الفهرس 
Chapter 1 IntroductionOnly 14 pages are availabe for public view
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Abstract
As a result of the scarcity of services and resources, such as medical, transportation, environment, and education, daily living in metropolitan areas would become even more difficult. The phrase ””smart city”” is used to adopt an application of mobile computing systems across all of a city’s components and levels via realistic data management networks. For being become smarter, cities are putting increasing emphasis on using technology for networked data management, such as Internet of Things (IoT), cloud computing, and big data. These data management systems enhance several elements of operations and organizations in the smart city, including traffic control, resource management that is sustainable, life quality, and infrastructures. Novel strategies for effective data management are necessarily needed to be developed to achieve the long-term sustainability of these. Intrusion Detection Systems (IDS) has been a popular research area and a contentious problem owing to the Internet’s ever-increasing abundance of data. As a result, developing IDS stands to reason, and it is a widely used operational security defensive strategy in the information industry. IDS is a method/methodology for protecting application systems against malicious assaults, and it is the second line of defense. IDS may be either network- or host-based. These may be used to defend a computer from the network or end-user attacks. The extensive use of IoT technology has recently enabled the development of smart cities. Smart cities operate in real-time to improve metropolitan areas’ comfort and efficiency. Sensors in these IoT devices are immediately linked to enormous servers, creating smart city traffic flow. This flow is rapidly increasing and is creating new cybersecurity concerns. Malicious attackers increasingly target essential infrastructure such as electricity transmission and other vital infrastructures. Software-Defined Networking (SDN) is a resilient connectivity technology utilized to address security concerns more efficiently. The controller, which oversees the flows of each appropriate forwarding unit in the SDN architecture, is the most critical component. The controller’s flow statistics are thought to provide relevant information for building an Intrusion Detection System (IDS). As a result, we proposed a five-level classification approach based on SDN’s flow statistics to develop a Smart Attacks Learning Machine Advisor (SALMA) system for detecting intrusions and for protecting smart cities from smart threats. Each layer in our proposed model has a single classifier. At each layer, the training dataset is separated into two categories. One of these categories is for each form of attack and the other for ””Other”” traffic or an assault that the next layer must identify. We utilize the ELM technique in all layers because of its resilience against noisy training data. Also, Extreme Learning Machine (ELM) technique is used at all levels. The proposed system was implemented on NSL-KDD and KDDCUP99 benchmark datasets, in which it achieved accuracy 96.9% and 99.5%, respectively. As a result, our approach provides an effective method for detecting intrusions in SDNs.