## 1 Introduction  
IoT has impacted our everyday lives due to its supreme advancement and it is a rising technology that provides an exclusive connection for making available automated operations and services in diverse fields [1]. IoT is perceived as a method with a suitable system interrelated via servers, sensors, and different software. Due to their lesser processing power, IoT appliances are deployed in several security areas. As additional devices are deployed on IoT, security and privacy issues are gaining progressive deliberation [2]. The IoT attacks are leading to implausible losses to IoT networks and even threatening human security. Moreover, IoT networks contain limited characteristics that make attack exposure more difficult. Initially, the range of platforms, hardware, software, and protocols exposes diverse vulnerabilities. Subsequently, higher-rate and lower-rate attacks are widely used by IoT hackers to hack legitimate data [3]. These lower-rate attacks are complicated to notice and reside on the network. Finally, attackers become more intelligent and can vigorously modify their attack strategies as per ecological feedback to avoid detection, making it more complex for defenders to find out consistent patterns to recognize attacks [4].  
As the number of IoT devices increases, attackers have many opportunities to negotiate them through malicious email, secrecy attacks, and denial of service (DoS) attacks, amongst other types of attacks [5]. Attacks can come from the channels that unite the IoT elements. The protocols deployed in IoT systems include security issues that affect the whole system.  
IoT systems are also susceptible to renowned network attacks such as DoS and spoofing of software and appliances [6].  
Certain reports declared that 70% of IoT devices are subjected to different network attacks that cause diverse vulnerabilities, such as encryption and password security. The domain wherein IoT is broadly used are smart homes, intellectual transport systems, agriculture, hospitals, earthquake discovery, and so on [7; 8]. For the malevolent agents, IoT appliance is vulnerable making the IoT devices a launcher pad for attacks on varied domains [9]. Thereby, a protected IoT infrastructure is essential for protecting IoT devices from attacks. In this paper, a hybrid deep learning model that identifies security attacks in an IoT environment is implemented. Two deep learning model is used: GRU and Bi-LSTM, weights of Bi-LSTM are optimally tuned via a self-upgraded cat and mouse optimizer (SU-CMO) algorithm to refine classification accuracy. Furthermore, two separate datasets are utilized to compare the proposed model's classification accuracy to other existing schemes.  
The present paper follows mainly three steps which are as under:  
* Suggests a new attack detection model in IoT, where various diverse features are derived.
* Deploys hybrid classifiers such as GRU and BI-LSTM with an optimization strategy to detect attacks.
* Exploits an SU-CMO model to choose the optimal weights in Bi-LSTM.  
Section 2 reviews the work. Section 3 briefs a stepwise description of the proposed model and Section 4 explains about the extraction of diverse features. Section 5 depicts SU-CMO-based hybrid classification. Section 6 illustrates outcomes and the conclusion is presented in Section 7.