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The IoT points to an ever-increasing network containing the things which are not only conventional computers or mobile objects, but also the physical things similar to temperature sensors, wearable devices, watches, and other smart objects. Academics, industries, and governments are interested in studying how to connect all things in the world to the internet, called Internet of Things. Its applications contain large numbers of devices (perceptions), which are difficult to implement security methods such as encryption because of the restrictions on time, memory, processing, and energy constraints [1]. Recently, the smart devices have increased with the increased availability of distributed networks. From Figure 1. We should know that the number of devices connected to it is increasing through a positive relationship with the time. Also, the market of IoT is increasing with timeIoT is attracting for many organizations, one of them is Cisco internet business solutions group (IBSG), which reported that IoT is advantageous when the number of "objects/things" connected to the Internet is greater than the human"s connection [3]. IoT enables several applications and objects for connecting with each other through the internet to a certain scale, facilitating communication and access to information, including business as-well-as technologies related challenges to realize business benefits. These applications and objects impose a strict challenge to the security of the IoT environment and systems. For example, privacy, confidentiality, integrity, authentication, and authorization of IoT system. Moreover, the IoT environment should provide solutions for other challenges such as reliability, performance, availability, mobility, management, interoperability, scalability, and big data. IoT security is a major area of concern, it is the most impacted challenges for IoT [4].IoT architecture is divided into three layers: physical (aka perception/sensing) layer, network layer, and

The security of IoT that is based on layered approaches has shortcomings such as the redundancy, inflexibility and inefficiently of security solutions. There are many harmful attacks in IoT network such as DoS and DDoS attacks which can compromise the IoT architecture in all layers. Consequently, cross layer approach is proposed as an effective and practical security defending mechanism. Cross-Layer Distributed Attack Detection model (CLDAD) is proposed to enhance security solution for IoT environment. CLDAD presents a general detection method of DDoS in sensing layer, network layer and application layer. CLDAD is based on big data analytics techniques which enable the detection process to be performed in distributed way, so the model can detect DDoS attacks in any layer on-the-fly and the model support the scalability of the IoT environment. CLDAD is tested based on three datasets, namely, artificial jamming attack dataset, BoT-IoT dataset, and BoT-IoT based HTTP. The results showed that the proposed model is efficient in detecting attacks in the three layers of the IoT.

Nowadays, Internet of Things (IoT) is considered as part our lives and it includes different aspects - from wearable devices to smart devices used in military applications. IoT connects a variety of devices and as such, the generated data is considered as ‘Big Data'. There has however been an increase in attacks in this era of IoT since IoT carries crucial information regarding banking, environmental, geographical, medical, and other aspects of the daily lives of humans. In this paper, a Distributed Attack Detection Model (DADEM) that combines two techniques - Deep Learning and Big Data analytics - is proposed. Sequential Deep Learning model is chosen as a classification engine for the distributed processing model after testing its classification accuracy against other classification algorithms like logistic regression, KNN, ID3 decision tree, CART, and SVM. Results showed that Sequential Deep Learning model outperforms the aforementioned ones. The classification accuracy of DADEM approaches 99.64% and 99.98% for the UNSW-NB15 and BoT-IoT datasets, respectively. Moreover, a plan is proposed for optimizing the proposed model to reduce the overhead of the overall system operation in a constrained environment like IoT.