Biblio

With the rapid growth of the number of global network entities and interconnections, the security risks of network relationships are constantly accumulating. As the basis of network interconnection and communication, Internet routing is facing severe challenges such as insufficient online monitoring capability of large-scale routing events and lack of effective and credible verification mechanism. Major global routing security events emerge one after another, causing extensive and far-reaching impacts. To solve these problems, China Telecom studied the BGP (border gateway protocol) SDN (software defined network) controller technology to monitor the interconnection routing, constructed the global routing information database trust source integrating multi-dimensional information and developed the function of the protocol level based real-time monitoring system of Internet routing security events. Through these means, it realizes the second-level online monitoring capability of large-scale IP network Internet service routing events, forms the minute-level route leakage interception and route hijacking blocking solutions, and achieves intelligent protection capability of Internet routing security.

Wireless Sensor networks can be composed of smart buildings, smart homes, smart grids, and smart mobility, and they can even interconnect all these fields into a large-scale smart city network. Software-Defined Networking is an ideal technology to realize Internet-of-Things (IoT) Network and WSN network requirements and to efficiently enhance the security of these networks. Software defines Networking (SDN) is used to support IoT and WSN related networking elements, additional security concerns rise, due to the elevated vulnerability of such deployments to specific types of attacks and the necessity of inter-cloud communication any IoT application would require. This work is a study of different security mechanisms available in SDN for IoT and WSN network secure communication. This work also formulates the problems when existing methods are implemented with different networks parameters.

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 analysis shows how important Power Network Measuring and Characterization (PSMC) is to the plan. Networks planning and oversight for the transmission of electrical energy is becoming increasingly frequent. In reaction to the current contest of assimilating trying to cut charging in the crate, estimation, information sharing, but rather govern into PSMC reasonable quantities, Electrical Transmit Monitoring and Management provides a thorough outline of founding principles together with smart sensors for domestic spying, security precautions, and control of developed broadening power systems.Electricity supply control must depend increasingly heavily on telecommunications infrastructure to manage and run their processes because of the fluctuation in transmission and distribution of electricity. A wider attack surface will also be available to threat hackers as a result of the more communications. Large-scale blackout have occurred in the past as a consequence of cyberattacks on electrical networks. In order to pinpoint the key issues influencing power grid computer networks, we looked at the network infrastructure supporting electricity grids in this research.

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.

Using the methods of literature and interview, this paper analyzes the current situation of performance evaluation of volunteers in large-scale games based on mobile Internet, By analyzing the popularity of mobile Internet, the convenience of performance evaluation, the security and privacy of performance evaluation, this paper demonstrates the necessity of performance evaluation of volunteers in large-scale games based on mobile Internet, This paper puts forward the Countermeasures of performance evaluation of volunteers in large-scale games based on mobile Internet.

Artificial technology developed in recent years. It is an intelligent system that can perform tasks without human intervention. AI can be used for various purposes, such as speech recognition, face recognition, etc. AI can be used for good or bad purposes, depending on how it is implemented. The discuss the application of AI in data security technology and its advantages over traditional security methods. We will focus on the good use of AI by analyzing the impact of AI on the development of big data security technology. AI can be used to enhance security technology by using machine learning algorithms, which can analyze large amounts of data and identify patterns that cannot be detected automatically by humans. The computer big data security technology platform based on artificial intelligence in this paper is the process of creating a system that can identify and prevent malicious programs. The system must be able to detect all types of threats, including viruses, worms, Trojans and spyware. It should also be able to monitor network activity and respond quickly in the event of an attack.

Intelligent Systems for Personal Data Cyber Security is a critical component of the Personal Information Management of Medicaid Enterprises. Intelligent Systems for Personal Data Cyber Security combines components of Cyber Security Systems with Human-Computer Interaction. It also uses the technology and principles applied to the Internet of Things. The use of software-hardware concepts and solutions presented in this report is, in the authors’ opinion, some step in the working-out of the Intelligent Systems for Personal Data Cyber Security in Medicaid Enterprises. These concepts may also be useful for developers of these types of systems.

In the field of airport passenger security, a new type of security inspection equipment called intelligent passenger security equipment is applied widely, which can significantly improve the efficiency of airport security screening and passenger satisfaction. This paper establishes a security check channel model based on intelligent passenger security check equipment, and studies the factors affecting the efficiency of airport security screening, such as the number of baggage unloading points, baggage loading points, secondary inspection points, etc. A simulation model of security check channel is established based on data from existing intelligent passenger security check equipment and data collected from Beijing Daxing Airport. Equipment utilization and queue length data is obtained by running the simulation model. According to the data, the bottleneck is that the manual inspection process takes too long, and the utilization rate of the baggage unloading point is too low. For the bottleneck link, an optimization scheme is proposed. With more manual check points and secondary inspection points and less baggage unloading points, the efficiency of airport security screening significantly increases by running simulation model. Based on the optimized model, the effect of baggage unloading point and baggage loading point on efficiency is further studied. The optimal parameter configuration scheme under the expected efficiency is obtained. This research can assist engineers to find appropriate equipment configuration quickly and instruct the airport to optimize the arrangement of security staff, which can effectively improve the efficiency of airport security screening and reduce the operating costs of airport.

Tourism is one of the main sources of income in Australia. The number of tourists will affect airlines, hotels and other stakeholders. Predicting the arrival of tourists can make full preparations for welcoming tourists. This paper selects Queensland Tourism data as intelligent data. Carry out data visualization around the intelligent data, establish seasonal ARIMA model, find out the characteristics and predict. In order to improve the accuracy of prediction. Based on the tourism data around Queensland, build a 10 layer Back Propagation neural network model. It is proved that the network shows good performance for the data prediction of this paper.

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 recent 5G networks aim to provide higher speed, lower latency, and greater capacity; therefore, compared to the previous mobile networks, more advanced and intelligent network security is essential for 5G networks. To detect unknown and evolving 5G network intrusions, this paper presents an artificial intelligence (AI)-based network threat detection system to perform data labeling, data filtering, data preprocessing, and data learning for 5G network flow and security event data. The performance evaluations are first conducted on two well-known datasets-NSL-KDD and CICIDS 2017; then, the practical testing of proposed system is performed in 5G industrial IoT environments. To demonstrate detection against network threats in real 5G environments, this study utilizes the 5G model factory, which is downscaled to a real smart factory that comprises a number of 5G industrial IoT-based devices.

Nowadays, smart cities (SCs) use technologies and different types of data collected to improve the lifestyles of their citizens. Indeed, connected smart vehicles are technologies used for an SC’s intelligent traffic monitoring systems (ITMSs). However, most proposed monitoring approaches do not consider realtime monitoring. This paper presents real-time data processing for an intelligent traffic monitoring dashboard using the Hadoop ecosystem dashboard components. Many data are available due to our proposed monitoring approach, such as the total number of vehicles on different routes and data on trucks within a radius (10KM) of a specific point given. Based on our generated data, we can make real-time decisions to improve circulation and optimize traffic flow.

The application of mobile intelligent terminal in the environment is very complex, and its own computing capacity is also very limited, so it is vulnerable to malicious attacks. The security classification of mobile intelligent terminals can effectively ensure the security of their use. Therefore, a security classification method for mobile intelligent terminals based on multi-source data fusion is proposed. The Boolean value is used to count the multi-source data of the mobile intelligent terminal, and the word frequency method is used to calculate the weight of the multi-source data of the mobile intelligent terminal. The D-S evidence theory is used to complete the multi-source data fusion of the mobile intelligent terminal and implement the multi-source data fusion processing of the mobile intelligent terminal. On this basis, the security level permission value of mobile intelligent terminal is calculated to achieve the security level division of mobile intelligent terminal based on multi-source data fusion. The experimental results show that the accuracy of mobile intelligent terminal security classification is higher than 96% and the classification time is less than 3.8 ms after the application of the proposed method. Therefore, the security level of mobile intelligent terminals after the application of this method is high, and the security performance of mobile intelligent terminals is strong, which can effectively improve the accuracy of security classification and shorten the time of security classification.

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.

Digital forensics is essential when performing in-depth crime investigations and evidence extraction, especially in the field of the Internet of Things, where there is a ton of information every second boosted with latest and smartest technological devices. However, the enormous growth of data and the nature of its complexity could constrain the data examination process since traditional data acquisition techniques are not applicable nowadays. Therefore, if the knowledge gap between digital forensics and the Internet of Things is not bridged, investigators will jeopardize the loss of a possible rich source of evidence that otherwise could act as a lead in solving open cases. The work aims to introduce examples of employing the latest Internet of Things forensics approaches as a panacea in this regard. The paper covers a variety of articles presenting the new Blockchain, fog, and video-based applications that can aid in easing the process of digital forensics investigation with a focus on the Internet of Things. The results of the review indicated that the above current trends are very promising procedures in the field of Internet of Things digital forensics and need to be explored and applied more actively.

Access control includes authorization of security administrators and access of users. Aiming at the problems of log information storage difficulty and easy tampering faced by auditing and traceability forensics of authorization and access in cross-domain scenarios, we propose an access control auditing and traceability forensics method based on Blockchain, whose core is Ethereum Blockchain and IPFS interstellar mail system, and its main function is to store access control log information and trace forensics. Due to the technical characteristics of blockchain, such as openness, transparency and collective maintenance, the log information metadata storage based on Blockchain meets the requirements of distribution and trustworthiness, and the exit of any node will not affect the operation of the whole system. At the same time, by storing log information in the blockchain structure and using mapping, it is easy to locate suspicious authorization or judgment that lead to permission leakage, so that security administrators can quickly grasp the causes of permission leakage. Using this distributed storage structure for security audit has stronger anti-attack and anti-risk.

With the inundation of more cost effective and improved flight performance Unmanned Aerial Vehicles (UAVs) into the consumer market, we have seen more uses of these for both leisure and business purposes. As such, demand for digital forensic examination on these devices has seen an increase as well. This research will explore and discuss the forensic examination process on one of the more popular brands of UAV in Singapore, namely DJI. The findings are from the examination of the exposed File Transfer Protocol (FTP) channel and the extraction of the Data-at-Rest on the memory chip of the drone. The extraction was done using the Chip-Off and Chip-On technique.

With large advancements in image display technology, recapturing high-quality images from high-fidelity LCD screens becomes much easier. Such recaptured images can be used to hide image tampering traces and fool some intelligent identification systems. In order to prevent such a security loophole, we propose a recaptured image detection approach based on generalized central difference convolution (GCDC) network. Specifically, by using GCDC instead of vanilla convolution, more detailed features can be extracted from both intensity and gradient information from an image. Meanwhile, we concatenate the feature maps from multiple GCDC modules to fuse low-, mid-, and high-level features for higher performance. Extensive experiments on three public recaptured image databases demonstrate the superior of our proposed method when compared with the state-of-the-art approaches.

Frame deletion forensics has been a major area of video forensics in recent years. The detection effect of current deep neural network-based methods outperforms previous traditional detection methods. Recently, researchers have used residual features as input to the network to detect frame deletion and have achieved promising results. We propose an IReF (Improved Residual Feature) by analyzing the effect of residual features on frame deletion traces. IReF preserves the main motion features and edge information by denoising and enhancing the residual features, making it easier for the network to identify the tampered features. And the sparse noise reduction reduces the storage requirement. Experiments show that under the 2D convolutional neural network, the accuracy of IReF compared with residual features is increased by 3.81 %, and the storage space requirement is reduced by 78%. In the 3D convolutional neural network with video clips as feature input, the accuracy of IReF features is increased by 5.63%, and the inference efficiency is increased by 18%.

There are a large number of illegal websites on the Internet, such as pornographic websites, gambling websites, online fraud websites, online pyramid selling websites, etc. This paper studies the use of crawler technology for digital forensics on illegal websites. First, a crawler based illegal website forensics program is designed and developed, which can detect the peripheral information of illegal websites, such as domain name, IP address, network topology, and crawl key information such as website text, pictures, and scripts. Then, through comprehensive analysis such as word cloud analysis, word frequency analysis and statistics on the obtained data, it can help judge whether a website is illegal.

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.

With the advent of information and communication technology, the digital space is becoming a playing ground for criminal activities. Criminals typically prefer darkness or a hidden place to perform their illegal activities in a real-world while sometimes covering their face to avoid being exposed and getting caught. The same applies in a digital world where criminals prefer features which provide anonymity or hidden features to perform illegal activities. It is from this spirit the Darkweb is attracting all kinds of criminal activities conducted over the Internet such as selling drugs, illegal weapons, child pornography, assassination for hire, hackers for hire, and selling of malicious exploits, to mention a few. Although the anonymity offered by Darkweb can be exploited as a tool to arrest criminals involved in cybercrime, an in-depth research is needed to advance criminal investigation on Darkweb. Analysis of illegal activities conducted in Darkweb is in its infancy and faces several challenges like lack of standard operating procedures. This study proposes progressive standard operating procedures (SOPs) for Darkweb forensics investigation. We provide the four stages of SOP for Darkweb investigation. The proposed SOP consists of the following stages; identification and profiling, discovery, acquisition and preservation, and the last stage is analysis and reporting. In each stage, we consider the objectives, tools and expected results of that particular stage. Careful consideration of this SOP revealed promising results in the Darkweb investigation.