterprises to a certain extent, and improves the security of "cloud security" technology to a certain extent. This paper will outline the basic cloud computing security methods, and focus on the analysis of virtualization cloud security technology

This paper presents a program-code mutation technique that is applied in-field to embedded systems in order to create diversity in a population of systems that are identical at the time of their deployment. With this diversity, it becomes more difficult for attackers to carry out the very popular Return-Oriented-Programming (ROP) attack in a large scale, since the gadgets in different systems are located at different program addresses after code permutation. In order to prevent the system from a system crash after a failed ROP attack, we further propose the combination of the code mutation with a return address checking. We will report the overhead in time and memory along with a security analysis.

With the development of software defined network and network function virtualization, network operators can flexibly deploy service function chains (SFC) to provide network security services more than before according to the network security requirements of business systems. At present, most research on verifying the correctness of SFC is based on whether the logical sequence between service functions (SF) in SFC is correct before deployment, and there is less research on verifying the correctness after SFC deployment. Therefore, this paper proposes a method of using Colored Petri Net (CPN) to establish a verification model offline and verify whether each SF deployment in SFC is correct after online deployment. After the SFC deployment is completed, the information is obtained online and input into the established model for verification. The experimental results show that the SFC correctness verification method proposed in this paper can effectively verify whether each SF in the deployed SFC is deployed correctly. In this process, the correctness of SF model is verified by using SF model in the model library, and the model reuse technology is preliminarily discussed.

Control flow integrity (CFI) checks are used in desktop systems, in order to protect them from various forms of attacks, but they are rarely investigated for embedded systems, due to their introduced overhead. The contribution of this paper is an efficient software implementation of a CFI-check for ARM-and Xtensa processors. Moreover, we propose the combination of this CFI-check with another defense mechanism against return-oriented-programming (ROP). We show that by this combination the security is significantly improved. Moreover, it will also in-crease the safety of the system, since the combination can detect a failed ROP-attack and bring the system in a safe state, which is not possible when using each technique separately. We will also report on the introduced overhead in code size and run time.