Cloud computing helps users to store and retrieve their data in the cloud online on an as-per-pay basis anytime, anywhere in the world. As a consequence, the security of data stored in the cloud serves as a key concern for cloud consumers due to ongoing hacking incidents in the cloud. Both cloud service providers (CSPs) and consumers face various attacks that pose a serious threat to both the organization and the user. Therefore, a robust intrusion detection system (IDS) needs to be configured to identify and avoid potential attacks in the cloud at an earlier stage. The state-of-the-art IDS system which used shallow machine learning techniques suffered from poor accuracy with a higher false alarm rate and higher response time. To overcome this problem, a hybrid semantic deep learning (HSDL) architecture is developed in this paper by integrating the long short-term memory (LSTM), convolutional neural network (CNN), and support vector machine (SVM) architectures. The semantic information present in the network traffic is identified using a semantic layer known as the Word2Vec embedding layer. The HSDL model classifies the intrusion present in the text along with its corresponding attack class. The normal text without any trace of intrusion is encrypted using an advanced encryption standard (AES) algorithm to enhance cloud storage security, and the optimal key with the largest key breaking time for the AES algorithm is selected using the crossover-based mine blast optimization algorithm (CMBA). The proposed HSDL scheme is evaluated and tested using two real-time intrusion detection benchmark datasets, namely NSL-KDD and UNSW-NB15. The proposed model achieves an accuracy of 99.98% for the NSL-KDD and 98.47% for the UNSW-NB15 dataset, respectively. The experimental and security analysis conducted proves the efficiency and robustness of the proposed scheme.