Biblio

A privately owned smart device connected to a corporate network using a USB connection creates a potential channel for malware infection and its subsequent spread. For example, air-gapped (a.k.a. isolated) systems are considered to be the most secure and safest places for storing critical datasets. However, unlike network communications, USB connection streams have no authentication and filtering. Consequently, intentional or unintentional piggybacking of a malware infected USB storage or a mobile device through the air-gap is sufficient to spread infection into such systems. Our findings show that the contact rate has an exceptional impact on malware spread and destabilizing free malware equilibrium. This work proposes a USB authentication and delegation protocol based on radiofrequency identification (RFID) in order to stabilize the free malware equilibrium in air-gapped networks. The proposed protocol is modelled using Coloured Petri nets (CPN) and the model is verified and validated through CPN tools.

Most existing approaches focus on examining the values are dangerous for information flow within inter-suspicious modules of cloud applications (apps) in a host by using malware threat analysis, rather than the risk posed by suspicious apps were connected to the cloud computing server. Accordingly, this paper proposes a taint propagation analysis model incorporating a weighted spanning tree analysis scheme to track data with taint marking using several taint checking tools. In the proposed model, Android programs perform dynamic taint propagation to analyse the spread of and risks posed by suspicious apps were connected to the cloud computing server. In determining the risk of taint propagation, risk and defence capability are used for each taint path for assisting a defender in recognising the attack results against network threats caused by malware infection and estimate the losses of associated taint sources. Finally, a case of threat analysis of a typical cyber security attack is presented to demonstrate the proposed approach. Our approach verified the details of an attack sequence for malware infection by incorporating a finite state machine (FSM) to appropriately reflect the real situations at various configuration settings and safeguard deployment. The experimental results proved that the threat analysis model allows a defender to convert the spread of taint propagation to loss and practically estimate the risk of a specific threat by using behavioural analysis with real malware infection.