## 1 Introduction  
Cloud computing offers computing resources to users through the network (Internet) [1].  
The network is utilized by cloud service providers for communication, along with exchanging their private and confidential data [2]. It is vulnerable to numerous attacks like Denial of Services (DoS), exploits, generic, reconnaissance, shellcode, zero-day attack, Advanced Persistent Threat (APT), worms, and so on, owing to the cloud's distributed nature [3, 4]. The server's resource consumption starts elevating rapidly when such an attack is launched [5]. Owing to auto-scaling, assigning more resources to this server is commenced by the cloud manager [6]. If the attack is unidentified and unmitigated, the resource allocation process continues making a huge loss [7]. Moreover, the running of numerous Virtual Machines (VMs) occurs on a single physical server. A VM affects other VMs running on the same server when it is under attack [8]. Extortion and protest are the crucial motives behind these attacks [9]. A major concern for the latest attack paradigms is cloud security along with engendering severe damage to the cloud [10]. Therefore, a Cyber-Security Risk Management (CSRM) plan is requisite to tackle these issues, and limit the possibility of correlated risks, along with the readiness for cyber resiliency [11]. It is the process of locating cyber-security potential risks along with planning defenses to desist those risks [12]. Recently, the intensity of these attacks on the cloud has increased.  
To combat these attacks, simple deep neural network models and Machine Learning (ML) models are utilized by most CSRM plans to detect cyber-attacks on hosts and network systems [13]. A simple deep neural network consisting of one or two Hidden Layers (HL) is Artificial Neural Network (ANN). However, many HLs, together with different architectures, are included in a deep network [14]. Owing to its capability to study computation processes in-depth, which mimics the human brain's natural behavior, most researchers broadly utilize Deep Learning (DL) [15]. Moreover, extremely high false-positive rates have been seen using this method, along with difficulty in detecting the latest attack types [16]. Some limitations like extensive training time needs, low detection accuracy, along with a high rate of false alarms were shown by conventional ML techniques [17]. Consequently, typical adversary agnostic was shown by most Artificial Intelligence (AI) systems, i.e., they do not recognize the malicious attack's possibility. Hence, a high chance of an adversarial backdoor poisoning attack can exist. Additionally, false attack detection and data breaches were led by devoid of labelled samples [18]. Hence, this work has proposed intelligent cyber security defenses and protection using HT-RLSTM to tackle the above-mentioned problem.  
This paper's organization is given as follows: the associated works regarding the proposed methodology are surveyed in Section 2. The proposed methodology is explained in Section 3. Based on performance metrics, Section 4 illustrates the proposed methodology's results and discussion. Finally, Section 5 concludes the paper with future work.