Abstract: This research investigates the Electromagnetic (EM) side channel leakage of a Raspberry Pi 2 B+. An evaluation is performed on the EM leakage as the device executes the AES-128 cryptographic algorithm contained in the libcrypto++ library in a threaded environment. Four multi-threaded implementations are evaluated. These implementations are Portable Operating System Interface Threads, C++11 threads, Threading Building Blocks, and OpenMP threads. It is demonstrated that the various thread techniques have distinct variations in frequency and shape as EM emanations are leaked from the Raspberry Pi. It is demonstrated that the AES-128 cryptographic implementation within the libcrypto++ library on a Raspberry Pi is vulnerable to Side Channel Analysis (SCA) attacks. The cryptographic process was seen visibly within the EM spectrum and the data for this process was extracted where digital filtering techniques was applied to the signal. The resultant data was utilised in the Differential Electromagnetic Analysis (DEMA) attack and the results revealed 16 sub-keys that are required to recover the full AES-128 secret key. Based on this discovery, this research introduced a multi-threading approach with the utilisation of Secure Hash Algorithm (SHA) to serve as a software based countermeasure to mitigate SCA attacks. The proposed countermeasure known as the FRIES noise generator executed as a Daemon and generated EM noise that was able to hide the cryptographic implementations and prevent the DEMA attack and other statistical analysis.

Abstract: Information security is a fast-growing discipline, and relies on continued improvement of security measures to protect sensitive information. Human operators are one of the weakest links in the security chain as they are highly susceptible to manipulation. A social engineering attack targets this weakness by using various manipulation techniques to elicit individuals to perform sensitive requests. The field of social engineering is still in its infancy with respect to formal definitions, attack frameworks, and examples of attacks and detection models. In order to formally address social engineering in a broad context, this paper proposes the underlying abstract finite state machine of the Social Engineering Attack Detection Model (SEADM). The model has been shown to successfully thwart social engineering attacks utilising either bidirectional communication, unidirectional communication or indirect communication. Proposing and exploring the underlying finite state machine of the model allows one to have a clearer overview of the mental processing performed within the model. While the current model provides a general procedural template for implementing detection mechanisms for social engineering attacks, the finite state machine provides a more abstract and extensible model that highlights the inter-connections between task categories associated with different scenarios. The finite state machine is intended to help facilitate the incorporation of organisation specific extensions by grouping similar activities into distinct categories, subdivided into one or more states. The finite state machine is then verified by applying it to representative social engineering attack scenarios from all three streams of possible communication. This verifies that all the capabilities of the SEADM are kept in tact, whilst being improved, by the proposed finite state machine.

Abstract: Nudging has been adopted by many disciplines in the last decade in order to achieve behavioural change. Information security is no exception. A number of attempts have been made to nudge end-users towards stronger passwords. Here we report on our deployment of an enriched nudge displayed to participants on the system enrolment page, when a password has to be chosen. The enriched nudge was successful in that participants chose significantly longer and stronger passwords. One thing that struck us as we designed and tested this nudge was that we were unable to find any nudge-specific ethical guidelines to inform our experimentation in this context. This led us to reflect on the ethical implications of nudge testing, specifically in the password authentication context. We mined the nudge literature and derived a number of core principles of ethical nudging. We tailored these to the password authentication context, and then show how they can be applied by assessing the ethics of our own nudge. We conclude with a set of preliminary guidelines derived from our study to inform other researchers planning to deploy nudge-related techniques in this context.

Abstract: NoSQL databases have gained a lot of popularity over the last few years. They are now used in many new system implementations that work with vast amounts of data. Such data will typically also include sensitive information that needs to be secured. NoSQL databases are also underlying a number of cloud implementations which are increasingly being used to store sensitive information by various organisations. This has made NoSQL databases a new target for hackers and other state sponsored actors. Forensic examinations of compromised systems will need to be conducted to determine what exactly transpired and who was responsible. This paper examines specifically if NoSQL databases have security features that leave relevant traces so that accurate forensic attribution can be conducted. The seeming lack of default security measures such as access control and logging has prompted this examination. A survey into the top ranked NoSQL databases was conducted to establish what authentication and authorisation features are available. Additionally the provided logging mechanisms were also examined since access control without any auditing would not aid forensic attribution tremendously. Some of the surveyed NoSQL databases do not provide adequate access control mechanisms and logging features that leave relevant traces to allow forensic attribution to be done using those. The other surveyed NoSQL databases did provide adequate mechanisms and logging traces for forensic attribution, but they are not enabled or configured by default. This means that in many cases they might not be available, leading to insufficient information to perform accurate forensic attribution even on those databases.