Detection Method of Stealing Complex Network Attack Considering Security Situation Awareness

Big data to use JAVA, group software engineering, networking, cloud computing knowledge and technology. The purpose of this paper is Research on Wireless sensor network of the Internet of things. The Internet of things, mainly includes multicast network, ZIGBEE network, WSN network, Bluetooth network, infrared network and so on. Swarm software engineering is a way to implement cloud computing, Internet of things and big data. Cloud computing comes from big data, the Internet of things can be achieved through cloud computing. This paper mainly studies, computers, software, networks, smart cities, and the use of Excel and Matlab and Microsoft Office Visio 2003 mapping and so forth. And through the practice of research methods, including automotive networking and smart city. What is the Internet of things, objects connected to the Internet is the Internet of things, cup networking, car networking. Things better than other networks, is composed of what objects, what composition, what nature, what innovation and superiority. The four key technologies of the Internet are widely used, and these four technologies are mainly RFID, WSN, M2M, two kinds of integration. RFID can be implemented using MATLAB, NS2, and JAVA, and WSN can be implemented using NS2, and M2M can be developed using JAVA. The research results are that the Internet of things originated and developed in the Internet; on the contrary, the development of the Internet of things further promoted the Internet to a more widespread “Internet” evolution. The Internet of things and the Internet are the relationships between the parent and the child. Wireless networks are just like wireless WSN networks, but wireless nodes are fixed and moved into sensors. The Internet of things includes Internet technology, including wired and wireless networks. The research conclusion is the wireless internet of things are just like the wired internet of things, wired provides the basis for Wireless Research. Baidu Tiangong Internet of things platform, Amazon AWS platform, ERP and so on are also important applications of the Internet of things. The use of mirroring and replication software also requires Internet of things. Video is composed of multi frame images. H.264 and JM encoder are also the future directions. Where to go network Ctrip network is the application of the Internet of things. Recalling what we do today, planning tomorrow, how to spend the university stage and predicting the future of university development can be the application of the Internet of things. I visited the Tangshan Rd. Mart store in Qian’an from January 2017 to March. The feeling is that Beijing is still Carrefour and Jing Kelong and WAL-MART and Hualian. The electricity supplier which Ma Yun’s Alibaba still feels more powerful. Now Hebei province and all parts of the country have venture capital and subsidy, which will undoubtedly promote the Internet of things.

Based on the traditional city, smart city is a new city that is constructed by Internet of things, mobile Internet, cloud computing, big data and other information technologies. Smart city can change interaction way of people, improve the real-time processing capability of information and response speed; promote comprehensive and sustainable development of various social works of smart city. But because Internet of things connected the real world and cyber space, in the smart city operation, not only include the traditional security problems, for example, the related equipment and building destruction, also include through software and instruction to damage network information, city construction and so on, which will bring a big challenge to smart city operation. To solve the security problem of smart city should put forward a system security mode from the view of system engineering. Through analyzing smart city architecture and security risk, put forward smart city security platform, studied the physical equipment protection, information security technologies, effective management, laws, personal information security literacy of smart city.

Internet of Things (IoT) and Big Data have played a significant role in the development and sustainability of Smart Cities. They are the backbones of Smart Cities' efforts to connect human resources, social capital, and ICT infrastructure to address public challenges, achieve sustainable development and improve people's standard of life. It helps Smart Cities improve public services, such as water management, healthcare, education, security issues, transportation, energy usage, and other community to achieve a better life. Big Data helps to analyze, store and back-ups a massive amount of data generated in smart cities in seconds day by day. For this matter, different analytical tools are employed, such as data analytics, machine learning, statistics, and data mining. Even though there are many benefits offered to smart cities; the Smart Cities encounters challenges that need to be addressed. Many of these challenges focus on security and privacy, infrastructures and operational and handling massive data. This paper describes the overview of IoT, Big Data, and Smart Cities. The Applications of IoT and Smart Cities were also discussed; the features of Smart Cities and Big Data; Different challenges of IoT, Big Data, and Smart Cities are described. Also, the proposed research work has presented the relationship between IoT and Big Data to Smart Cities; the role of IoT and Big Data in smart cities was identified; finally, the future directions for IoT and Big Data regarding Smart Cities are discussed.

Major advances in the Internet of Things (IoT) technologies are offering unprecedented automated services to various sectors. IoT has shown great promise in transforming buildings and institutions into smart buildings and institutions, making traditional task management more efficient. In particular, there has been remarkable progress in successfully integrating IoT into various contexts, such as smart homes, businesses and cities. Success in such technological transformations is not only due to the proper deployment and implementation of IoT but also to the effective employment of related technologies, such as big data and cloud computing technologies, and making informed decisions on the inclusion of the required hardware and software for all the elements involved in the IoT environment. While a significant amount of IoT research in various contexts has been proliferated in recent years, attention given to its deployment for linguistic landscapes (LLs) is still very rare. The aim of this paper is to propose a smart Al-Noor Mall LL which takes into account the appropriate deployment of IoT tools and relevant technologies. The paper also thoroughly discusses the background of the field, covering IoT technologies, cloud computing, and big data studies, followed by a review of the recent publications involving the successful deployment of IoT technologies combined with big data and cloud computing in various contexts. The literature also covers the success factors that contribute to the robustness of the required real-time operating system (RTOS) in an IoT-based environment in order to enable IoT devices to manage their resources efficiently. Based on a detailed analysis of the literature, a three-level framework (i.e. the object, communications and application levels) together with the elements that are required for IoT-based smart LL solutions is proposed. A case study of Al-Noor Mall is used to validate the proposed framework and the results indicate that IBM Bluemix can help Al-Noor Mall management and customers to locate any particular shop in the huge shopping mall, which thereby makes the customers’ shopping experience more satisfying. Finally, the challenges (i.e. energy efficiency, security, and intelligent data analysis) facing the successful deployment of IoT technologies are discussed with proposals for future research directions in the IoT research field.

ABSTRACTTraditional protection measures are difficult to cope with in complex and changeable network attacks. This paper presents an innovative construction of a hybrid model based on the combination of graph neural network (GNN) and random forest (RF), and uses an adaptive feature selection mechanism and multi‐task learning strategy to carry out network security big data prediction analysis. Through experiments on CICIDS 2017 and KDD Cup 1999 datasets, the results show that the model has excellent performance in detecting various types of attacks, with an accuracy of up to 0.94 in DoS attack detection of the CICIDS 2017 dataset, and maintains strong robustness in different noise environments. The model performs outstandingly in training efficiency, generalization ability, and multi‐task learning performance, and significantly improves the accuracy and efficiency of network security threat prediction. Research shows that this model has opened up a new path for network security protection, has important value in dealing with complex network attack scenarios, and provides strong support for the development of the network security field.

Big data and artificial intelligence based early risk warning system of fire hazard for smart cities

An amazing utilization of internet of things (IoT)-based smart technologies has been observed in recent years. Internet of things (IoT) became smarter and autonomous, leading the way by interconnecting and interacting with the environment. Existing technologies such as big data, AI, and cloud computing have been supported by IoT but with some limitations. Blockchain is the better solution to access and share data with the decentralized system. IoT spreads its applications in healthcare, industrial internet of things, supply chain management, traffic management, etc., out of which smart cities take special attention in recent days by integrating with IoT to create a sustainable ecosystem by using information and communication technology (ICT). Smart cities can offer various smart applications for boosting the life prominence of society. These characteristics of blockchain are beneficial to the creation of smart cities. In this regard, the authors have conducted a systematic review of articles. This study highlights the integration of blockchain with IoT.

With the rapid development in the internet, cloud computing, Internet of Things, mobile internet, and other information technologies, the big data technology is widely used in all aspects of smart city construction. This study analyzes the development status of big data in urban planning and construction and studies the application of the big data technology in smart city construction. This study mainly analyzes and studies the aspects of smart city planning, smart transportation, smart community, smart health care, and smart education and finally puts forward relevant thoughts.

Leveraging Deep Learning and IoT big data analytics to support the smart cities development: Review and future directions

Substation network security is the key to maintaining the stable operation of power systems. In the face of growing threats of network attacks, traditional security protection measures have been brutal to meet the needs of modern power systems. Research on substation network security, situational awareness strategies, and remote operation and maintenance of equipment is essential to improve network defense capability and ensure the continuity and reliability of power supply. This study explores effective security situational awareness methods and remote operation and maintenance techniques to provide new solutions for substation network security. This paper builds an efficient network attack detection model by introducing linear discriminant analysis (LDA) and radial basis function (RBF) neural networks. The experiment uses the KDD Cup99 dataset, which is preprocessed to provide the model training and testing data. The LDA-RBF model in this paper outperforms the traditional RNF neural and BP neural networks regarding recognition rate. Specifically, the recognition rate reaches 90.2% for the Smurf attack and 100% for the Ipsweep attack. The proposed model of the study also performs well in terms of leakage and false alarm rates, with an overall recognition rate of 97.00%. This study proposes a network security situational awareness strategy and equipment remote operation and maintenance method that can effectively enhance substation networks’ security and operation and maintenance efficiency.

The concept of smart cities transformed as global implementation is hampered by a host of technical, economic, and government road barriers. In addition, the integration of Internet of Things (IoT) and big data technology has opened new horizons in terms of smart home technology and smart city development that is yet to be explored. IoT is a technology that allows devices to connect to the Internet, and applications are designed to meet the needs of the community. Communities are very interested in using this and other information technology in cities to build smart cities in recent years. It is important to consider big data analytics (BDA) when using IoT in a small ecosystem because there is less data to be managed than a big city where there is data from more smart devices and people BDA aims to make data informed decisions on the basis of raw facts provided. Aside from the fact that there are many articles in the literature that talk about the BDA as a decision-maker in smart cities, general research on BDA and smart cities focuses on the technical or intellectual properties of a particular smart city.

A walkthrough of the emerging IoT paradigm: Visualizing inside functionalities, key features, and open issues

With the rapid development of information technology and the advancement of smart city construction, urban management and services increasingly rely on networked intelligent systems. However, this highly interconnected network environment has also brought numerous network security challenges to smart cities. Smart cities involve a large number of Internet of Things (IoT) devices, big data platforms, cloud computing technologies, and smart infrastructure, making network security problems more complex and severe. This paper analyzes the composition and development trends of smart cities, explores the main security risks currently faced, and focuses on the security risks in key areas such as the Internet of Things, cloud computing, big data, and intelligent transportation systems. Furthermore, this paper proposes a multi-layered protection system and strategies for smart city network security, aiming to improve the network security assurance level of smart cities. The research seeks to provide practical protection measures to help smart cities cope with the growing security threats and ensure their sustainable development.

The research relevance is determined by the growing threats in cybersecurity, which require improved methods of detecting vulnerabilities in software, with the help of the latest technologies such as artificial intelligence (AI). The study aimed to determine the effectiveness of AI for vulnerability testing as an alternative to traditional security testing methods, particularly pentesting. To achieve this goal, machine learning algorithms were analysed, a hybrid model for vulnerability detection was developed, and the use of intrusion detection and prevention systems, automated approaches to software testing, and application interface security was addressed. The study determined that decision trees quickly classify traffic but overlearn; support vector machines accurately analyse logs but are sensitive to settings; naive Bayesian classifiers effectively filter messages but are limited by assumptions; neural networks and deep learning detect complex threats but require a lot of data; the k-nearest neighbours’ algorithm is suitable for small systems but slow; and random forests are accurate in code analysis but less interpretable. AI is fast and scalable but limited in understanding context and responding to new threats. The results showed that pentesting prevailed in detecting complex vulnerabilities, and the hybrid machine learning model achieved 93% accuracy and 100% prediction accuracy but missed 17% of vulnerabilities. For their part, cybersecurity technologies such as application interface testing, bug bounty programmes, security integration into development, intrusion detection systems, end-to-end encryption, IoT security and phishing are also effective, but need to be adapted to new threats. The results of the study can be used by cybersecurity companies, software developers, and security engineers to improve vulnerability testing and cyber threat protection processes, including with the help of AI-based tools

Smart city with Chinese characteristics against the background of big data: Idea, action and risk