JOURNAL OF ELECTRONIC AND CYBER DEFENCE
Year:2015 | Volume:3 | Issue:2 (10)|Papers Count:6

Vehicular Ad-hoc Networks (VANETs) can improve the communication between vehicles and traffic management to control appropriate. Authentication of messages which is issued by each vehicle is the most important problem in VANETs, because the wrong messages can cause crashes and change the traffic patterns of network. Many authentication schemes have been proposed in VANETs, each of which has advantages and disadvantages. In this paper first we introduce, a brief overview of these schemes. Then we analyse an authentication scheme for VANETs which introduced by Lee and Lai and introduce three scenarios that show their scheme is vulnerable to forgery attack. These attacks motivated us to design a novel and secure TPD based authentication scheme satisfying all security requirements in VANETs. Moreover, we introduce a simulation and comparison expressing the efficiency and performance of the proposed scheme.

Determination of plausible future of ungoing cyber-attacks enables the security analyst to make the best defense decisions based on achieved plausibility level. To achive the plausibility level of a situation, situational estimation and high level information fusion are used. In high level information fusion, for situation awareness of future and impact assessment of cyber-attacks four componnets of projecting, behaviour, capability, opportunity and intent are used.Almost all of the models used for projecting multi stage cyber-attacks assuming the four components independent from each other to simplify the implementation. Thus, they ignored the impact of the components on each other and their combination ability in projecting multi stage cyber-attacks. In this paper, we have presented a scheme based on belif model and fuzzy inference. Finally, the scheme has been evaluated using valid dataset, high stealth attacks and high impact and low impact attacks. The simulation results for the defined scenarios show accuracy increasy in projecting multi-stage cyber-attacks.

Progress of technology in recent decade causes that video transmission via communication channels has met high demands. Therefore, several methods have been proposed to improve the quality of video under channel errors. The aim of this paper is to increase PSNR for synthesized video by increasing channel encoder rate but in constant transmission rate. This is achieved by using intelligent neural network and Huffman used in the MPEG standard to compress transmitted data significantly. Then, depending to the amount of compression by the proposed method, the compressed data is coded again using secondary channel encoder. The proposed method is able to increase channel coding rate without increasing the amount of information for each frame. This method provides more robustness for video frames against channel errors. The proposed method is tested for different source coding rates and several SNRs for channel and the obtained results are compared with state-of-the-art methods.

One of the common challenges in wireless communications networks is wiretapping broadcast signals from radio stations by eavesdroppers. In this paper is shown that interference alignment (IA) technique can be completely eliminated intra and inter-cell interferences, in a cellular communications network with the same frequency allocation to all of the users. Also, this technique is able to provide maximum degrees of freedom (DoF) and securing the communication against eavesdropping. IA approximately can close the rate of network secrecy to rate region of the interfering broadcast channel (IFBC) in high SNR regime. In order to prove the mathematical and intuitive capability of the interference alignment technique in increasing communication security, a kind of IA algorithm closed-form is used in the downlink of an unsecured cellular network. Simulation results are demonstrated for a certain range from the number of eavesdropper’s receive antennas, that secrecy rate well possible come close to the network sum-capacity in high SNRs.

Error correction capability of convolutional codes is improved by increasing code constraint length. However, increasing the constraint length results in high complexity of optimum Viterbi decoding algorithm because the number of computations in Viterbi algorithm is exponentially proportional to the constraint length. Consequently, decoding of high constraint length convolutional codes using Viterbi algorithm may practically be impossible. Sub-optimum decoding algorithms such as Fano and Stack algorithms have been proposed to feasible fast sequential decoding of high constraint length convolutional codes. In this paper, we introduce different methods of implementing Fano and Stack algorithms and propose some techniques to improve their speed and required memory. We compare the introduced implementations of the algorithms in terms of error correction capability, decoding time and required memory. Furthermore, we use simulation results to show that if the communication error is low, Fano and Stack algorithms can provide the same error correction capability as the optimum Viterbi algorithm in very short decoding time.

In this paper, channel estimation and data detection under nonlinear time-varying channel are investigated. The model of nonlinear time varying channel that we focused on is known as switching state space model (SSSM). This model combines the hidden Markov model (HMM) and the linear state space model (LSSM). In this paper based on the EM approach, we propose a new iterative method for joint data detection and channel estimation. Monte Carlo simulations show that the bit error rate (BER) of the proposed scheme is close to BER of the Viterbi algorithm (VA) with perfect channel state information (CSI).