Biblio

The Internet of things (IoT) is proving to be a boon in granting internet access to regularly used objects and devices. Sensors, programs, and other innovations interact and trade information with different gadgets and frameworks over the web. Even in modern times, IoT gadgets experience the ill effects of primary security threats, which expose them to many dangers and malware, one among them being IoT botnets. Botnets carry out attacks by serving as a vector and this has become one of the significant dangers on the Internet. These vectors act against associations and carry out cybercrimes. They are used to produce spam, DDOS attacks, click frauds, and steal confidential data. IoT gadgets bring various challenges unlike the common malware on PCs and Android devices as IoT gadgets have heterogeneous processor architecture. Numerous researches use static or dynamic analysis for detection and classification of botnets on IoT gadgets. Most researchers haven't addressed the multi-architecture issue and they use a lot of computing resources for analyzing. Therefore, this approach attempts to classify botnets in IoT by using PSI-Graphs which effectively addresses the problem of encryption in IoT botnet detection, tackles the multi-architecture problem, and reduces computation time. It proposes another methodology for describing and recognizing botnets utilizing graph-based Machine Learning techniques and Exploratory Data Analysis to analyze the data and identify how separable the data is to recognize bots at an earlier stage so that IoT devices can be prevented from being attacked.

Nowadays, the popularity of intelligent terminals makes malwares more and more serious. Among the many features of application, the call graph can accurately express the behavior of the application. The rapid development of graph neural network in recent years provides a new solution for the malicious analysis of application using call graphs as features. However, there are still problems such as low accuracy. This paper established a large-scale data set containing more than 40,000 samples and selected the class call graph, which was extracted from the application, as the feature and used the graph embedding combined with the deep neural network to detect the malware. The experimental results show that the accuracy of the detection model proposed in this paper is 97.7%; the precision is 96.6%; the recall is 96.8%; the F1-score is 96.4%, which is better than the existing detection model based on Markov chain and graph embedding detection model.

Cloud computing forms the backbone of the era of automation and the Internet of Things (IoT). It offers computing and storage-based services on consumption-based pricing. Large-scale datacenters are used to provide these service and consumes enormous electricity. Datacenters contribute a large portion of the carbon footprint in the environment. Through virtual machine (VM) consolidation, datacenter energy consumption can be reduced via efficient resource management. VM selection policy is used to choose the VM that needs migration. In this research, we have proposed PbV mSp: A priority-based VM selection policy for VM consolidation. The PbV mSp is implemented in cloudsim and evaluated compared with well-known VM selection policies like gpa, gpammt, mimt, mums, and mxu. The results show that the proposed PbV mSp selection policy has outperformed the exisitng policies in terms of energy consumption and other metrics.

The data of large-scale distributed demand-side iot devices are gradually migrated to the cloud. This cloud deployment mode makes it convenient for IoT devices to participate in the interaction between supply and demand, and at the same time exposes various vulnerabilities of IoT devices to the Internet, which can be easily accessed and manipulated by hackers to launch large-scale DDoS attacks. As an easy-to-understand supervised learning classification algorithm, KNN can obtain more accurate classification results without too many adjustment parameters, and has achieved many research achievements in the field of DDoS detection. However, in the face of high-dimensional data, this method has high operation cost, high cost and not practical. Aiming at this disadvantage, this chapter explores the potential of classical KNN algorithm in data storage structure, K-nearest neighbor search and hyperparameter optimization, and proposes an improved KNN algorithm for DDoS attack detection of demand-side IoT devices.

As one of the effective methods to enhance traffic safety and improve traffic efficiency, the Internet of vehicles has attracted wide attention from all walks of life. V2X secure communication, as one of the research hotspots of the Internet of vehicles, also has many security and privacy problems. Attackers can use these vulnerabilities to obtain vehicle identity information and location information, and can also attack vehicles through camouflage.Therefore, the identity authentication process in vehicle network communication must be effectively protected. The anonymous identity authentication scheme based on moving target defense proposed in this paper not only ensures the authenticity and integrity of information sources, but also avoids the disclosure of vehicle identity information.

Problems such as the increase in the number of private vehicles with the population, the rise in environmental pollution, the emergence of unmet infrastructure and resource problems, and the decrease in time efficiency in cities have put local governments, cities, and countries in search of solutions. These problems faced by cities and countries are tried to be solved in the concept of smart cities and intelligent transportation by using information and communication technologies in line with the needs. While designing intelligent transportation systems (ITS), beyond traditional methods, big data should be designed in a state-of-the-art and appropriate way with the help of methods such as artificial intelligence, machine learning, and deep learning. In this study, a data-driven decision support system model was established to help the business make strategic decisions with the help of intelligent transportation data and to contribute to the elimination of public transportation problems in the city. Our study model has been established using big data technologies and business intelligence technologies: a decision support system including data sources layer, data ingestion/ collection layer, data storage and processing layer, data analytics layer, application/presentation layer, developer layer, and data management/ data security layer stages. In our study, the decision support system was modeled using ITS data supported by big data technologies, where the traditional structure could not find a solution. This paper aims to create a basis for future studies looking for solutions to the problems of integration, storage, processing, and analysis of big data and to add value to the literature that is missing within the framework of the model. We provide both the lack of literature, eliminate the lack of models before the application process of existing data sets to the business intelligence architecture and a model study before the application to be carried out by the authors.

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.

Artificial intelligence creation comes into fashion and has brought unprecedented challenges to intellectual property law. In order to study the viewpoints of AI creation copyright ownership from professionals in different institutions, taking the papers of AI creation on CNKI from 2016 to 2021, we applied orthogonal design and analysis of variance method to construct the dataset. A kernel-SVM classifier with different kernel methods in addition to some shallow machine learning classifiers are selected in analyzing and predicting the copyright ownership of AI creation. Support vector machine (svm) is widely used in statistics and the performance of SVM method is closely related to the choice of the kernel function. SVM with RBF kernel surpasses the other seven kernel-SVM classifiers and five shallow classifier, although the accuracy provided by all of them was not satisfactory. Various performance metrics such as accuracy, F1-score are used to evaluate the performance of KSVM and other classifiers. The purpose of this study is to explore the overall viewpoints of AI creation copyright ownership, investigate the influence of different features on the final copyright ownership and predict the most likely viewpoint in the future. And it will encourage investors, researchers and promote intellectual property protection in China.

WhatsApp is one of the rare applications that has managed to become one of the most popular instant messaging applications all over the world. While inherently designed for simple and fast communication, privacy features such as end-to-end encryption have made confidential communication easy for criminals aiming to commit illegal acts. However, as it meets many daily communication and communication needs, it has a great potential to be digital evidence in interpersonal disputes. In this study, in parallel with the potential of WhatsApp application to contain digital evidence, the abuse of this situation and the manipulation method of multimedia files, which may cause wrong decisions by the judicial authorities, are discussed. The dangerous side of this method, which makes the analysis difficult, is that it can be applied by anyone without the need for high-level root authority or any other application on these devices. In addition, it is difficult to detect as no changes can be made in the database during the analysis phase. In this study, a controlled experimental environment was prepared on the example scenario, the manipulation was carried out and the prepared system analysis was included. The results obtained showed that the evidence at the forensic analysis stage is open to misinterpretation.

As an important branch of computer forensics, network forensics technology, whether abroad or at home, is in its infancy. It mainly focuses on the research on the framework of some forensics systems or some local problems, and has not formed a systematic theory, method and system. In order to improve the network forensics sys-tem, have a relatively stable and correct model for refer-ence, ensure the authenticity and credibility of network fo-rensics from the forensics steps, provide professional and non professional personnel with a standard to measure the availability of computer network crime investigation, guide the current network forensics process, and promote the gradual maturity of network forensics theories and methods, This paper presents a fuzzy decision tree reason-ing method for network forensics analysis.

All-or-nothing transforms (AONT) were proposed by Rivest as a message preprocessing technique for encrypting data to protect against brute-force attacks, and have many applications in cryptography and information security. Later the unconditionally secure AONT and their combinatorial characterization were introduced by Stinson. Informally, a combinatorial AONT is an array with the unbiased requirements and its security properties in general depend on the prior probability distribution on the inputs s-tuples. Recently, it was shown by Esfahani and Stinson that a combinatorial AONT has perfect security provided that all the inputs s-tuples are equiprobable, and has weak security provided that all the inputs s-tuples are with non-zero probability. This paper aims to explore on the gap between perfect security and weak security for combinatorial (t, s, v)-AONTs. Concretely, we consider the typical scenario that all the s inputs take values independently (but not necessarily identically) and quantify the amount of information H(\textbackslashmathcalX\textbackslashmid \textbackslashmathcalY) about any t inputs \textbackslashmathcalX that is not revealed by any s−t outputs \textbackslashmathcalY. In particular, we establish the general lower and upper bounds on H(\textbackslashmathcalX\textbackslashmid \textbackslashmathcalY) for combinatorial AONTs using information-theoretic techniques, and also show that the derived bounds can be attained in certain cases.

Building occupancy data helps increase energy management systems’ performance, enabling lower energy use while preserving occupant comfort. The focus of this study is employing environmental data (e.g., including but not limited to temperature, humidity, carbon dioxide (CO2), etc.) to infer occupancy information. This will be achieved by exploring the application of information theory metrics with machine learning (ML) approaches to classify occupancy levels for a given dataset. Three datasets and six distinct ML algorithms were used in a comparative study to determine the best strategy for identifying occupancy patterns. It was determined that both k-nearest neighbors (kNN) and random forest (RF) identify occupancy labels with the highest overall level of accuracy, reaching 97.99% and 98.56%, respectively.

The problem of information security of critical information infrastructure objects in the conditions of openness is formulated. The concept of information infrastructure openness is analyzed. An approach to assessing the openness of an information system is presented. A set-theoretic model of information resources openness was developed. The formulation of the control problem over the degree of openness with restrictions on risk was carried out. An example of solving the problem of finding the coefficient of openness is presented.

Many organisations responded to the recent global pandemic by moving operations online. This has led to increased exposure to information security-related risks. There is thus an increased need to ensure organisational information security awareness programs are up to date and relevant to the needs of the intended target audience. The advent of online educational providers has similarly placed increased pressure on the formal educational sector to ensure course content is updated to remain relevant. Such processes of academic reflection and review should consider formal curriculum standards and guidelines in order to ensure wide relevance. This paper presents a case study of the review of an Introduction to Information Security course. This review is informed by the Information Security and Assurance knowledge area of the ACM/IEEE Computer Science 2013 curriculum standard. The paper presents lessons learned during this review process to serve as a guide for future reviews of this nature. The authors assert that these lessons learned can also be of value during the review of organisational information security awareness programs.

This article provides an overview of the security of VANET, which is a vehicle network. When reviewing this topic, publications of various researchers were considered. The article provides information security requirements for VANET, an overview of security research, an overview of existing attacks, methods for detecting attacks and appropriate countermeasures against such threats.

As an important pillar of social informatization, e-government not only provides more convenient services for the public, but also effectively improves administrative efficiency. At the same time, the application of cloud computing technology also urgently requires the government to improve the level of digital construction. This paper proposes the concept of e-government based on cloud computing, analyze the possible hidden dangers that cloud computing brings to e-government in management, technology, and security, and build cloud computing e-government information security system from three aspects: cloud security management, cloud security technology, and cloud security assurance.

Big Data is a concept used in various sectors today, including the government sector in the Smart Government initiative. With a large amount of structured and unstructured data being managed, information assurance becomes important in adopting Big Data. However, so far, no research has focused on information assurance for Big Data. This paper identified information assurance factors for Big Data. This research used the systematic snapshot mapping approach to examine factors relating to information assurance from the literature related to Big Data from 2011 through 2021. The data extraction process in gathering 15 relevant papers. The findings revealed ten factors influencing the information assurance implementation for Big Data, with the security factor becoming the most concentrated factor with 18 sub-factors. The findings are expected to serve as a foundation for adopting information assurance for Big Data to develop an information assurance framework for Smart Government.

Machine tool is known as the mother of industry. CNC machine tool is the embodiment of modern automatic control productivity. In the context of the rapid development of the industrial Internet, a large number of equipment and systems are interconnected through the industrial Internet, realizing the flexible adaptation from the supply side to the demand side. As the a typical core system of industrial Internet, CNC system is facing the threat of industrial virus and network attack. The problem of information security is becoming more and more prominent. This paper analyzes the security risks of the existing CNC system from the aspects of terminal security, data security and network security. By comprehensively using the technologies of data encryption, identity authentication, digital signature, access control, secure communication and key management, this paper puts forward a targeted security protection and management scheme, which effectively strengthens the overall security protection ability.

With the development of industrial informatization, information security in the power production industry is becoming more and more important. In the power production industry, as the critical information egress of the industrial control system, the information security of the Networked Control System is particularly important. This paper proposes a construction method for an information security platform of Networked Control System, which is used for research, testing and training of Networked Control System information security.

Digital identity management system is the securi-ty infrastructure of computer and internet applications. However, currently, most of the digital identity management systems are faced with problems such as the difficulty of cross-domain authentication and interoperation, the lack of credibility of identity authentication, the weakness of the security of identity data. Although the advantages of block-chain technology have attached the attentions of experts and scholars in the field of digital identity management and many digital identity management systems based on block-chain have been built, the systems still can't completely solve the problems mentioned above. Therefore, in this pa-per, an effective digital identity management system model is proposed which combines technologies of self-sovereign identity and oracle with blockchain so as to pave a way in solving the problems mentioned above and constructing a secure and reliable digital identity management system.

In this work, we examine the following question: How can we improve the best data complexity among the impossible differential (ID) attacks on AES? One of the most efficient attacks on AES are ID attacks. We have seen that the Biham-Keller ID characteristics are frequently used in these ID attacks. We observe the following fact: The probability that a given pair with a wrong key produce an ID characteristic is closely correlated to the data usage negatively. So, we maximize this probability by exploiting a Biham-Keller ID characteristic in a different manner than the other attacks. As a result, we mount an ID attack on 7-round AES-192 and obtain the best data requirement among all the ID attacks on 7-round AES. We make use of only 2$^\textrm58$ chosen plaintexts.

Misuse of caller ID spoofing combined with social engineering has the potential as a means to commit other crimes, such as fraud, theft, leaking sensitive information, spreading hoaxes, etc. The appropriate forensic technique must be carried out to support the verification and collection of evidence related to these crimes. In this research, a digital forensic analysis was carried out on the BlueStacks emulator, Redmi 5A smartphone, and SIM card which is a device belonging to the victim and attacker to carry out caller ID spoofing attacks. The forensic analysis uses the NIST SP 800-101 R1 guide and forensic tools FTK imager, Oxygen Forensic Detective, and Paraben’s E3. This research aims to determine the artifacts resulting from caller ID spoofing attacks to assist in mapping and finding digital evidence. The result of this research is a list of digital evidence findings in the form of a history of outgoing calls, incoming calls, caller ID from the source of the call, caller ID from the destination of the call, the time the call started, the time the call ended, the duration of the call, IMSI, ICCID, ADN, and TMSI.

A hash is a fixed-length output of some data that has been through a one-way function that cannot be reversed, called the hashing algorithm. Hashing algorithms are used to store secure information, such as passwords. They are stored as hashes after they have been through a hashing algorithm. Also, hashing algorithms are used to insure the checksum of certain data over the internet. This paper discusses how Ibn Omar's hashing algorithm will provide higher security for data than other hash functions used nowadays. Ibn Omar's hashing algorithm in produces an output of 1024 bits, four times as SHA256 and twice as SHA512. Ibn Omar's hashing algorithm reduces the vulnerability of a hash collision due to its size. Also, it would require enormous computational power to find a collision. There are eight salts per input. This hashing algorithm aims to provide high privacy and security for users.

Cloud computing plays major role in the development of accessing clouduser’s document and sensitive information stored. It has variety of content and representation. Cyber security and attacks in the cloud is a challenging aspect. Information security attains a vital part in Cyber Security management. It involves actions intended to reduce the adverse impacts of such incidents. To access the documents stored in cloud safely and securely, access control will be introduced based on cloud users to access the user’s document in the cloud. To achieve this, it is highly required to combine security components (e.g., Access Control, Usage Control) in the security document to get automatic information. This research work has proposed a Role Key Homomorphic Encryption Algorithm (RKHEA) to monitor the cloud users, who access the services continuously. This method provides access creation of session-based key to store the singularized encryption to reduce the key size from random methods to occupy memory space. It has some terms and conditions to be followed by the cloud users and also has encryption method to secure the document content. Hence the documents are encrypted with the RKHEA algorithm based on Service Key Access (SKA). Then, the encrypted key will be created based on access control conditions. The proposed analytics result shows an enhanced control over the documents in cloud and improved security performance.

Transmitting data through the internet may have severe security risks due to illegal access done by attackers. Some methods have been introduced to overcome this issue, such as cryptography and steganography. Nevertheless, some problems still arise, such as the quality of the stego data. Specifically, it happens if the stego is shared with some users. In this research, a shared-secret mechanism is combined with steganography. For this purpose, the fuzzy logic edge detection and Prediction Error (PE) methods are utilized to hide private data. The secret sharing process is carried out after data embedding in the cover image. This sharing mechanism is performed on image pixels that have been converted to PE values. Various Peak Signal to Noise Ratio (PSNR) values are obtained from the experiment. It is found that the number of participants and the threshold do not significantly affect the image quality of the shares.