Biblio

Detection of malware and security attacks is a complex process that can vary in its details and analysis activities. As part of the detection process, malware scanners try to categorize a malware once it is detected under one of the known malware categories (e.g. worms, spywares, viruses, etc.). However, many studies and researches indicate problems with scanners categorizing or identifying a particular malware under more than one malware category. This paper, and several others, show that machine learning can be used for malware detection especially with ensemble base prediction methods. In this paper, we evaluated several custom-built ensemble models. We focused on multi-label malware classification as individual or classical classifiers showed low accuracy in such territory.This paper showed that recent machine models such as ensemble and deep learning can be used for malware detection with better performance in comparison with classical models. This is very critical in such a dynamic and yet important detection systems where challenges such as the detection of unknown or zero-day malware will continue to exist and evolve.

This document addresses the issue of the actual security level of PDF documents. Two types of detection approaches are utilized to detect dangerous elements within malware: static analysis and dynamic analysis. Analyzing malware binaries to identify dangerous strings, as well as reverse-engineering is included in static analysis for t1he malware to disassemble it. On the other hand, dynamic analysis monitors malware activities by running them in a safe environment, such as a virtual machine. Each method has its own set of strengths and weaknesses, and it is usually best to employ both methods while analyzing malware. Malware detection could be simplified without sacrificing accuracy by reducing the number of malicious traits. This may allow the researcher to devote more time to analysis. Our worry is that there is no obvious need to identify malware with numerous functionalities when it isn't necessary. We will solve this problem by developing a system that will identify if the given file is infected with malware or not.

Open set recognition (OSR) problem has been a challenge in many machine learning (ML) applications, such as security. As new/unknown malware families occur regularly, it is difficult to exhaust samples that cover all the classes for the training process in ML systems. An advanced malware classification system should classify the known classes correctly while sensitive to the unknown class. In this paper, we introduce a self-supervised pre-training approach for the OSR problem in malware classification. We propose two transformations for the function call graph (FCG) based malware representations to facilitate the pretext task. Also, we present a statistical thresholding approach to find the optimal threshold for the unknown class. Moreover, the experiment results indicate that our proposed pre-training process can improve different performances of different downstream loss functions for the OSR problem.

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.

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.

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.

The Internet of Vehicles (IoV) has a tremendous prospect for numerous vehicular applications. IoV enables vehicles to transmit data to improve roadway safety and efficiency. Data security is essential for increasing the security and privacy of vehicle and roadway infrastructures in IoV systems. Several researchers proposed numerous solutions to address security and privacy issues in IoV systems. However, these issues are not proper solutions that lack data authentication and verification protocols. In this paper, a blockchain-enabled automated data management system for vehicles has been proposed and demonstrated. This work enables automated data verification and authentication using smart contracts. Certified organizations can only access vehicle data uploaded by the vehicle user to the Interplanetary File System (IPFS) server through that vehicle user’s consent. The proposed system increases the security of vehicles and data. Vehicle privacy is also maintained here by increasing data privacy.

In the traditional Internet of Vehicles, communication data is easily tampered with and easily leaked. In order to improve the trust evaluation mechanism of the Internet of Vehicles and establish a trust relationship between vehicles, a blockchain-based Internet of Vehicles trust evaluation (BBTE) scheme is proposed. First, the scheme uses the roadside unit RSU to calculate the trust value of vehicle nodes and maintain the generation, verification and storage of blocks, so as to realize distributed data storage and ensure that data cannot be tampered with. Secondly, an efficient trust evaluation method is designed. The method integrates four trust decision factors: initial trust, historical experience trust, recommendation trust and RSU observation trust to obtain the overall trust value of vehicle nodes. In addition, in the process of constructing the recommendation trust method, the recommendation trust is divided into three categories according to the interaction between the recommended vehicle node and the communicator, use CRITIC to obtain the optimal weights of three recommended trusts, and use CRITIC to obtain the optimal weights of four trust decision-making factors to obtain the final trust value. Finally, the NS3 simulation platform is used to verify the security and accuracy of the trust evaluation method, and to improve the identification accuracy and detection rate of malicious vehicle nodes. The experimental analysis shows that the scheme can effectively deal with the gray hole attack, slander attack and collusion attack of other vehicle nodes, improve the security of vehicle node communication interaction, and provide technical support for the basic application of Internet of Vehicles security.

With the advent of the Internet era, all walks of life in our country have undergone earth-shaking changes, especially the drone and geographic information industries, which have developed rapidly under the impetus of the Internet of Things era. However, with the continuous development of science and technology, the network structure has become more and more complex, and the types of network attacks have varied. UAV information security and geographic information data have appeared security risks on the network. These hidden dangers have contributed to the progress of the drone and geographic information industry. And development has caused a great negative impact. In this regard, this article will conduct research on the network security of UAV systems and geographic information data, which can effectively assess the network security risks of UAV systems, and propose several solutions to potential safety hazards to reduce UAV networks. Security risks and losses provide a reference for UAV system data security.

With the development of urbanization, the number of vehicles is gradually increasing, and vehicles are gradually developing in the direction of intelligence. How to ensure that the data of intelligent vehicles is not tampered in the process of transmission to the cloud is the key problem of current research. Therefore, we have established a data security transmission system based on blockchain. First, we collect and filter vehicle data locally, and then use blockchain technology to transmit key data. Through the smart contract, the key data is automatically and accurately transmitted to the surrounding node vehicles, and the vehicles transmit data to each other to form a transaction and spread to the whole network. The node data is verified through the node data consensus protocol of intelligent vehicle data security transmission system, and written into the block to form a blockchain. Finally, the vehicle user can query the transaction record through the vehicle address. The results show that we can safely and accurately transmit and query vehicle data in the blockchain database.

Roads are the backbone of our country, they play an important role for human progress. Roads seem to be dangerous and harmful for human beings on hills, near rivers, lakes and small ridges. It's possible with the help of IoT (Internet of things) to incorporate all the things made efficiently and effectively. IoT in combination with roads make daily life smart and excellent. This paper shows IoT technology will be the beginning of smart cities and it will reduce road accidents and collisions. If all vehicles are IoT based and connected with the internet, then an efficient method to guide, it performs urgent action, when less time is available. Internet and antenna technology in combination with IoT perform fully automation in our day-to-day life. It will provide excellent service as well as accuracy and precision.

Internet of Vehicles consists of a three-layer architecture of electric vehicles, charging piles, and a grid dispatch management control center. Therefore, V2G presents multi-level, multi-agent and frequent information interaction, which requires a highly secure and lightweight identity authentication method. Based on the characteristics of Internet of Vehicles, this paper designs a multi-subject information interaction and one-way hash chain authentication method, it includes one-way hash chain and key distribution update strategy. The operation experiment of multiple electric vehicles and charging piles shows that the algorithm proposed in this paper can meet the V2G ID authentication requirements of Internet of Vehicles, and has the advantages of lightweight and low consumption. It is of great significance to improve the security protection level of Internet of Vehicles V2G.

The scale of the intelligent networked vehicle market is expanding rapidly, and network security issues also follow. A Situational Awareness (SA) system can detect, identify, and respond to security risks from a global perspective. In view of the discrete and weak correlation characteristics of perceptual data, this paper uses the Fly Optimization Algorithm (FOA) based on dynamic adjustment of the optimization step size to improve the convergence speed, and optimizes the extraction model of security situation element of the Internet of Vehicles (IoV), based on Probabilistic Neural Network (PNN), to improve the accuracy of element extraction. Through the comparison of experimental algorithms, it is verified that the algorithm has fast convergence speed, high precision and good stability.

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.

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.

In order to meet the needs of intellectual property protection and controlled sharing of scientific research sensitive data, a mechanism is proposed for security protection throughout “transfer, store and use” process of sensitive data which based on blockchain. This blockchain bottom layer security is reinforced. First, the encryption algorithm used is replaced by the national secret algorithm and the smart contract is encapsulated as API at the gateway level. Signature validation is performed when the API is used to prevent illegal access. Then the whole process of data up-chain, storage and down-chain is encrypted, and a mechanism of data structure query and data query condition construction based on blockchain smart is provided to ensure that the data is “usable and invisible”. Finally, data access control is ensured through role-based and hierarchical protection, and the blockchain base developed has good extensibility, which can meet the requirement of sensitive data security protection in scientific research filed and has broad application prospects.

Hardware IPs are assumed to be roots-of-trust in complex SoCs. However, their design and security verification are still heavily dependent on manual expertise. Extensive research in this domain has shown that even cryptographic modules may lack information flow security, making them susceptible to remote attacks. Further, when an SoC is in the hands of the attacker, physical attacks such as fault injection are possible. This paper introduces EISec, a novel tool utilizing symbolic execution for exhaustive analysis of hardware IPs. EISec operates at the pre-silicon stage on the gate level netlist of a design. It detects information flow security violations and generates the exhaustive set of control sequences that reproduces them. We further expand its capabilities to quantify the confusion and diffusion present in cryptographic modules and to analyze an FSM's susceptibility to fault injection attacks. The proposed methodology efficiently explores the complete input space of designs utilizing symbolic execution. In short, EISec is a holistic security analysis tool to help hardware designers capture security violations early on and mitigate them by reporting their triggers.