CAM04-1: Admission control in self aware networks

The worldwide growth in broadband access and multimedia traffic has led to an increasing need for Quality- of-Service (QoS) in networks. Real time network applications require a stable, reliable, and predictable network that will guarantee packet delivery under QoS constraints. Network self- awareness through on-line measurement and adaptivity in response to user needs is one way to advance user QoS when overall network conditions can change, while admission control (AC) is an approach that has been commonly used to reduce traffic congestion and to satisfy users' QoS requests. The purpose of this paper is to describe a novel measurement-based admission control algorithm which bases its decision on different QoS metrics that users can specify. The self-observation and self- awareness capabilities of the network are exploited to collect data that allows an AC algorithm to decide whether to admit users based on their QoS needs, and the QoS impact they will have on other users. The approach we propose finds whether feasible paths exist for the projected incoming traffic, and estimates the impact that the newly accepted traffic will have on the QoS of pre-existing connections. The AC decision is then taken based on the outcome of this analysis

Strengthening the security of cognitive packet networks

Route selection in cognitive packet networks (CPNs) occurs continuously for active flows and is driven by the users' choice of a quality of service (QoS) goal. Because routing occurs concurrently to packet forwarding, CPN flows are able to better deal with unexpected variations in network status, while still achieving the desired QoS. Random neural networks (RNNs) play a key role in CPN routing and are responsible to the next-hop decision making of CPN packets. By using reinforcement learning, RNNs' weights are continuously updated based on expected QoS goals and information that is collected by packets as they travel on the network experiencing the current network conditions. CPN's QoS performance had been extensively investigated for a variety of operating conditions. Its dynamic and self-adaptive properties make them suitable for withstanding availability attacks, such as those caused by worm propagation and denial-of-service attacks. However, security weaknesses related to confidentiality and integrity attacks have not been previously examined. Here, we look at related network security threats and propose mechanisms that could enhance the resilience of CPN to confidentiality, integrity and availability attacks

Factors Influencing the Surface Functionalization of Citrate Stabilized Gold Nanoparticles with Cysteamine, 3-Mercaptopropionic Acid or l -Selenocystine for Sensor Applications

Thiols and selenides bind to the surface of gold nanoparticles (AuNPs) and thus provide suitable platforms for the fabrication of sensors. However, the co-existence of adsorbed citrate on the surface of the nanoparticles can influence their functionalization behavior and potentially their sensing performance measured by the extent of particle aggregation. In this study, the functionalization of purchased (7.3 ± 1.2 nm) and in-house prepared AuNPs (13.8 ± 1.2 nm), under the same experimental conditions with either cysteamine (Cys), 3-mercaptopropionic acid (3-MPA), or l-selenocystine (SeCyst) was investigated. 1H-NMR measurements showed distinct citrate signatures on the in-house synthesized citrate-stabilized AuNPs, while no citrate signals were detected on the purchased AuNPs other than evidence of the presence of α-ketoglutaric acid. Carboxylate-containing species attributed to either citrate or α-ketoglutaric acid were identified in all functionalized AuNPs. ATR-FTIR spectroscopy confirmed the functionalization of AuNPs with Cys and 3-MPA, and energy dispersive X-ray (EDX) spectroscopy measurements suggested the formation of SeCyst functionalized AuNPs. Co-adsorption rather than displacement by the functionalizing agents and carboxylate-containing molecules was indicated, which for Cys and SeCyst functionalized AuNPs was also the aggregation limiting factor. In contrast, the behavior of 3-MPA functionalized AuNPs could be attributed to electrostatic repulsions between the functionalized groups

Solid lipid nanoparticles and nanostructured lipid carriers of dual functionality at emulsion interfaces. Part II:active carrying/delivery functionality

The utilisation of lipid nanostructures that can in tandem act as Pickering emulsion stabilisers and as active carrier/delivery systems, could potentially enable the development of liquid (emulsion-based) formulations with the capacity for multi-active encapsulation and delivery. Part I of this work focused on the first aspect of this two-fold functionality by investigating the capacity of both solid lipid nanoparticles (SLNs) and nanostructured lipid carriers (NLCs) to act as effective Pickering particles in o/w emulsions. Herein, attention shifts to the secondary functionality, with part II of this study assessing both SLNs and NLCs in terms of their capacity to act as carriers and release regulators for curcumin, a model hydrophobic active. The previously established Pickering functionality and physical properties in terms of particle size, zeta potential and interfacial tension of the lipid particles remained unaffected after encapsulation of curcumin. In emulsions, loss of crystalline (solid lipid) matter and particle interfacial presence were specifically investigated, as these aspects can impact upon the particles’ active carrying and delivery performance. Low solid matter losses were recorded for all emulsions (ranging between 0% and 15%), with increasing liquid lipid fraction in the particles (SLNs to NLCs) resulting in relatively higher depletion of crystallinity. Removal of unadsorbed surfactant (remnant from the particle formation processing step) prior to emulsification led to higher particle interfacial occupancy. Despite said changes, the lipid particles’ curcumin carrying capacity, expressed as encapsulation efficiency and loading capacity, did not differ between an emulsion and dispersion setting. Although the active carrying capacity was retained, it was shown that the presence of the particles at the emulsion interfaces affects the curcumin release rate. Partial migration of curcumin to the oil droplet and creation of an additional release-inducing potential to the particles in close proximity to the droplet interface are proposed to be responsible for the overall faster active expulsion. What is more, the curcumin release profile from either SLNs or NLCs (also) stabilising an emulsion microstructure, was shown to persist after storage; either storage of the particles (up to 4 months) prior to emulsification, or storage of emulsions (up to 3 months) stabilised by ‘freshly’ formed lipid particles. Overall, the present study provides evidence that the two-fold functionality of the lipid particles can be indeed realised, markedly demonstrating that their concurrency does not compromise one another

Cloud-based cyber-physical intrusion detection for vehicles using Deep Learning

Detection of cyber attacks against vehicles is of growing interest. As vehicles typically afford limited processing resources, proposed solutions are rule-based or lightweight machine learning techniques. We argue that this limitation can be lifted with computational offloading commonly used for resource-constrained mobile devices. The increased processing resources available in this manner allow access to more advanced techniques. Using as case study a small four-wheel robotic land vehicle, we demonstrate the practicality and benefits of offloading the continuous task of intrusion detection that is based on deep learning. This approach achieves high accuracy much more consistently than with standard machine learning techniques and is not limited to a single type of attack or the in-vehicle CAN bus as previous work. As input, it uses data captured in real-time that relate to both cyber and physical processes, which it feeds as time series data to a neural network architecture. We use both a deep multilayer perceptron and a recurrent neural network architecture, with the latter benefitting from a long-short term memory hidden layer, which proves very useful for learning the temporal context of different attacks. We employ denial of service, command injection and malware as examples of cyber attacks that are meaningful for a robotic vehicle. The practicality of the latter depends on the resources afforded onboard and remotely, as well as the reliability of the communication means between them. Using detection latency as the criterion, we have developed a mathematical model to determine when computation offloading is beneficial given parameters related to the operation of the network and the processing demands of the deep learning model. The more reliable the network and the greater the processing demands, the greater the reduction in detection latency achieved through offloading

A survey on cyber risk management for the Internet of Things

The Internet of Things (IoT) continues to grow at a rapid pace, becoming integrated into the daily operations of individuals and organisations. IoT systems automate crucial services within daily life that users may rely on, which makes the assurance of security towards entities such as devices and information even more significant. In this paper, we present a comprehensive survey of papers that model cyber risk management processes within the context of IoT, and provide recommendations for further work. Using 39 collected papers, we studied IoT cyber risk management frameworks against four research questions that delve into cyber risk management concepts and human-orientated vulnerabilities. The importance of this work being human-driven is to better understand how individuals can affect risk and the ways that humans can be impacted by attacks within different IoT domains. Through the analysis, we identified open areas for future research and ideas that researchers should consider

Solid lipid nanoparticles and nanostructured lipid carriers of dual functionality at emulsion interfaces. Part I : Pickering stabilisation functionality

Solid lipid nanoparticles and nanostructured lipid carriers are two types of lipid nanoparticulate systems, that have been primarily studied for their capability to function as active carriers, and only more recently utilised in Pickering emulsion stabilisation. Unveiling the factors that impact upon the lipid particle characteristics related to their Pickering functionality could enable the development of a liquid formulation with tailored microstructure and potentially the capacity to display a two-fold performance. In part I, this work investigates how certain formulation characteristics, namely solid-to-liquid lipid mass ratio and presence of unadsorbed surfactant in the aqueous carrier phase, affect the structural properties of the lipid particles, and in turn how these influence their Pickering stabilisation capacity. The effect of the formulation parameters was assessed in terms of the wettability and physicochemical properties of the lipid particles, including particle size, crystallinity and interfacial behaviour. Lipid particles fabricated with higher liquid lipid content (70% w/w) were shown to be more hydrophilic and have lower surfactant decoration at their surface compared to particles containing lower or no liquid lipid in their crystalline matrix. The emulsion stabilisation ability through a Pickering mechanism was confirmed for all types of lipid particles using polarised microscopy. Increasing liquid lipid content and removal of excess surfactant did not compromise the particle stabilisation capacity, though emulsion droplets of larger sizes were initially acquired in the latter case. The particle-stabilised emulsions maintained their physical integrity, with particles retaining close association with the emulsion interface over a storage period of 12 weeks

AndroDex: Android Dex images of obfuscated malware

With the emergence of technology and the usage of a large number of smart devices, cyber threats are increasing. Therefore, research studies have shifted their attention to detecting Android malware in recent years. As a result, a reliable and large-scale malware dataset is essential to build effective malware classifiers. In this paper, we have created AndroDex: an Android malware dataset containing a total of 24,746 samples that belong to more than 180 malware families. These samples are based on .dex images that truly reflect the characteristics of malware. To construct this dataset, we first downloaded the APKs of the malware, applied obfuscation techniques, and then converted them into images. We believe that this dataset will significantly enhance a series of research studies, including Android malware detection and classification, and it will also boost deep learning classification efforts, among others. The main objective of creating images based on the Android dataset is to help other malware researchers better understand how malware works. Additionally, an important result of this study is that most malware nowadays employs obfuscation techniques to hide their malicious activities. However, malware images can overcome such issues. The main limitation of this dataset is that it contains images based on .dex files that is based on static analysis. However, dynamic analysis takes time, therefore, to overcome the issue of time and space this dataset can be used for the initial examination of any .apk files

Dynamic decision support for resource offloading in heterogeneous internet of things environments

Computation offloading is one of the primary technological enablers of the Internet of Things (IoT), as it helps address individual devices' resource restrictions. In the past, offloading would always utilise remote cloud infrastructures, but the increasing size of IoT data traffic and the real-time response requirements of modern and future IoT applications have led to the adoption of the edge computing paradigm, where the data is processed at the edge of the network. The decision as to whether cloud or edge resources will be utilised is typically taken at the design stage based on the type of the IoT device. Yet, the conditions that determine the optimality of this decision, such as the arrival rate, nature and sizes of the tasks, and crucially the real-time condition of the networks involved, keep changing. At the same time, the energy consumption of IoT devices is usually a key requirement, which is affected primarily by the time it takes to complete tasks, whether for the actual computation or for offloading them through the network. Here, we model the expected time and energy costs for the different options of offloading a task to the edge or the cloud, as well as of carrying out on the device itself. We use this model to allow the device to take the offloading decision dynamically as a new task arrives and based on the available information on the network connections and the states of the edge and the cloud. Having extended EdgeCloudSim to provide support for such dynamic decision making, we are able to compare this approach against IoT-first, edge-first, cloud-only, random and application-oriented probabilistic strategies. Our simulations on four different types of IoT applications show that allowing customisation and dynamic offloading decision support can improve drastically the response time of time-critical and small-size applications, and the energy consumption not only of the individual IoT devices but also of the system as a whole. This paves the way for future IoT devices that optimise their application response times, as well as their own energy autonomy and overall energy efficiency, in a decentralised and autonomous manner

Computation offloading of a vehicle’s continuous intrusion detection workload for energy efficiency and performance

Computation offloading has been used and studied extensively in relation to mobile devices. That is because their relatively limited processing power and reliance on a battery render the concept of offloading any processing/energy-hungry tasks to a remote server, cloudlet or cloud infrastructure particularly attractive. However, the mobile device’s tasks that are typically offloaded are not time-critical and tend to be one-off. We argue that the concept can be practical also for continuous tasks run on more powerful cyber-physical systems where timeliness is a priority. As case study, we use the process of real-time intrusion detection on a robotic vehicle. Typically, such detection would employ lightweight statistical learning techniques that can run onboard the vehicle without severely affecting its energy consumption. We show that by offloading this task to a remote server, we can utilse approaches of much greater complexity and detection strength based on deep learning. We show both mathematically and experimentally that this allows not only greater detection accuracy, but also significant energy savings, which improve the operational autonomy of the vehicle. In addition, the overall detection latency is reduced in most of our experiments. This can be very important for vehicles and other cyber-physical systems where cyber attacks can directly affect physical safety. In fact, in some cases, the reduction in detection latency thanks to offloading is not only beneficial but necessary. An example is when detection latency onboard the vehicle would be higher than the detection period, and as a result a detection run cannot complete before the next one is scheduled, increasingly delaying consecutive detection decisions. Offloading to a remote server is an effective and energy-efficient solution to this problem too