Research in the Internet of Things (IoT) conceives a world where everyday objects are connected to the Internet and exchange, store, process, and collect data from the surrounding environment. IoT devices are becoming essential for supporting the delivery of data to enable electronic services, but they are not sufficient in most cases to host application services directly due to their intrinsic resource constraints. Fog Computing (FC) can be a suitable paradigm to overcome these limitations, as it can coexist and cooperate with centralized Cloud systems and extends the latter toward the network edge. In this way, it is possible to distribute resources and services of computing, storage, and networking along the Cloud-to-Things continuum. As such, FC brings all the benefits of Cloud Computing (CC) closer to end (user) devices. This article presents a survey on the employment of FC to support IoT devices and services. The principles and literature characterizing FC are described, highlighting six IoT application domains that may benefit from the use of this paradigm. The extension of Cloud systems towards the network edge also creates new challenges and can have an impact on existing approaches employed in Cloud-based deployments. Research directions being adopted by the community are highlighted, with an indication of which of these are likely to have the greatest impact. An overview of existing FC software and hardware platforms for the IoT is also provided, along with the standardisation efforts in this area initiated by the OpenFog Consortium (OFC).

Fog Computing for the Internet of Things: A Survey

The Internet of Drones (IoD) is widely used in a wide range of applications from military to civilian applications from the past years. However, during communication either with the control room/ground station server(s) or moving access points in the sky, security and privacy is one the crucial issues which needs to be tackled efficiently. In this direction, blokchain technology can be one of the viable solutions due to the immutability and traceability of various transactions and decentralized nature. In this paper, we provide in-depth challenges and issues of applicability of blokchain in 5G-based Internet of Things (IoT)-enabled IoD environment. We propose and analyze a new blokchain based secure framework for data management among IoD communication entities. The proposed scheme has ability to resist several potential attacks that are essential in IoT-enabled IoD environment. A detailed comparative analysis exhibits that the proposed scheme offers better security and functionality requirements, and also provides less communication and computation overheads as compared to other related schemes.

Blockchain-Envisioned Secure Data Delivery and Collection Scheme for 5G-Based IoT-Enabled Internet of Drones Environment

Distributed Denial of Service (DDoS) attacks are one of the most harmful threats in today's Internet, disrupting the availability of essential services. The challenge of DDoS detection is the combination of attack approaches coupled with the volume of live traffic to be analysed. In this paper, we present a practical, lightweight deep learning DDoS detection system called LUCID, which exploits the properties of Convolutional Neural Networks (CNNs) to classify traffic flows as either malicious or benign. We make four main contributions; (1) an innovative application of a CNN to detect DDoS traffic with low processing overhead, (2) a dataset-agnostic preprocessing mechanism to produce traffic observations for online attack detection, (3) an activation analysis to explain LUCID's DDoS classification, and (4) an empirical validation of the solution on a resource-constrained hardware platform. Using the latest datasets, LUCID matches existing state-of-the-art detection accuracy whilst presenting a 40x reduction in processing time, as compared to the state-of-the-art. With our evaluation results, we prove that the proposed approach is suitable for effective DDoS detection in resource-constrained operational environments.

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LUCID: A Practical, Lightweight Deep Learning Solution for DDoS Attack Detection

A fundamental assumption in software security is that memory contents do not change unless there is a legitimate deliberate modification. Classical fault attacks show that this assumption does not hold if the attacker has physical access. Rowhammer attacks showed that local code execution is already sufficient to break this assumption. Rowhammer exploits parasitic effects in DRAM to modify the content of a memory cell without accessing it. Instead, other memory locations are accessed at a high frequency. All Rowhammer attacks so far were local attacks, running either in a scripted language or native code. In this paper, we present Nethammer. Nethammer is the first truly remote Rowhammer attack, without a single attacker-controlled line of code on the targeted system. Systems that use uncached memory or flush instructions while handling network requests, e.g., for interaction with the network device, can be attacked using Nethammer. Other systems can still be attacked if they are protected with quality-of-service techniques like Intel CAT. We demonstrate that the frequency of the cache misses is in all three cases high enough to induce bit flips. We evaluated different bit flip scenarios. Depending on the location, the bit flip compromises either the security and integrity of the system and the data of its users, or it can leave persistent damage on the system, i.e., persistent denial of service. We investigated Nethammer on personal computers, servers, and mobile phones. Nethammer is a security landslide, making the formerly local attack a remote attack.

Nethammer: Inducing Rowhammer Faults through Network Requests

Smart head-worn or head-mounted devices, including smart glasses and Virtual Reality (VR) headsets, are gaining popularity. Online shopping and in-app purchase from such headsets are presenting new e-commerce opportunities to the app developers. For convenience, users of these headsets may store account login, bank account and credit card details in order to perform quick in-app purchases. If the device is unattended, then an attacker, which can include insiders, can make use of the stored account and banking details to perform their own in-app purchases at the expense of the legitimate owner. To better protect the legitimate users of VR headsets (or head mounted displays in general) from such threats, in this paper, we propose to use eye movement to continuously authenticate the current wearer of the VR headset. We built a prototype device which allows us to apply visual stimuli to the wearer and to video the eye movements of the wearer at the same time. We use implicit visual stimuli (the contents of existing apps) which evoke eye movements from the headset wearer but without distracting them from their normal activities. This is so that we can continuously authenticate the wearer without them being aware of the authentication running in the background. We evaluated our proposed system experimentally with 30 subjects. Our results showed that the achievable authentication accuracy for implicit visual stimuli is comparable to that of using explicit visual stimuli. We also tested the time stability of our proposed method by collecting eye movement data on two different days that are two weeks apart. Our authentication method achieved an Equal Error Rate of 6.9% (resp. 9.7%) if data collected from the same day (resp. two weeks apart) were used for testing. In addition, we considered active impersonation attacks where attackers trying to imitate legitimate users' eye movements. We found that for a simple (resp. complex) eye tracking scene, a successful attack could be realised after on average 5.67 (13.50) attempts and our proposed authentication algorithm gave a false acceptance rate of 14.17% (3.61%). These results show that active impersonating attacks can be prevented using complex scenes and an appropriate limit on the number of authentication attempts. Lastly, we carried out a survey to study the user acceptability to our proposed implicit stimuli. We found that on a 5-point Likert scale, at least 60% of the respondents either agreed or strongly agreed that our proposed implicit stimuli were non-intrusive.

Continuous Authentication Using Eye Movement Response of Implicit Visual Stimuli

It is an accepted fact that, as the years go by, technology rapidly evolves. For this reason, it is absolutely necessary to constantly watch its evolution, to search for new ways for its progression, adjusting to the new conditions and demands. This effort can be mainly fulfilled by computers. Computers allow us to comprehend the development of technology, assisted by the growing development of the tool-programs that can be used. Another fundamental stepping stone for the technological evolution is the electrical machines which replace manual labor, since they are faster, more precise, and dependable. On a long term, using the electrical machines decreases the cost, whereas manual labor loses its prestige as the years go by. The present project extensively discusses the Raspberry Pi, one of the most sophisticated pocket computer models, which is able to conduct more complex implementations than the rest of the available models, using more program languages. More specifically, in the 1st chapter, we will make a small introduction about the Raspberry Pi, mentioning its definition and its history, referring to the date of the release of each model and some details about each one of them. We will, also, discuss the differentiations of each model and, this very chapter will make us understand what we can do with each one of the models.
In the 2nd chapter, we will refer to the raspberry hardware. First of all, the pins of the Raspberry Pi will be introduced and analyzed. The attachments needed for the activation of the Raspberry Pi will be mentioned and the right steps for the installation of the NOOBS operating system to the SD Card will be studied. Moreover, the RASPBIAN operating system will be installed to the Raspberry Pi 3. Finally, the first steps that we need to make once the Raspberry Pi turns on will be mentioned. In the 3rd chapter we will talk about the SSH. We will closely take a look at the steps needed in order to install the RASPBIAN operating system to the SD Card. We will see what the SSH is, as well.
In the 4th chapter, we will study the examples where the micro-controller is used, the attachments needed for each project, and we will analyze the circuits for each of the examples. We will be citing the circuit through the FRITZING and we will be discussing the code of each example so that the application can function. The examples will be of a gradual difficulty. The present dissertation aims to inform the students about an innovative micro-controller; the Raspberry Pi 3 Model B. The truth is that only few people are aware of this micro-controller. For this reason, our goal is to make the micro-controller more comprehensive to you.

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Papers

Οδηγός για το Raspberry Pi 3 Model B