With built-in artificial intelligence (AI), edge devices, e.g., smart cameras, can perform tasks like detecting and tracking individuals, which is referred to as edge biometrics. As a driving force for AI, machine/deep learning plays a critical role in edge biometrics. Machine/deep learning based edge biometric systems outperform their nonmachine learning counterpart. However, research shows that artificial neural networks, e.g., convolutional neural networks, are invertible such that adversaries can obtain a certain amount of information about the original inputs/templates. This information leakage is not tolerable for biometric systems because biometric data in the original (raw) templates cannot be reset or replaced. Once compromised, they are lost forever. Therefore, how to prevent original biometric templates from being attacked through inverting deep neural networks is a pressing, but unsolved issue, for deep learning based biometric recognition. To address the issue, in this paper, we develop a novel biometric template protection algorithm using the binary decision diagram (BDD) for deep learning based finger-vein biometric systems. The proposed algorithm is capable of creating a new noninvertible version of the original finger-vein template, which is stacked with an artificial neural network-the multilayer extreme learning machine (ML-ELM) to generate a privacy-preserving finger-vein recognition system, named BDD-ML-ELM. The proposed BDD-ML-ELM ensures the safety of the original finger-vein template even if its transformed version is compromised. The transformed template, if compromised, can be revoked and replaced with another new version by simply changing the user-specific keys. Therefore, the BDD-ML-ELM has a clear advantage over the existing machine/deep learning based biometric systems, whose raw biometric templates are vulnerable when the artificial neural network suffers an inversion attack.

Any proposal to provide security for implantable medical devices (IMDs), such as cardiac pacemakers and defibrillators, has to achieve a trade-off between security and accessibility for doctors to gain access to an IMD, especially in an emergency scenario. In this paper, we propose a finger-to-heart (F2H) IMD authentication scheme to address this trade-off between security and accessibility. This scheme utilizes a patient's fingerprint to perform authentication for gaining access to the IMD. Doctors can gain access to the IMD and perform emergency treatment by scanning the patient's finger tip instead of asking the patient for passwords/security tokens, thereby, achieving the necessary trade-off. In the scheme, an improved minutia-cylinder-code-based fingerprint authentication algorithm is proposed for the IMD by reducing the length of each feature vector and the number of query feature vectors. Experimental results show that the improved fingerprint authentication algorithm significantly reduces both the size of messages in transmission and computational overheads in the device, and thus, can be utilized to secure the IMD. Compared to existing electrocardiogram signal-based security schemes, the F2H scheme does not require the IMD to capture or process biometric traits in every access attempt since a fingerprint template is generated and stored in the IMD beforehand. As a result, the scarce resources in the IMD are conserved, making the scheme sustainable as well as energy efficient.

The original version of this article was revised due to a retrospective Open Access order.

Abstract Evaluating cyber security risk is a challenging task regardless of an organisation’s nature of business or size, however, an essential activity. This paper uses the National Institute of Standards and Technology (NIST) cyber security framework (CSF) to assess the cyber security posture of a local government organisation in Western Australia. Our approach enabled the quantification of risks for specific NIST CSF core functions and respective categories and allowed making recommendations to address the gaps discovered to attain the desired level of compliance. This has led the organisation to strategically target areas related to their people, processes, and technologies, thus mitigating current and future threats.

Building automation systems, or building management systems, control services such as heating, airconditioning and security access in facilities. A common protocol used to transmit data regarding the status of components is BACnet. Unfortunately, whilst security is included in the BACnet standard, it is rarely implemented by vendors of building automation systems. This lack of attention to security can lead to vulnerabilities in the protocol being exploited with the result that the systems and the buildings they control can be compromised. This paper describes a proof-of-concept protocol attack on a BACnet system and examines the potential of modeling the basis of the attack.

The ubiquitous nature of Bluetooth technology presents opportunities for intelligence gathering based on historical and real-time device presence data. This information can be of value to law enforcement agencies, intelligence organizations, and industry. Despite the introduction of the Bluetooth Low Energy standard that incorporates anonymity preservation mechanisms, the presence of devices that support Classic Bluetooth that uses unique and persistent device identifiers is expected to remain significant for a number of years. The common approach to finding discoverable Classic Bluetooth devices relies on a standard inquiry process that is not truly passive. Furthermore, this approach fails to detect devices that remain undiscoverable. Ubertooth One, a low-cost open source Bluetooth development platform, can assist with overcoming this limitation in a truly passive manner, making it an attractive digital forensic instrument. Using vehicle-based sensors and parallel multi-method device discovery, we conduct a practical evaluation of Ubertooth One for passive discovery and contrast its discovery rate to the standard method. Based on 83 comparative field experiments, we show that Ubertooth One can produce forensically sound observations while able to discover up to ten times as many devices. We also show that this method can identify repeat device presence, as we observe 2370 instances of repeat observations on different days in single and multiple location scenarios. We conclude that this passive technique can complement the standard method and has the potential be used as a viable alternative.

The 802.11 active service discovery mechanism requires the transmission of various attributes in a plain text. These attributes can be collected using passive monitoring and can be used to enumerate the preferred network list (PNL) of client devices. In this paper, we focus on the information that can be obtained using the service set identifiers (SSIDs) that make up the PNL. First, we describe a simple model based on a wireless access point geolocation technique to gauge the potential device locatability using data available on WiGLE.net. Second, we look at additional information that can be extracted from the SSID strings. Our hypothesis is that the entities of potential interest, such as locations and personal names contained within SSIDs, can be recognized in an automated fashion. Using two freely available pretrained named entity recognizers, we were able to identify up to 49% of SSIDs as possibly carrying entities of interest based on multiple data sets. We also show that extracted attributes can be used as an inference basis for additional inference attacks, which presents further opportunities in forensic and intelligence contexts.

The paper examines the advice and support provided by seven major Internet Service Providers in Australia through late 2009 and early 2010 in relation to computer and network security. Previous research has indicated that many end-users will attempt to utilise the support provided by Internet Service Providers as a simple and effective method by which to obtain key information in regards to computer security. This paper demonstrates that in many cases the individuals working at the help desk are either reluctant to provide IT security support or have insufficient skill to provide the correct information.

Honeypot technologies are proving successful in mitigating against external attackers and there is significant literature for their deployment and development as external facing countermeasures. Very little research has been done on their suitability or adaptability as an internally deployed countermeasure. This paper explores issues with the deployment and design of honeypot technologies as an internal countermeasure to insider malfeasance.