Although the number of cloud projects has dramatically increased over the last few years, ensuring the availability and security of project data, services and resources is still a crucial and challenging research issue. Distributed denial of service (DDoS) attacks are the second most prevalent cybercrime attacks after information theft. DDoS TCP flood attacks can exhaust the cloud’s resources, consume most of its bandwidth, and damage an entire cloud project within a short period of time. The timely detection and prevention of such attacks in cloud projects are therefore vital, especially for eHealth clouds. In this paper, we present a new classifier system for detecting and preventing DDoS TCP flood attacks (CS_DDoS) in public clouds. The proposed CS_DDoS system offers a solution to securing stored records by classifying the incoming packets and making a decision based on the classification results. During the detection phase, the CS_DDOS identifies and determines whether a packet is normal or originates from an attacker. During the prevention phase, packets which are classified as malicious will be denied access to the cloud service and the source IP will be blacklisted. The performance of the CS_DDoS system is compared using the different classifiers of the least squares support vector machine (LS-SVM), naïve Bayes, K-nearest, and multilayer perceptron. The results show that CS_DDoS yields the best performance when the LS-SVM classifier is adopted. It can detect DDoS TCP flood attacks with about 97% accuracy and with a Kappa coefficient of 0.89 when under attack from a single source, and 94% accuracy with a Kappa coefficient of 0.9 when under attack from multiple attackers. Finally, the results are discussed in terms of accuracy and time complexity, and validated using a K-fold cross-validation model.

The acceleration in developments in communication technology has led to a consequent increase in the vulnerability of data due to penetration attacks. These attacks often came from outside where non-qualified companies develop IT projects. Cryptography can offer high levels of security but has recently shown vulnerabilities such as the man-in-the-middle (MITM) attack in areas of key exchange protocols, especially in the Diffie-Hellman (DH) protocol. Firstly, this paper presents an overview of MITM attacks targeted at the DH protocol then discusses some of the shortcomings of current defenses. A proposed method to secure DH, which helps secure systems against MITM attacks, is then presented. This method involves the use of Geffe generation of binary sequences. The use of Geffe generator offers high levels of randomness. Data hashed and encrypted using this proposed method will be so difficult to intercept and decrypt without the appropriate keys. This offers high levels of security and helps prevent MITM attacks.

The proliferation and usefulness of cloud computing in eHealth demands high levels of security and privacy for health records. However, eHealth clouds pose serious security and privacy concerns for sensitive health data. Therefore, practical and effective methods for security and privacy management are essential to preserve the privacy and security of the data. To review the current research directions in security and privacy in eHealth clouds, this study has analysed and summarized the state of the art technologies and approaches reported in security and privacy in the eHealth cloud. An extensive review covering 132 studies from several peer-reviewed databases such as IEEE Xplore was conducted. The relevant studies were reviewed and summarized in terms of their benefits and risks. This study also compares several research works in the domain of data security requirements. This paper will provide eHealth stakeholders and researchers with extensive knowledge and information on current research trends in the areas of privacy and security.