# DeepDCA: Novel Network-Based Detection of IoT Attacks Using Artificial Immune System
## 1 Introduction  
Recently in the academic and industrial circles, _Internet of Things_ (IoT) have became an active area. According to Cisco, 500 billion devices will be connected by the year 2030 [1]. Although this technology is promising in many sectors, such as uch as smart homes, health-care, intelligent transportation, power smart grid and numerous areas that not yet even conceived [2], it carries with it many security risks. Easy accessibility and tremendous propagation of IoT devices creates a fertile environment for cyber attacks. Most of these devices are small, inexpensive and have limited memory and computing capacity to run the current existing security software [3]. Additionally, the _Original Equipment Manufacturers_ (OEMs) are using commercial embedded _Real-Time Operating Systems_ (RTOS), such as FreeRTOS and OpenRTOS to minimize the cost [4] which makes these end devices vulnerable to be targeted. As stated by a report for Malwarebytes, the IoT attacks will continue at steady levels with increased sophistication [5]. For example, recent malware such as Mirai [6] and _Ransomware of Things_ (RoT) [7, 8]proven that conventional security methods are ineffective and do not provide decentralized and strong security solutions. In addition to the urgent need for a new paradigm of security commensurate with the changes that have emerged with the IoT ecosystem. Where the security problems inherited from the traditional network alongside the _Advanced Persistent Threat_ (APT). Therefore, to dissolve the obstacles security of IoT we must look at solutions from a comprehensive perspective and take into account new circumstances and requirements. Lately, multiple solutions have been applied to secure the IoT environment and guarantee security requirements like authentication, availability, integrity, confidentiality and privacy [9]. From another aspect, the _Artificial Immune System_ (AIS) is a bionic intelligent system that mimics the biological immune system and its way to protect against foreign or dangerous invaders [10]. AIS has proven effective in protecting TCP/IP networks [11; 12], _Wireless Sensor Networks_ (WSN) [13; 14], and _Mobile Ad Hoc Network_ (MANET) [15; 16]. This makes it more suitable for a dynamic and changeable environment such as IoT. Moreover, the human immune system properties make it a perfect approach to resolve IoT security dilemmas. Due to its ability to self-learning, adaptability robustness, resource optimization, dynamic structure, and lightweight [17] make it adapted to various applications such as computer security [18], intrusion detection [19; 20], anomaly detection [21], data analysis [22; 23], pattern recognition [24] and scheduling [25; 26]. Over and above, AIS methods solve multi-objective optimization problems successfuly [27; 28], control engineering [29; 30] and robotics [31]. After extensive IoT attacks, we reviewed the proposed recommendations and solutions to avoid infection. The suggested solutions were generally summed up to change the default passwords for the IoT devices, disable some ports, and guide consumers and manufacturers to use more secure devices. Although these security practices are effective and provide the first line of defense, their application is limited to security management and human interaction. Another challenge in the case of known software vulnerabilities is the delay of download the patches. Under these conditions, intrusion detection techniques become more important. Thus, the motivation for this study is that the traditional detection approaches are not able to efficiently detect new variants of IoT attacks. Consequently, it is urgent to study intrusion detection approaches in depth.Wherefore, the immune-based detection methods consider as a priority option due to its desirable properties. The artificial immune system has various algorithms detect different types of attacks. The second generation of these algorithms called Dendritic Cell Algorithm (DCA).