Biblio

Today, Internet of Things (IoT) implements an ecosystem where a panoply of interconnected devices collect data from physical environments and supply them to processing services, on top of which cloud-based applications are built and provided to mobile end users. The undebatable advantages of smart IoT systems clash with the need of a secure and trustworthy environment. In this paper, we propose a service-based methodology based on blockchain and smart contracts for trustworthy evidence collection at the basis of a trustworthy IoT assurance evaluation. The methodology balances the provided level of trustworthiness and its performance, and is experimentally evaluated using Hyperledger fabric blockchain.

Plenty of methods applied in system identification, while those based on data-driven are increasingly popular. Usually we ignore the absence of outliers among the system to be modeled, but it is unreachable in reality. To improve the precision of identification towards system with outliers, advantageous approaches with robustness are needed. This study analyzes the superiority of weighted Least Square Support Vector Machine Regression (LS-SVMR) in the field of system identification under random outliers, and compare it with LS-SVMR mainly.

As cyber attacks to Industrial Internet of Things (IIoT) remain a major challenge, blockchain has emerged as a promising technology for IIoT security due to its decentralization and immutability characteristics. Existing blockchain designs, however, introduce high computational complexity and latency challenges which are unsuitable for IIoT. This paper proposes Xyreum, a new high-performance and scalable blockchain for enhanced IIoT security and privacy. Xyreum uses a Time-based Zero-Knowledge Proof of Knowledge (T-ZKPK) with authenticated encryption to perform Mutual Multi-Factor Authentication (MMFA). T-ZKPK properties are also used to support Key Establishment (KE) for securing transactions. Our approach for reaching consensus, which is a blockchain group decision-making process, is based on lightweight cryptographic algorithms. We evaluate our scheme with respect to security, privacy, and performance, and the results show that, compared with existing relevant blockchain solutions, our scheme is secure, privacy-preserving, and achieves a significant decrease in computation complexity and latency performance with high scalability. Furthermore, we explain how to use our scheme to strengthen the security of the REMME protocol, a blockchain-based security protocol deployed in several application domains.

In this paper we consider the threat surface and security of air gapped wallet schemes for permissioned blockchains as preparation for a Markov based mathematical model, and quantify the risk associated with private key leakage. We identify existing threats to the wallet scheme and existing work done to both attack and secure the scheme. We provide an overview the proposed model and outline justification for our methods. We follow with next steps in our remaining work and the overarching goals and motivation for our methods.

The rate at which a secure key can be generated in a quantum key distribution (QKD) protocol is limited by the channel loss and the quantum bit-error rate (QBER). Increases to the QBER can stem from detector noise, channel noise, or the presence of an eavesdropper, Eve. Eve is capable of obtaining information of the unsecure key by performing an attack on the quantum channel or by listening to all discussion performed via a noiseless public channel. Conventionally a QKD protocol will perform the information reconciliation over the authenticated public channel, revealing the parity bits used to correct for any quantum bit errors. In this invited paper, the possibility of limiting the information revealed to Eve during the information reconciliation is considered. Using a covert communication channel for the transmission of the parity bits, secure key rates are possible at much higher QBERs. This is demonstrated through the simulation of a polarization based QKD system implementing the BB84 protocol, showing significant improvement of the SKRs over the conventional QKD protocols.

To be prepared against cyberattacks, most organizations resort to security information and event management systems to monitor their infrastructures. These systems depend on the timeliness and relevance of the latest updates, patches and threats provided by cyberthreat intelligence feeds. Open source intelligence platforms, namely social media networks such as Twitter, are capable of aggregating a vast amount of cybersecurity-related sources. To process such information streams, we require scalable and efficient tools capable of identifying and summarizing relevant information for specified assets. This paper presents the processing pipeline of a novel tool that uses deep neural networks to process cybersecurity information received from Twitter. A convolutional neural network identifies tweets containing security-related information relevant to assets in an IT infrastructure. Then, a bidirectional long short-term memory network extracts named entities from these tweets to form a security alert or to fill an indicator of compromise. The proposed pipeline achieves an average 94% true positive rate and 91% true negative rate for the classification task and an average F1-score of 92% for the named entity recognition task, across three case study infrastructures.

Trust is known to be a key component in human social relationships. It is trust that defines human behavior with others to a large extent. Generative models have been extensively used in social networks study to simulate different characteristics and phenomena in social graphs. In this work, an attempt is made to understand how trust in social graphs can be combined with generative modeling techniques to generate trust-based social graphs. These generated social graphs are then compared with the original social graphs to evaluate how trust helps in generative modeling. Two well-known social network data sets i.e. the soc-Bitcoin and the wiki administrator network data sets are used in this work. Social graphs are generated from these data sets and then compared with the original graphs along with other standard generative modeling techniques to see how trust is a good component in this. Other Generative modeling techniques have been available for a while but this investigation with the real social graph data sets validate that trust can be an important factor in generative modeling.

We propose a distributed machine-learning architecture to predict trustworthiness of sensor services in Mobile Edge Computing (MEC) based Internet of Things (IoT) services, which aligns well with the goals of MEC and requirements of modern IoT systems. The proposed machine-learning architecture models training a distributed trust prediction model over a topology of MEC-environments as a Network Lasso problem, which allows simultaneous clustering and optimization on large-scale networked-graphs. We then attempt to solve it using Alternate Direction Method of Multipliers (ADMM) in a way that makes it suitable for MEC-based IoT systems. We present analytical and simulation results to show the validity and efficiency of the proposed solution.

Resolving distributed attacks benefits from collaboration between networks. We present three approaches for the same multi-domain defensive action that can be applied in such an alliance: 1) Counteract Everywhere, 2) Minimize Countermeasures, and 3) Minimize Propagation. First, we provide a formula to compute efficiency of a defense; then we use this formula to compute the efficiency of the approaches under various circumstances. Finally, we discuss how task execution order and timing influence defense efficiency. Our results show that the Minimize Propagation approach is the most efficient method when defending against the chosen attack.

Several efforts are currently active in dealing with scenarios combining fog, cloud computing, out of which a significant proportion is devoted to control, and manage the resulting scenario. Certainly, although many challenging aspects must be considered towards the design of an efficient management solution, it is with no doubt that whatever the solution is, the quality delivered to the users when executing services and the security guarantees provided to the users are two key aspects to be considered in the whole design. Unfortunately, both requirements are often non-convergent, thus making a solution suitably addressing both aspects is a challenging task. In this paper, we propose a decoupled transversal security strategy, referred to as DCF, as a novel architectural oriented policy handling the QoS-Security trade-off, particularly designed to be applied to combined fog-to-cloud systems, and specifically highlighting its impact on the delivered QoS.

In this paper, we focuses on an access control issue in the Internet of Things (IoT). Generally, we firstly propose a decentralized IoT system based on blockchain. Then we establish a secure fine-grained access control strategies for users, devices, data, and implement the strategies with smart contract. To trigger the smart contract, we design different transactions. Finally, we use the multi-index table struct for the access right's establishment, and store the access right into Key-Value database to improve the scalability of the decentralized IoT system. In addition, to improve the security of the system we also store the access records on the blockchain and database.

Fog computing extends cloud computing technology to the edge of the infrastructure to support dynamic computation for IoT applications. Reduced latency and location awareness in objects' data access is attained by displacing workloads from the central cloud to edge devices. Doing so, it reduces raw data transfers from target objects to the central cloud, thus overcoming communication bottlenecks. This is a key step towards the pervasive uptake of next generation IoT-based services. In this work we study efficient orchestration of applications in fog computing, where a fog application is the cascade of a cloud module and a fog module. The problem results into a mixed integer non linear optimisation. It involves multiple constraints due to computation and communication demands of fog applications, available infrastructure resources and it accounts also the location of target IoT objects. We show that it is possible to reduce the complexity of the original problem with a related placement formulation, which is further solved using a greedy algorithm. This algorithm is the core placement logic of FogAtlas, a fog computing platform based on existing virtualization technologies. Extensive numerical results validate the model and the scalability of the proposed algorithm, showing performance close to the optimal solution with respect to the number of served applications.

A plethora of recent work has shown that convolutional networks are not robust to adversarial images: images that are created by perturbing a sample from the data distribution as to maximize the loss on the perturbed example. In this work, we hypothesize that adversarial perturbations move the image away from the image manifold in the sense that there exists no physical process that could have produced the adversarial image. This hypothesis suggests that a successful defense mechanism against adversarial images should aim to project the images back onto the image manifold. We study such defense mechanisms, which approximate the projection onto the unknown image manifold by a nearest-neighbor search against a web-scale image database containing tens of billions of images. Empirical evaluations of this defense strategy on ImageNet suggest that it very effective in attack settings in which the adversary does not have access to the image database. We also propose two novel attack methods to break nearest-neighbor defense settings and show conditions under which nearest-neighbor defense fails. We perform a series of ablation experiments, which suggest that there is a trade-off between robustness and accuracy between as we use features from deeper in the network, that a large index size (hundreds of millions) is crucial to get good performance, and that careful construction of database is crucial for robustness against nearest-neighbor attacks.

The Named Data Network (NDN) is a promising network paradigm for content distribution based on caching. However, it may put consumer privacy at risk, as the adversary may identify the content, the name and the signature (namely a certificate) through side-channel timing responses from the cache of the routers. The adversary may identify the content name and the consumer node by distinguishing between cached and un- cached contents. In order to mitigate the timing attack, effective countermeasure methods have been proposed by other authors, such as random caching, random freshness, and probabilistic caching. In this work, we have implemented a timing attack scenario to evaluate the efficiency of these countermeasures and to demonstrate how the adversary can be detected. For this goal, a brute force timing attack scenario based on a real topology was developed, which is the first brute force attack model applied in NDN. Results show that the adversary nodes can be effectively distinguished from other legitimate consumers during the attack period. It is also proposed a multi-level mechanism to detect an adversary node. Through this approach, the content distribution performance can be mitigated against the attack.

The Internet of Things (IoT) is a technological vision in which constrained or embedded devices connect together through the Internet. This enables common objects to be empowered with communication and cooperation capabilities. Industry can take an enormous advantage of IoT, leading to the so-called Industrial IoT. In these systems, integrity, confidentiality, and access control over data are key requirements. An emerging approach to reach confidentiality and access control is Attribute-Based Encryption (ABE), which is a technique able to enforce cryptographically an access control over data. In this paper, we propose fABElous, an ABE scheme suitable for Industrial IoT applications which aims at minimizing the overhead of encryption on communication. fABElous ensures data integrity, confidentiality, and access control, while reducing the communication overhead of 35% with respect to using ABE techniques naively.

The Internet of Things (IoT) is enabling a new generation of innovative services based on the seamless integration of smart objects into information systems. Such IoT devices generate an uninterrupted flow of information that can be transmitted through an untrusted network and stored on an untrusted infrastructure. The latter raises new security and privacy challenges that require novel cryptographic methods. Attribute-Based Encryption (ABE) is a new type of public-key encryption that enforces a fine-grained access control on encrypted data based on flexible access policies. The feasibility of ABE adoption in fully-fledged computing systems, i.e. smartphones or embedded systems, has been demonstrated in recent works. In this paper we assess the feasibility of the adoption of ABE in typical IoT constrained devices, characterized by limited capabilities in terms of computing, storage and power. Specifically, an implementation of three ABE schemes for ESP32, a low-cost popular platform to deploy IoT devices, is developed and evaluated in terms of encryption/decryption time and energy consumption. The performance evaluation shows that the adoption of ABE on constrained devices is feasible, although it has a cost that increases with the number of attributes. The analysis in particular highlights how ABE has a significant impact in the lifetime of battery-powered devices, which is impaired significantly when a high number of attributes is adopted.

Policy based Security Management in an accepted practice in the industry, and required to simplify the administrative overhead associated with security management in complex systems. However, the growing dynamicity, complexity and scale of modern systems makes it difficult to write the security policies manually. Using AI, we can generate policies automatically. Security policies generated automatically can reduce the manual burden introduced in defining policies, but their impact on the overall security of a system is unclear. In this paper, we discuss the security metrics that can be associated with a system using generative policies, and provide a simple model to determine the conditions under which generating security policies will be beneficial to improve the security of the system. We also show that for some types of security metrics, a system using generative policies can be considered as equivalent to a system using manually defined policies, and the security metrics of the generative policy based system can be mapped to the security metrics of the manual system and vice-versa.

With WhatsApp's adoption of the Signal Protocol as its default, end-to-end encryption by the masses happened almost overnight. Unlike iMessage, WhatsApp notifies users that encryption is enabled, explicitly informing users about improved privacy. This rare feature gives us an opportunity to study people's understandings and perceptions of secure messaging pre-and post-mass messenger encryption (pre/post-MME). To study changes in perceptions, we compared the results of two mental models studies: one conducted in 2015 pre-MME and one in 2017 post-MME. Our primary finding is that users do not trust encryption as currently offered. When asked about encryption in the study, most stated that they had heard of encryption, but only a few understood the implications, even on a high level. Their consensus view was that no technical solution to stop skilled attackers from getting their data exists. Even with a major development, such as WhatsApp rolling out end-to-end encryption, people still do not feel well protected by their technology. Surprisingly, despite WhatsApp's end-to-end security info messages and the high media attention, the majority of the participants were not even aware of encryption. Most participants had an almost correct threat model, but don't believe that there is a technical solution to stop knowledgeable attackers to read their messages. Using technology made them feel vulnerable.

Remote attestation is a security technique through which a remote trusted party (i.e., Verifier) checks the trust-worthiness of a potentially untrusted device (i.e., Prover). In the Internet of Things (IoT) systems, the existing remote attestation protocols propose various approaches to detect the modified software and physical tampering attacks. However, in an inter-operable IoT system, in which IoT devices interact autonomously among themselves, an additional problem arises: a compromised IoT service can influence the genuine operation of other invoked service, without changing the software of the latter. In this paper, we propose a protocol for Remote Attestation of Distributed IoT Services (RADIS), which verifies the trust-worthiness of distributed IoT services. Instead of attesting the complete memory content of the entire interoperable IoT devices, RADIS attests only the services involved in performing a certain functionality. RADIS relies on a control-flow attestation technique to detect IoT services that perform an unexpected operation due to their interactions with a malicious remote service. Our experiments show the effectiveness of our protocol in validating the integrity status of a distributed IoT service.

The Internet of Things (IoT) is growing rapidly controlling and connecting thousands of devices every day. The increased number of interconnected devices increase the network traffic leading to energy and Quality of Service efficiency problems of the IoT network. Therefore, IoT platforms and networks are susceptible to failures and attacks that have significant economic and security consequences. In this regard, implementing effective secure IoT platforms and networks are valuable for both the industry and society. In this paper, we propose two frameworks that aim to verify a number of security policies related to runtime information of the network and dynamic flow routing paths, respectively. The underlying rationale is to allow the operator of an IoT network in order to have an overall control of the network and to define different policies based on the demands of the network and the use cases (e.g., achieving more secure or faster network).

Upon the new paradigm of Cellular Internet of Things, through the usage of technologies such as Narrowband IoT (NB-IoT), a massive amount of IoT devices will be able to use the mobile network infrastructure to perform their communications. However, it would be beneficial for these devices to use the same security mechanisms that are present in the cellular network architecture, so that their connections to the application layer could see an increase on security. As a way to approach this, an identity management and provisioning mechanism, as well as an identity federation between an IoT platform and the cellular network is proposed as a way to make an IoT device deemed worthy of using the cellular network and perform its actions.

Naturally Grover is best at detecting its own fake articles, since in a way the agent knows its own processes. But it can also detect those made by other models, such as OpenAI's GPT2, with high accuracy.

Internet of Things is nowadays growing faster than ever before. Operators are planning or already creating dedicated networks for this type of devices. There is a need to create dedicated solutions for this type of network, especially solutions related to information security. In this article we present a mechanism of security-aware routing, which takes into account the evaluation of trust in devices and packet flows. We use trust relationships between flows and network nodes to create secure SDN paths, not ignoring also QoS and energy criteria. The system uses SDN infrastructure, enriched with Cognitive Packet Networks (CPN) mechanisms. Routing decisions are made by Random Neural Networks, trained with data fetched with Cognitive Packets. The proposed network architecture, implementing the security-by-design concept, was designed and is being implemented within the SerIoT project to demonstrate secure networks for the Internet of Things (IoT).

Attacks on cloud-computing services are becoming more prevalent with recent victims including Tesla, Aviva Insurance and SIM-card manufacturer Gemalto[1]. The risk posed to organisations from malicious insiders is becoming more widely known about and consequently many are now investing in hardware, software and new processes to try to detect these attacks. As for all types of attack vector, there will always be those which are not known about and those which are known about but remain exceptionally difficult to detect - particularly in a timely manner. We believe that insider attacks are of particular concern in a cloud-computing environment, and that cloud-service providers should enhance their ability to detect them by means of indirect detection. We propose a combined attack-tree and kill-chain based method for identifying multiple indirect detection measures. Specifically, the use of attack trees enables us to encapsulate all detection opportunities for insider attacks in cloud-service environments. Overlaying the attack tree on top of a kill chain in turn facilitates indirect detection opportunities higher-up the tree as well as allowing the provider to determine how far an attack has progressed once suspicious activity is detected. We demonstrate the method through consideration of a specific type of insider attack - that of attempting to capture virtual machines in transit within a cloud cluster via use of a network tap, however, the process discussed here applies equally to all cloud paradigms.

In recent years, almost all the real-world operations are transferred to cyber world and these market computers connect with each other via Internet. As a result of this, there is an increasing number of security breaches of the networks, whose admins cannot protect their networks from the all types of attacks. Although most of these attacks can be prevented with the use of firewalls, encryption mechanisms, access controls and some password protections mechanisms; due to the emergence of new type of attacks, a dynamic intrusion detection mechanism is always needed in the information security market. To enable the dynamicity of the Intrusion Detection System (IDS), it should be updated by using a modern learning mechanism. Neural Network approach is one of the mostly preferred algorithms for training the system. However, with the increasing power of parallel computing and use of big data for training, as a new concept, deep learning has been used in many of the modern real-world problems. Therefore, in this paper, we have proposed an IDS system which uses GPU powered Deep Learning Algorithms. The experimental results are collected on mostly preferred dataset KDD99 and it showed that use of GPU speed up training time up to 6.48 times depending on the number of the hidden layers and nodes in them. Additionally, we compare the different optimizers to enlighten the researcher to select the best one for their ongoing or future research.