There are increased security challenges that target cloud systems. One of the most important requirements of users in cloud storage is protecting their cloud from attacks and keeping data secure. Modern technologies of machine learning are providing the ability to analyze and classify data perfectly. This paper proposes a model placed between users and the cloud, which is based on two phases. The first of which is protecting the cloud from different types of network attacks and detecting normal and abnormal flow. The second one is categorizing the users' data and then encrypting it based on its importance using different encryption algorithms. The accuracy results of random forest (RF) and decision tree (DT) are 100% of attack detection for each one. For the second phase of classifying data, the algorithms used are the logistic regression (LR) and stochastic gradient descent (SGD) learning which resulted in 98% accuracy for both. Besides, the encryption algorithms that have been adopted are rivest cipher (RC4), triple data encryption (3DES), and advanced encryption standard (AES) for encryption of the classified data according to the importance which will be then stored in the cloud in its secure form.

Wireless sensor network (WSN) security is an important component for protecting data from an attacker. For improving security, cryptography technologies are divided into two kinds: symmetric and asymmetric. Therefore, the implementation of protocols for generating a secret key takes a long time in comparison to the sensor’s limitations, which  decrease network throughput because they are based on an asymmetric method. The asymmetric algorithms are complex and decrease network throughput. In this paper, an encryption symmetric secret key in wireless sensor networks (WSN) is proposed. In this work, 24 experiments are proposed, which are encryption using the AES algorithm in the cases of 1 key, 10 keys, 25 keys, and 50 keys. In each experiment, two chains are combined by using a hash function (SHA-2) to produce secret keys. The Network Simulator Version 2 (NS2) was used to assess the network throughput for the generated key. The randomness of the suggested LWM method has been tested by using the Diehard statistical test and the Entropy test. The results of the tests show that the encryption secret keys have a high level of data randomness.

No abstract