Abstract:
The emergence of the pervasive device has made log-in details more vulnerable to unauthorized access and damage. This is due to frequent changes in users of pervasive devices and the close affinity of many attackers. Most models available only prevent attackers from gaining access to user login details. This study proposed a model that both detects and reveals the attacker's identity using the strength of the Honey Encryption algorithm with the ability to build a randomized message encoding called a Distribution-Transforming Encoder (DTE). The proposed model has the capability of providing a guide to security operatives to track and arrest the suspected perpetrator. An evaluation of the model was carried out which shows a 62% success of revealing attackers. A further examination of the model shows that 21% of the attackers could gain access through close affinity to log-in users. An extension of the proposed model can be achieved by improving the detection rate of the model.
