Biblio

Security has always been a major issue in cloud. Data sources are the most valuable and vulnerable information which is aimed by attackers to steal. If data is lost, then the privacy and security of every cloud user are compromised. Even though a cloud network is secured externally, the threat of an internal attacker exists. Internal attackers compromise a vulnerable user node and get access to a system. They are connected to the cloud network internally and launch attacks pretending to be trusted users. Machine learning approaches are widely used for cloud security issues. The existing machine learning based security approaches classify a node as a misbehaving node based on short-term behavioral data. These systems do not differentiate whether a misbehaving node is a malicious node or a broken node. To address this problem, this paper proposes an Improvised Long Short-Term Memory (ILSTM) model which learns the behavior of a user and automatically trains itself and stores the behavioral data. The model can easily classify the user behavior as normal or abnormal. The proposed ILSTM not only identifies an anomaly node but also finds whether a misbehaving node is a broken node or a new user node or a compromised node using the calculated trust factor. The proposed model not only detects the attack accurately but also reduces the false alarm in the cloud network.

Cloud computing is a standard architecture for providing computing services among servers and cloud user (CU) for preserving data from unauthorized users. Therefore, the user authentication is more reliable to ensure cloud services accessed only by a genuine user. To improve the authentication accuracy, Tiger Hash-based Kerberos Biometric Blowfish Authentication (TH-KBBA) Mechanism is introduced for accessing data from server. It comprises three steps, namely Registration, Authentication and Ticket Granting. In the Registration process, client enrolls user details and stores on cloud server (CS) using tiger hashing function. User ID and password is given by CS after registration. When client wants to access data from CS, authentication server (AS) verifies user identity by sending a message. When authenticity is verified, AS accepts user as authenticated user and convinces CS that user is authentic. For convincing process, AS generates a ticket and encrypted using Blowfish encryption. Encrypted ticket is sent back to user. Then, CU sends message to server containing users ID and encrypted ticket. Finally, the server decrypts ticket using blowfish decryption and verifies the user ID. If these two ID gets matched, the CS grants requested data to the user. Experimental evaluation of TH-KBBA mechanism and existing methods are carried out with different factors such as Authentication accuracy, authentications time and confidentiality rate with respect to a number of CUs and data.

Most centralized systems allow data access to its cloud user if a cloud user has a certain set of satisfying attributes. Presently, one method to compete such policies is to use an authorized cloud server to maintain the user data and have access control over it. At times, when one of the servers keeping data is compromised, the security of the user data is compromised. For getting access control, maintaining data security and obtaining precise computing results, the data owners have to keep attribute-based security to encrypt the stored data. During the delegation of data on cloud, the cloud servers may be tampered by the counterfeit cipher-text. Furthermore, the authorized users may be cheated by retorting them that they are unauthorized. Largely the encryption control access attribute policies are complex. In this paper, we present Cipher-text Policy Attribute-Based Encryption for maintaining complex access control over encrypted data with verifiable customizable authorization. The proposed technique provides data confidentiality to the encrypted data even if the storage server is comprised. Moreover, our method is highly secured against collusion attacks. In advance, performance evaluation of the proposed system is elaborated with implementation of the same.

Cloud data centers are critical infrastructures to deliver cloud services. Although security and performance of cloud data centers have been well studied in the past, their networking aspects are overlooked. Current network infrastructures in cloud data centers limit the ability of cloud provider to offer guaranteed cloud network resources to users. In order to ensure security and performance requirements as defined in the service level agreement (SLA) between cloud user and provider, cloud providers need the ability to provision network resources dynamically and on the fly. The main challenge for cloud provider in utilizing network resource can be addressed by provisioning virtual networks that support information centric services by separating the control plane from the cloud infrastructure. In this paper, we propose an sdn based information centric cloud framework to provision network resources in order to support elastic demands of cloud applications depending on SLA requirements. The framework decouples the control plane and data plane wherein the conceptually centralized control plane controls and manages the fully distributed data plane. It computes the path to ensure security and performance of the network. We report initial experiment on average round-trip delay between consumers and producers.