Biblio

The use of Virtual Machine (VM) migration as support for software rejuvenation was introduced more than a decade ago. Since then, several works have validated this approach from experimental and theoretical perspectives. Recently, some works shed light on the possibility of using the same technique as Moving Target Defense (MTD). However, to date, no work evaluated the availability and security levels while applying VM migration for both rejuvenation and MTD (multipurpose VM migration). In this paper, we conduct a comprehensive evaluation using Stochastic Petri Net (SPN) models to tackle this challenge. The evaluation covers the steady-state system availability, expected MTD protection, and related metrics of a system under time-based multipurpose VM migration. Results show that the availability and security improvement due to VM migration deployment surpasses 50% in the best scenarios. However, there is a trade-off between availability and security metrics, meaning that improving one implies compromising the other.

The test of security primitives is particularly strategic as any bias coming from the implementation or environment can wreck havoc on the security it is intended to provide. This paper presents how some security properties are tested on leading primitives: True Random Number Generation (TRNG), Physically Unclonable Function (PUF), cryptographic primitives and Digital Sensor (DS). The test of TRNG and PUF to ensure a high level of security is mainly about the entropy assessment, which requires specific statistical tests. The security against side-channel analysis (SCA) of cryptographic primitives, like the substitution box in symmetric cryptography, is generally ensured by masking. But the hardware implementation of masking can be damaged by glitches, which create leakages on sensitive variables. A test method is to search for nets of the cryptographic netlist, which are vulnerable to glitches. The DS is an efficient primitive to detect disturbances and rise alarms in case of fault injection attack (FIA). The dimensioning of this primitive requires a precise test to take into account the environment variations including the aging.

Humans are a key part of software development, including customers, designers, coders, testers and end users. In this keynote talk I explain why incorporating human-centric issues into software engineering for next-generation applications is critical. I use several examples from our recent and current work on handling human-centric issues when engineering various `smart living' cloud- and edge-based software systems. This includes using human-centric, domain-specific visual models for non-technical experts to specify and generate data analysis applications; personality impact on aspects of software activities; incorporating end user emotions into software requirements engineering for smart homes; incorporating human usage patterns into emerging edge computing applications; visualising smart city-related data; reporting diverse software usability defects; and human-centric security and privacy requirements for smart living systems. I assess the usefulness of these approaches, highlight some outstanding research challenges, and briefly discuss our current work on new human-centric approaches to software engineering for smart living applications.

The progress made in terms of controller technologies with the introduction of remotely-accessibility capacity in the digital controllers has opened the door to new cybersecurity threats on the Industrial Control Systems (ICSs). Among them, some aim at damaging the ICS's physical system. In this paper, a corrupted controller emitting a non-legitimate Pulse Width Modulation control signal to an Electro-Mechanical Actuator (EMA) is considered. The attacker's capabilities for accelerating the EMA's aging by inducing Partial Discharges (PDs) are investigated. A simplified model is considered for highlighting the influence of the carrier frequency of the control signal over the amplitude and the repetition of the PDs involved in the EMA's aging.

The "aging" phenomenon occurs after the long-term running of software, with the fault rate rising and running efficiency dropping. As there is no corresponding testing type for this phenomenon among conventional software tests, "software runtime accumulative testing" is proposed. Through analyzing several examples of software aging causing serious accidents, software is placed in the system environment required for running and the occurrence mechanism of software aging is analyzed. In addition, corresponding testing contents and recommended testing methods are designed with regard to all factors causing software aging, and the testing process and key points of testing requirement analysis for carrying out runtime accumulative testing are summarized, thereby providing a method and guidance for carrying out "software runtime accumulative testing" in software engineering.

Reliability and robustness of Internet of Things (IoT)-cloud-based communication is an important issue for prospective development of the IoT concept. In this regard, a robust and unique client-to-cloud communication physical layer is required. Physical Unclonable Function (PUF) is regarded as a suitable physics-based random identification hardware, but suffers from reliability problems. In this paper, we propose novel hardware concepts and furthermore an analysis method in CMOS technology to improve the hardware-based robustness of the generated PUF word from its first point of generation to the last cloud-interfacing point in a client. Moreover, we present a spectral analysis for an inexpensive high-yield implementation in a 65nm generation. We also offer robust monitoring concepts for the PUF-interfacing communication physical layer hardware.

Given the complexities involved in the sensing, navigational and positioning environment on board automated vehicles we conduct an exploratory survey and identify factors capable of influencing the users' trust in such system. After the analysis of the survey data, the Situational Awareness of the Vehicle (SAV) emerges as an important factor capable of influencing the trust of the users. We follow up on that by conducting semi-structured interviews with 12 experts in the CAV field, focusing on the importance of the SAV, on the factors that are most important when talking about it as well as the need to keep the users informed regarding its status. We conclude that in the context of Connected and Automated Vehicles (CAVs), the importance of the SAV can now be expanded beyond its technical necessity of making vehicles function to a human factors area: calibrating users' trust.

With Software Defined Networking (SDN) the control plane logic of forwarding devices, switches and routers, is extracted and moved to an entity called SDN controller, which acts as a broker between the network applications and physical network infrastructure. Failures of the SDN controller inhibit the network ability to respond to new application requests and react to events coming from the physical network. Despite of the huge impact that a controller has on the network performance as a whole, a comprehensive study on its failure dynamics is still missing in the state of the art literature. The goal of this paper is to analyse, model and evaluate the impact that different controller failure modes have on its availability. A model in the formalism of Stochastic Activity Networks (SAN) is proposed and applied to a case study of a hypothetical controller based on commercial controller implementations. In case study we show how the proposed model can be used to estimate the controller steady state availability, quantify the impact of different failure modes on controller outages, as well as the effects of software ageing, and impact of software reliability growth on the transient behaviour.

Modern smart surveillance systems can not only record the monitored environment but also identify the targeted objects and detect anomaly activities. These advanced functions are often facilitated by deep neural networks, achieving very high accuracy and large data processing throughput. However, inappropriate design of the neural network may expose such smart systems to the risks of leaking the target being searched or even the adopted learning model itself to attackers. In this talk, we will present the security challenges in the design of smart surveillance systems. We will also discuss some possible solutions that leverage the unique properties of emerging nano-devices, including the incurred design and performance cost and optimization methods for minimizing these overheads.

Detecting early trends indicating cognitive decline can allow older adults to better manage their health, but current assessments present barriers precluding the use of such continuous monitoring by consumers. To explore the effects of cognitive status on computer interaction patterns, the authors collected typed text samples from older adults with and without pre-mild cognitive impairment (PreMCI) and constructed statistical models from keystroke and linguistic features for differentiating between the two groups. Using both feature sets, they obtained a 77.1 percent correct classification rate with 70.6 percent sensitivity, 83.3 percent specificity, and a 0.808 area under curve (AUC). These results are in line with current assessments for MC–a more advanced disease–but using an unobtrusive method. This research contributes a combination of features for text and keystroke analysis and enhances understanding of how clinicians or older adults themselves might monitor for PreMCI through patterns in typed text. It has implications for embedded systems that can enable healthcare providers and consumers to proactively and continuously monitor changes in cognitive function.

The delay-tolerant-network (DTN) model is becoming a viable communication alternative to the traditional infrastructural model for modern mobile consumer electronics equipped with short-range communication technologies such as Bluetooth, NFC, and Wi-Fi Direct. Proximity malware is a class of malware that exploits the opportunistic contacts and distributed nature of DTNs for propagation. Behavioral characterization of malware is an effective alternative to pattern matching in detecting malware, especially when dealing with polymorphic or obfuscated malware. In this paper, we first propose a general behavioral characterization of proximity malware which based on naive Bayesian model, which has been successfully applied in non-DTN settings such as filtering email spams and detecting botnets. We identify two unique challenges for extending Bayesian malware detection to DTNs ("insufficient evidence versus evidence collection risk" and "filtering false evidence sequentially and distributedly"), and propose a simple yet effective method, look ahead, to address the challenges. Furthermore, we propose two extensions to look ahead, dogmatic filtering, and adaptive look ahead, to address the challenge of "malicious nodes sharing false evidence." Real mobile network traces are used to verify the effectiveness of the proposed methods.