This paper proposes an audio tampering detection method based on the ENF phase and BI-LSTM network from the perspective of temporal feature representation learning. First, the ENF phase is obtained... Show More

This paper proposes an audio tampering detection method based on the ENF phase and BI-LSTM network from the perspective of temporal feature representation learning. First, the ENF phase is obtained by discrete Fourier transform of ENF component in audio. Second, the ENF phase is divided into frames to obtain ENF phase sequence characterization, and each frame is represented as the change information of the ENF phase in a period. Then, the BI-LSTM neural network is used to train and output the state of each time step, and the difference information between real audio and tampered audio is obtained. Finally, these differences were fitted and dimensionally reduced by the fully connected network and classified by the Softmax classifier. Experimental results show that the performance of this method is better than the state-of-the-art approaches.

The objective of this paper is to examine a model to identify Social Engineer Attack Phases to improve the security countermeasures by Social-Engineer Involvement. A questionnaire was developed and... Show More

The objective of this paper is to examine a model to identify Social Engineer Attack Phases to improve the security countermeasures by Social-Engineer Involvement. A questionnaire was developed and distributed to a sample of 243 respondents who were actively engaged in 3 Jordanian telecommunication companies. All hypotheses were tested using (PLS-SEM). The results of the study indicate that Social Engineer Attack Phases (Identification the potential target, Target Recognition, Decision approach, and Execution) have a partially mediate and significant impact on improving the security countermeasures by Social-Engineer Involvement. On the other hand, the Social Engineer Attack Phases (Information Aggregations, Analysis and Interpretation, Armament, and Influencing) have a fully mediate and significant impact on improving the security countermeasures by Social-Engineer Involvement. The findings of this study help to provide deep insight to help security professionals prepare better and implement the right and appropriate countermeasures, whether technical or soft measures.