Biblio

This paper investigates the physical layer (PHY-layer) authentication that exploits channel state information (CSI) to enhance multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) system security by detecting spoofing attacks in wireless networks. A multi-user authentication system is proposed using convolutional neural networks (CNNs) which also can distinguish spoofers effectively. In addition, the mini batch scheme is used to train the neural networks and accelerate the training speed. Meanwhile, L1 regularization is adopted to prevent over-fitting and improve the authentication accuracy. The convolutional-neural-network-based (CNN-based) approach can authenticate legitimate users and detect attackers by CSIs with higher performances comparing to traditional hypothesis test based methods.

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.