Biblio

The IoT (Internet of Things) is one of the primary reasons for the massive growth in the number of connected devices to the Internet, thus leading to an increased volume of traffic in the core network. Fog and edge computing are becoming a solution to handle IoT traffic by moving timesensitive processing to the edge of the network, while using the conventional cloud for historical analysis and long-term storage. Providing processing, storage, and network communication at the edge network are the aim of fog computing to reduce delay, network traffic, and decentralise computing. In this paper, we define a framework that realises fog computing that can be extended to install any service of choice. Our framework utilises fog nodes as an extension of the traditional switch to include processing, networking, and storage. The fog nodes act as local decision-making elements that interface with software-defined networking (SDN), to be able to push updates throughout the network. To test our framework, we develop an IP spoofing security application and ensure its correctness through multiple experiments.

Cloud computing is a solution to reduce the cost of IT by providing elastic access to shared resources. It also provides solutions for on-demand computing power and storage for devices at the edge networks with limited resources. However, increasing the number of connected devices caused by IoT architecture leads to higher network traffic and delay for cloud computing. The centralised architecture of cloud computing also makes the edge networks more susceptible to challenges in the core network. Fog computing is a solution to decrease the network traffic, delay, and increase network resilience. In this paper, we study how fog computing may improve network resilience. We also conduct a simulation to study the effect of fog computing on network traffic and delay. We conclude that using fog computing prepares the network for better response time in case of interactive requests and makes the edge networks more resilient to challenges in the core network.

The Internet of Things (IoT) will connect not only computers and mobile devices, but it will also interconnect smart buildings, houses, and cities, as well as electrical grids, gas plants, and water networks, automobiles, airplanes, etc. IoT will lead to the development of a wide range of advanced information services that are pervasive, cost-effective, and can be accessed from anywhere and at any time. However, due to the exponential number of interconnected devices, cyber-security in the IoT is a major challenge. It heavily relies on the digital identity concept to build security mechanisms such as authentication and authorization. Current centralized identity management systems are built around third party identity providers, which raise privacy concerns and present a single point of failure. In addition, IoT unconventional characteristics such as scalability, heterogeneity and mobility require new identity management systems to operate in distributed and trustless environments, and uniquely identify a particular device based on its intrinsic digital properties and its relation to its human owner. In order to deal with these challenges, we present a Blockchain-based Identity Framework for IoT (BIFIT). We show how to apply our BIFIT to IoT smart homes to achieve identity self-management by end users. In the context of smart home, the framework autonomously extracts appliances signatures and creates blockchain-based identifies for their appliance owners. It also correlates appliances signatures (low level identities) and owners identifies in order to use them in authentication credentials and to make sure that any IoT entity is behaving normally.

In this paper we discuss the Internet of Things (IoT) by exploring aspects which go beyond the proliferation of devices and information enabled by: the growth of the Internet, increased miniaturization, prolonged battery life and an IT literate user base. We highlight the role of feedback mechanisms and illustrate this with reference to implemented computer enabled factory control systems. As the technology has developed, the cost of computing has reduced drastically, programming interfaces have improved, sensors are simpler and more cost effective and high performance communications across a wide area are readily available. We illustrate this by considering an application based on the Raspberry Pi, which is a low cost, small, programmable and network capable computer based on a powerful ARM processor with a programmable I/O interface, which can provide access to sensors (and other devices). The prototype application running on this platform can sense the presence of human being, using inexpensive passive infrared detectors. This can be used to monitor the activity of vulnerable adults, logging the results to a central server using a domestic Internet solution over a Wireless LAN. Whilst this demonstrates the potential for the use of such control/monitoring systems, practical systems spanning thousands of sites will be more complex to deliver and will have more stringent data processing and management demands and security requirements. We will discuss these concepts in the context of delivery of a smart interconnected society.

The Internet of Things (IoT) connects not only computers and mobile devices, but it also interconnects smart buildings, homes, and cities, as well as electrical grids, gas, and water networks, automobiles, airplanes, etc. However, IoT applications introduce grand security challenges due to the increase in the attack surface. Current security approaches do not handle cybersecurity from a holistic point of view; hence a systematic cybersecurity mechanism needs to be adopted when designing IoTbased applications. In this work, we present a risk management framework to deploy secure IoT-based applications for Smart Infrastructures at the design time and the runtime. At the design time, we propose a risk management method that is appropriate for smart infrastructures. At the design time, our framework relies on the Anomaly Behavior Analysis (ABA) methodology enabled by the Autonomic Computing paradigm and an intrusion detection system to detect any threat that can compromise IoT infrastructures by. Our preliminary experimental results show that our framework can be used to detect threats and protect IoT premises and services.

Given the advent of cyber-physical systems (CPS), event-based control paradigms such as complex event processing (CEP) are vital enablers for adaptive analytical control mechanisms. CPS are becoming a high-profile research topic as they are key to disruptive digital innovations such as autonomous driving, industrial internet, smart grid and ambient assisted living. However, organizational and technological scalability of today's CEP approaches is limited by their monolithic architectures. This leads to the research idea for atomic CEP entities and the hypothesis that a network of small event-based control services is better suited for CPS development and operation than current centralised approaches. In addition, the paper summarizes preliminary results of the presented doctoral work and outlines questions for future research as well as an evaluation plan.

Prior remote attestation may not be suitable to scale to the proliferation of IoT device swarms. To this end, we propose a cooperative remote attestation scheme, which validates the security state and verifies the system integrity with the cooperation of neighbor devices. The efficiency of the proposed mechanism is analyzed as a cryptographic protocol and then discussed in terms of security and scalability.

The Internet of Things (IoT) offers a more advanced service than a single device or an isolated system, as IoT connects diverse components, such as sensors, actuators, and embedded devices through the internet. As predicted by Cisco, there will be 50 billion IoT connected devices by 2020. Integration of such a tremendous number of devices into IoT potentially brings in a new concern, system security. In this work, we review two typical hardware attacks that can harm the emerging IoT applications. As IoT devices typically have limited computation power and need to be energy efficient, sophisticated cryptographic algorithms and authentication protocols are not suitable for every IoT device. To simultaneously thwart hardware Trojan and side-channel analysis attacks, we propose a low-cost dynamic permutation method for IoT devices. Experimental results show that the proposed method achieves 5.8X higher accumulated partial guessing entropy than the baseline, thus strengthening the IoT processing unit against hardware attacks.

In the area of the Internet of Things, cloud-based camera surveillance systems are ubiquitously available for industrial and private environments. However, the sensitive nature of the surveillance use case imposes high requirements on privacy/confidentiality, authenticity, and availability of such systems. In this work, we investigate how currently available mass-market camera systems comply with these requirements. Considering two attacker models, we test the cameras for weaknesses and analyze for their implications. We reverse-engineered the security implementation and discovered several vulnerabilities in every tested system. These weaknesses impair the users' privacy and, as a consequence, may also damage the camera system manufacturer's reputation. We demonstrate how an attacker can exploit these vulnerabilities to blackmail users and companies by denial-of-service attacks, injecting forged video streams, and by eavesdropping private video data - even without physical access to the device. Our analysis shows that current systems lack in practice the necessary care when implementing security for IoT devices.

A fresh look at the way secure communications is currently being done has been undertaken as a consequence of the large hacking's that have taken place recently. A plausible option maybe a return to the future via Morse code using how a quantum bit (Qubit) reacts when entangled to suggest a cypher. This quantum cyphers uses multiple properties of unique entities that have many random radicals which makes hacking more difficult that traditional 'Rivest-Shamir-Adleman' (RSA), 'Digital Signature Algorithm' (DSA) or 'Elliptic Curve Digital Signature Algorithm' (ECDSA). Additional security is likely by Backchannelling (slipstreaming) Quantum Morse code (Q-Morse) keys composed of living and non-living entities. This means Blockchain ledger history (forwards-backwards) is audited during an active session. Verification keys are Backchannelling (slipstreaming) during the session (e.g. train driver must incrementally activate a switch otherwise the train stops) using predicted-expected sender-receiver properties as well as their past history of disconformities to random radicals encountered. In summary, Quantum Morse code (Q-Morse) plausibly is the enabler to additional security by Backchannelling (slipstreaming) during a communications session.

In the Internet of Things (IoT), Internet-connected things provide an influx of data and resources that offer unlimited possibility for applications and services. Smart City IoT systems refer to the things that are distributed over wide physical areas covering a whole city. While the new breed of data and resources looks promising, building applications in such large scale IoT systems is a difficult task due to the distributed and dynamic natures of entities involved, such as sensing, actuating devices, people and computing resources. In this paper, we explore the process of developing Smart City IoT applications from a coordination-based perspective. We show that a distributed coordination model that oversees such a large group of distributed components is necessary in building Smart City IoT applications. In particular, we propose Adaptive Distributed Dataflow, a novel Dataflow-based programming model that focuses on coordinating city-scale distributed systems that are highly heterogeneous and dynamic.

This study stems from the premise that we need to break away from the "reactive" cycle of developing defenses against new DDoS attacks (e.g., amplification) by proactively investigating the potential for new types of DDoS attacks. Our specific focus is on pulsating attacks, a particularly debilitating type that has been hypothesized in the literature. In a pulsating attack, bots coordinate to generate intermittent pulses at target links to significantly reduce the throughput of TCP connections traversing the target. With pulsating attacks, attackers can cause significantly greater damage to legitimate users than traditional link flooding attacks. To date, however, pulsating attacks have been either deemed ineffective or easily defendable for two reasons: (1) they require a central coordinator and can thus be tracked; and (2) they require tight synchronization of pulses, which is difficult even in normal non-congestion scenarios. This paper argues that, in fact, the perceived drawbacks of pulsating attacks are in fact not fundamental. We develop a practical pulsating attack called CICADAS using two key ideas: using both (1) congestion as an implicit signal for decentralized implementation, and (2) a Kalman-filter-based approach to achieve tight synchronization. We validate CICADAS using simulations and wide-area experiments. We also discuss possible countermeasures against this attack.

Increase in M2M use cases, the availability of narrow band spectrum with operators and a need for very low cost modems for M2M applications has led to the discussions around what is called as Cellular IOT (CIOT). In order to develop the Cellular IOT network, discussions are focused around developing a new air interface that can leverage narrow band spectrum as well as lead to low cost modems which can be embedded into M2M/IOT devices. One key issue that arises during the development of a clean slate CIOT network is that of coexistence with the 4G networks. In this paper we explore architectures for Cellular IOT and 4G network harmonization that also addresses the one key requirement of possibly using narrow channels for IOT on the existing 4G networks and not just as a separate standalone Cellular IOT system. We analyze the architectural implication on the core network load in a tightly coupled CIOT-LTE architecture propose a offload mechanism from LTE to CIOT cells.

Existing compact routing schemes, e.g., Thorup and Zwick [SPAA 2001] and Chechik [PODC 2013], often have no means to tolerate failures, once the system has been setup and started. This paper presents, to our knowledge, the first self-healing compact routing scheme. Besides, our schemes are developed for low memory nodes, i.e., nodes need only O(log2 n) memory, and are thus, compact schemes. We introduce two algorithms of independent interest: The first is CompactFT, a novel compact version (using only O(log n) local memory) of the self-healing algorithm Forgiving Tree of Hayes et al. [PODC 2008]. The second algorithm (CompactFTZ) combines CompactFT with Thorup-Zwick's tree-based compact routing scheme [SPAA 2001] to produce a fully compact self-healing routing scheme. In the self-healing model, the adversary deletes nodes one at a time with the affected nodes self-healing locally by adding few edges. CompactFT recovers from each attack in only O(1) time and Δ messages, with only +3 degree increase and O(logΔ) graph diameter increase, over any sequence of deletions (Δ is the initial maximum degree). Additionally, CompactFTZ guarantees delivery of a packet sent from sender s as long as the receiver t has not been deleted, with only an additional O(y logΔ) latency, where y is the number of nodes that have been deleted on the path between s and t. If t has been deleted, s gets informed and the packet removed from the network.

In IoT (Internet of Things) networks, RPL (IPv6 Routing protocol for Low Power and Lossy Networks) is preferred for reducing routing overhead. In RPL, a node selects one parent node which includes the lowest routing metric among its neighbors and the other neighbors are stored as immediate successors. If the selected parent node is lost, the node selects a new parent node among the immediate successors. However, if the new path also includes the same intermediate node which is lost in previous path, it also fails to transmit upward packets. This procedure might be repeated until the new path is selected which does not include the lost immediate node. In this paper, we therefore propose a new path recovery method to reduce the unnecessary repetition for upward path recovery. When a node receives routing message, it calculates the hash value and sets 1 to a new field in the routing message. Based on the field, the node estimates an approximate number of ancestors that are shared between each paths. When loss of upward path is detected, the node selects a new path according to both approximate number and the routing metric. Therefore, a new path which dose not include same ancestors with the previous path is selected and data packet can be resumed immediately.

In this work, we constructively combine adaptive wormholes with channel-reciprocity based key establishment (CRKE), which has been proposed as a lightweight security solution for IoT devices and might be even more important for the 5G Tactile Internet and its embedded low-end devices. We present a new secret key generation protocol where two parties compute shared cryptographic keys under narrow-band multi-path fading models over a delayed digital channel. The proposed approach furthermore enables distance-bounding the key establishment process via the coherence time dependencies of the wireless channel. Our scheme is thoroughly evaluated both theoretically and practically. For the latter, we used a testbed based on the IEEE 802.15.4 standard and performed extensive experiments in a real-world manufacturing environment. Additionally, we demonstrate adaptive wormhole attacks (AWOAs) and their consequences on several physical-layer security schemes. Furthermore, we proposed a countermeasure that minimizes the risk of AWOAs.

Wireless sensor networks (WSNs) are implemented in various Internet-of-Things applications such as energy management systems. As the applications may involve personal information, they must be protected from attackers attempting to read information or control network devices. Research on WSN security is essential to protect WSNs from attacks. Studies in such research domains propose solutions against the attacks. However, they focus mainly on the security measures rather than on their ease in implementation in WSNs. In this paper, we propose a coordination middleware that provides an environment for constructing updatable WSNs for security. The middleware is based on LINC, a rule-based coordination middleware. The proposed approach allows the development of WSNs and attaches or detaches security modules when required. We implemented three security modules on LINC and on a real network, as case studies. Moreover, we evaluated the implementation costs while comparing the case studies.

Smart Transportation applications by nature are examples of Vehicular Ad-hoc Network (VANETs) applications where mobile vehicles, roadside units and transportation infrastructure interplay with one another to provide value added services. While there are abundant researches that focused on the communication aspect of such Mobile Ad-hoc Networks, there are few research bodies that target the development of VANET applications. Among the popular VANET applications, a dominant direction is to leverage Cloud infrastructure to execute and deliver applications and services. Recent studies showed that Cloud Computing is not sufficient for many VANET applications due to the mobility of vehicles and the latency sensitive requirements they impose. To this end, Fog Computing has been proposed to leverage computation infrastructure that is closer to the network edge to compliment Cloud Computing in providing latency-sensitive applications and services. However, applications development in Fog environment is much more challenging than in the Cloud due to the distributed nature of Fog systems. In this paper, we investigate how Smart Transportation applications are developed following Fog Computing approach, their challenges and possible mitigation from the state of the arts.

Drones have quickly become ubiquitous for both recreational and serious use. As is frequently the case with new technology in general, their rapid adoption already far exceeds our legal, policy, and social ability to cope with such issues as privacy and interference with well-established commercial and military air space. While the FAA has issued rulings, they will almost certainly be challenged in court as disputes arise, for example, when property owners shoot drones down. It is clear that drones will provide a critical role in smart cities and be connected to, if not directly a part of the IoT (Internet of Things). Drones will provide an essential role in providing network relay connectivity and situational awareness, particularly in disaster assessment and recovery scenarios. As is typical for new network technologies, the deployment of the drone hardware far exceeds our research in protocols – extending our previous understanding of MANETs (mobile ad hoc networks) and DTNs (disruption tolerant networks) – and more importantly, management, control, resilience, security, and privacy concerns. This keynote address will discuss these challenges and consider future research directions.

Contemporary vehicles are getting equipped with an increasing number of Electronic Control Units (ECUs) and wireless connectivities. Although these have enhanced vehicle safety and efficiency, they are accompanied with new vulnerabilities. In this paper, we unveil a new important vulnerability applicable to several in-vehicle networks including Control Area Network (CAN), the de facto standard in-vehicle network protocol. Specifically, we propose a new type of Denial-of-Service (DoS), called the bus-off attack, which exploits the error-handling scheme of in-vehicle networks to disconnect or shut down good/uncompromised ECUs. This is an important attack that must be thwarted, since the attack, once an ECU is compromised, is easy to be mounted on safety-critical ECUs while its prevention is very difficult. In addition to the discovery of this new vulnerability, we analyze its feasibility using actual in-vehicle network traffic, and demonstrate the attack on a CAN bus prototype as well as on two real vehicles. Based on our analysis and experimental results, we also propose and evaluate a mechanism to detect and prevent the bus-off attack.

The Internet of Things (IoT) is slowly, but steadily, changing the way we interact with our surrounding. Smart cities, smart environments, smart buildings are just a few macroscopic examples of how smart ecosystems are increasingly involved in our daily life, each one offering a different set of information. This information's decentralization and scattering can be exploited, optimizing mobile nodes on-demand information retrieval process. We propose an approach focused on defining competence domains in smart systems where the responsibility of providing a specific information to a mobile node is defined by spatial constraints. By exploiting the interplay and duality of Cloud Computing and Fog Computing we introduce an approach to exploit data spatial allocation in smart systems to optimize mobile nodes information retrieval.

To ensure the authenticity and integrity, data are traditionally signed by digital signatures, which will be invalidated by any processing of the data. With the vast amount of data generated every day, it is however desirable to allow flexible processing of the signed data via applying computations or functions on them, without losing the authenticity. Signatures can also serve as credentials for access control, which appears in many aspects of life, ranging from unlocking security gates of buildings, to virtual access of data by computer programs. With the prolific use of Internet-of-Things (IoT), everything is getting connected together. There is an emerging need for more versatile credentials to secure new application scenarios, for instance, assigning different credentials to different devices, such that they can authenticate and cooperate with each other to jointly perform some computation tasks. To realize the above, we envision a general framework called functional credentials. Functional credentials allow multiple entities to (jointly) issue, combine, delegate, present, verify, escrow, and decrypt different forms of credentials, by operating on the associated "cryptographic objects" including secret keys, attributes, ciphertexts, and auxiliary data (e.g., pseudonym, expiry date, or policies for combination / delegation / revocation). Instantiating this framework with different functions can provide a spectrum of solutions for securing IoT. This talk covers both the practical applications and theoretic foundations. I will first motivate the versatility of functional credentials by case studies on IoT, which identify the need of new credential systems. I will then formulate the definition of functional credentials. Finally, I will share some initial ideas in realizing functional credentials, and discuss the obstacles ahead.

The surprising success of cryptocurrencies has led to a surge of interest in deploying large scale, highly robust, Byzantine fault tolerant (BFT) protocols for mission-critical applications, such as financial transactions. Although the conventional wisdom is to build atop a (weakly) synchronous protocol such as PBFT (or a variation thereof), such protocols rely critically on network timing assumptions, and only guarantee liveness when the network behaves as expected. We argue these protocols are ill-suited for this deployment scenario. We present an alternative, HoneyBadgerBFT, the first practical asynchronous BFT protocol, which guarantees liveness without making any timing assumptions. We base our solution on a novel atomic broadcast protocol that achieves optimal asymptotic efficiency. We present an implementation and experimental results to show our system can achieve throughput of tens of thousands of transactions per second, and scales to over a hundred nodes on a wide area network. We even conduct BFT experiments over Tor, without needing to tune any parameters. Unlike the alternatives, HoneyBadgerBFT simply does not care about the underlying network.