Many encryption strategies have been applied to ensure data confidentiality and improve cloud security. The most recent cryptosystems are based on homomorphic (HE), attribute-based (ABE), and hybrid encryption. However, most of them suffer from numerous drawbacks: high time consumption, encrypted message size, and some vulnerabilities. Hence, a secure method is highly required to get a satisfying security level while keeping the computational complexity reduced. This paper outlines a novel technique that combines elliptic curve cryptography (ECC) and Blockchain technology. The data is first encoded using the Elliptic Curve Integrated Encryption Scheme, then signed using signed using the Elliptic Curve Digital Signature Algorithm, and finally confirmed by the blockchain network before being stored in the cloud. The performance evaluation results prove that the proposed system is highly resistant to man-in-the-middle and replay attacks and performs better than a set of existing solutions in terms of cryptography cost, encryption/decryption time, and algortithm complexity.

On one hand, there are many proposed intrusion detection systems (IDSs) in the literature. On the other hand, many studies try to deduce the important features that can best detect attacks. This paper presents a new and an easy-to-implement approach to intrusion detection, named distance sum-based k-nearest neighbors (DS-kNN), which is an improved version of k-NN classifier. Given a data sample to classify, DS-kNN computes the distance sum of the k-nearest neighbors of the data sample in each of the possible classes of the dataset. Then, the data sample is assigned to the class having the smallest sum. The experimental results show that the DS-kNN classifier performs better than the original k-NN algorithm in terms of accuracy, detection rate, false positive, and attacks classification. The authors mainly compare DS-kNN to CANN, but also to SVM, S-NDAE, and DBN. The obtained results also show that the approach is very competitive.

Distributed denial of service (DDoS) attacks have become a serious danger against the availability of services in cloud computing environment. Current defending mechanisms cannot detect DDoS attacks with high accuracy. This is mainly due to the fact that the unrealistic value of the studied variables was used. In view of this problem, the authors propose a novel approach called confidence interval-based filtering (CIF) to detect DDoS attacks. The proposed approach is implemented using VMware and JAVA applications. The simulation results showed that CIF outperforms the existing approaches in terms of detection rate and false negative and positive rates with an acceptable computation time.