# Smart Attacks Learning Machine Advisor System for Protecting Smart Cities from Smart Threats
## 1 Introduction
Confidentiality, integrity, availability, privacy, access control, and non-repudiation are the most crucial security and privacy concepts that need to be understood [5]. In the disciplines of information, communication, and physics, they must be met. Internet technology has advanced quickly in response to the advancement of new cyber-technologies. Hence, the age of interconnectedness with everything has been reached today. With the advent of Internet technology, we have entered a new generation of connectivity that has elevated connectivity to a critical and integral part of our modern life, giving ease and progressing civilization. On the other hand, this technology has a slew of security issues brought on by hostile network attacks. According to Kaspersky Lab data released in the second quarter of 2018, over 962 million fraudulent intrusions were conducted in 187 nations worldwide, a steadily rising figure. Furthermore, cyber-attacks on mobile devices have demonstrated a random pattern of expansion because of the widespread use of mobile networking, exacerbating the severity of the crisis. Figure 2 shows that mobile threats in the second quarter of 2017 were around 1,300,000 threats, and this number kept increasing until it reached approximately 1,750,000 threats in the second quarter of 2018 [6], which illustrates how dealing with these intrusions by detecting and preventing them becomes more critical. Globally, the adoption of IoT has recently risen dramatically. In 2017, the number of linked IoT devices topped 27 billion, and these IoT devices would continue to overgrow in response to market demand, potentially reaching roughly 125 billion by 2030 [7]. Malicious attackers increasingly target essential infrastructure such as smart cities, electricity trans  
Figure 1: A smart city’s generic multi-tiered ICT architecture [4]. Figure 2: The situation of global mobile threats. mission, and other vital infrastructures. There will be significant problems in the future if these facilities are vulnerable to cyber-attacks. As a result, protecting equipment against malware activity has become an essential and pressing task, as such intrusions may pose serious risks. Intrusion Detection Systems (IDSs) have been a popular research area and a contentious problem, owing to the Internet's ever-increasing abundance of data. As a result, developing IDSs stands to reason, and it is a widely used operational security defensive strategy in the information industry. An IDS is a method/methodology for protecting application systems against malicious assaults, and it is the second line of defense. IDSs may be either network- or host-based. These may be used to defend a computer from the network or from end-user attacks. An IoT network and system, such as sensing equipment, might be an end-user device in the network [8]. Anomaly-based detection and signature-based detection are two types of detection techniques. By analyzing network traffic or data in computer memory for specified patterns, signature-based Detection approaches effectively recognize detected assaults. Anomaly-based detection monitors the behaviors of the whole objects, systems, or traffic, and compares them to predetermined behaviors that are thought to be normal, to find unknown threats. Any deviation from the usual operations is seen as a potential assault. Due to the complexity of attackers' techniques and the increase in zero-day assaults, an anomaly-based IDS is ideally suited to today's environment [9]. To detect abnormalities, most anomaly-based IDSs use Artificial Intelligence (AI), such as Machine Learning (ML) [9]. AI and Machine Learning aim to develop a computer that is capable of learning independently and distinguishing between normal and aberrant system behavior [10]. Many techniques for improving IDS performance have been advocated by academics, including Decision Tree (DT), artificial immune system, data mining, clustering-based methods, and statistics, etc. Various applications link massive IoT devices to real-world items in smart cities.