Biblio

Cloud computing systems (CCSs) enable the sharing of physical computing resources through virtualisation, where a group of virtual machines (VMs) can share the same physical resources of a given machine. However, this sharing can lead to a so-called side-channel attack (SCA), widely recognised as a potential threat to CCSs. Specifically, malicious VMs can capture information from (target) VMs, i.e., those with sensitive information, by merely co-located with them on the same physical machine. As such, a VM allocation algorithm needs to be cognizant of this issue and attempts to allocate the malicious and target VMs onto different machines, i.e., the allocation algorithm needs to be security-aware. This paper investigates the allocation patterns of VM allocation algorithms that are more likely to lead to a secure allocation. A driving objective is to reduce the number of VM migrations during allocation. We also propose a graph-based secure VMs allocation algorithm (GbSRS) to minimise SCA threats. Our results show that algorithms following a stacking-based behaviour are more likely to produce secure VMs allocation than those following spreading or random behaviours.

Database audit records are important for investigating suspicious actions against transactional databases. Their admissibility as digital evidence depends on satisfying Chain of Custody (CoC) properties during their generation, collection and preservation in order to prevent their modification, guarantee action accountability, and allow third-party verification. However, their production has relied on auditing capabilities provided by commercial database systems which may not be effective if malicious users (or insiders) misuse their privileges to disable audit controls, and compromise their admissibility. Hence, in this paper, we propose a forensically-aware distributed database architecture that implements CoC properties as functional requirements to produce admissible audit records. The novelty of our proposal is the use of hybrid logical clocks, which compared with a previous centralised vector-clock architecture, has evident advantages as it (i) allows for more accurate provenance and causality tracking of insider actions, (ii) is more scalable in terms of system size, and (iii) although latency is higher (as expected in distributed environments), 70 per cent of user transactions are executed within acceptable latency intervals.

Source location privacy (SLP) is becoming an important property for a large class of security-critical wireless sensor network applications such as monitoring and tracking. Much of the previous work on SLP have focused on the development of various protocols to enhance the level of SLP imparted to the network, under various attacker models and other conditions. Others works have focused on analysing the level of SLP being imparted by a specific protocol. In this paper, we focus on deconstructing routing-based SLP protocols to enable a better understanding of their structure. We argue that the SLP-aware routing protocols can be classified into two main categories, namely (i) spatial and (ii) temporal. Based on this, we show that there are three important components, namely (i) decoy selection, (ii) use and routing of control messages and (iii) use and routing of decoy messages. The decoy selection technique imparts the spatial or temporal property of SLP-aware routing. We show the viability of the framework through the construction of well-known SLP-aware routing protocols using the identified components.