Biblio

In addition to the feature of real-time analytics, fog computing allows detection nodes to be located at the edges of the network. On the other hand, intrusion detection systems require prompt and accurate attack analysis and detection. These systems must promptly respond appropriately to an event. Increasing the speed of data transfer and response requires less bandwidth in the network, reducing the data sent to the cloud and increasing information security as some of the advantages of using detection nodes at the edges of the network in fog computing. The use of neural networks in the analyzer engine is important for the low consumption of system resources, avoidance of explicit production of detection rules, detection of known deformed attacks, and the ability to manage noise and outlier data. The current paper proposes and implements the architecture of network intrusion detection nodes in fog computing, in addition to presenting the proposed fog network architecture. In the proposed architecture, each node can, in addition to performing intrusion detection operations, observe the nodes around it, find the compromised node or intrusion node, and inform the nodes close to it to disconnect from that node.

Verifying the identity of nodes within a wireless ad hoc mesh network and the authenticity of their messages in sufficiently secure, yet power-efficient ways is a long-standing challenge. This paper shows how the more recent concepts of self-sovereign identity management can be applied to Internet-of-Things mesh networks, using LoRaWAN as an example and applying Sovrin's decentralized identifiers and verifiable credentials in combination with Schnorr signatures for securing the communication with a focus on simplex and broadcast connections. Besides the concept and system architecture, the paper discusses an ESP32-based implementation using SX1276/SX1278 LoRa chips, adaptations made to the lmic- and MbedTLS-based software stack, and practically evaluates performance aspects in terms of data overhead, time-on-air impact, and power consumption.

The Internet of Things integrates multiple hardware appliances from large cloud data centres to constrained devices embedded within the physical reality, from multiple vendors and providers, under the same infrastructure. These appliances are subject to different restrictions, have different available resources and show different risk profiles and vulnerabilities. In these scenarios, remote attestation mechanisms are essential, enabling the verification of a distant appliance’s internal state before allowing it to access sensitive data or execute critical workloads. This work proposes a new attestation approach based on a Trusted Platform Module (TPM), devoted to performing Remote Attestation as a Service (RAaaS) while guaranteeing essential properties such as flexibility, generality, domain separation and authorized initiation. The proposed solution can prove both edge devices and IoT devices reliability to services running on cloud data centres. Furthermore, the first prototype of this service has been validated and evaluated via a real use case.

Nowadays, the Internet of Things (IoT) is playing an important role in our life. This inevitably generates mass data and requires a more secure transmission. As blockchain technology can build trust in a distributed environment and ensure the data traceability and tamper resistance, it is a promising way to support IoT data transmission and sharing. In this paper, edge computing is considered to provide adequate resources for end users to offload computing tasks in the blockchain enabled IoT system, and the node pairing problem between end users and edge computing servers is researched with the consideration of wireless channel quality and the service quality. From the perspective of the end users, the objective optimization is designed to maximize the profits and minimize the payments for completing the tasks and ensuring the resource limits of the edge servers at the same time. The deep reinforcement learning (DRL) method is utilized to train an intelligent strategy, and the policy gradient based node pairing (PG-NP) algorithm is proposed. Through a deep neural network, the well-trained policy matched the system states to the optimal actions. The REINFORCE algorithm with baseline is applied to train the policy network. According to the training results, as the comparison strategies are max-credit, max-SINR, random and max-resource, the PG-NP algorithm performs about 57% better than the second-best method. And testing results show that PGNP also has a good generalization ability which is negatively correlated with the training performance to a certain extend.

RPL, the IPv6 Routing Protocol for low-power and lossy networks, was standardized by the Internet Engineering Task Force (IETF) in 2011. It is developed to connect resource constrained devices enabled by low-power and lossy networks (LLNs). RPL prominently becomes the routing protocol for IoT. However, the RPL protocol is facing many challenges such as trustworthiness among the nodes which need to be addressed and resolved to make the network secure and efficient. In this paper, a multicasting technique is developed that is based on trust mechanism to resolve this issue. This mechanism manages and protects the network from untrusted nodes which can hamper the security and result in delayed and distorted transmission of data. It allows any node to decide whether to trust other nodes or not during the construction of the topology. This is then proved efficient by comparing it with broadcasting nature of the transmission among the nodes in terms of energy, throughput, percentage of alive and dead nodes.

Majority of the daily and business activities nowadays are integrated and interconnected to the world across national, geographic and boundaries. Securing the Internet of Things (IoT) system is a challenge as these low powered devices in IoT system are very vulnerable to cyber-attacks and this will reduce the reliability of the system. Software Defined Network (SDN) intends to greatly facilitate the policy enforcement and dynamic network reconfiguration. This paper presents several architectures in the integration of IoT via SDN to improve security in the network and system.

To harness the value of data generated from IoT, there is a crucial requirement of new mechanisms. Machine learning (ML) is among the most suitable paradigms of computation which embeds strong intelligence within IoT devices. Various ML techniques are being widely utilised for improving network security in IoT. These techniques include reinforcement learning, semi-supervised learning, supervised learning, and unsupervised learning. This report aims to critically analyse the role played by supervised and unsupervised ML for the enhancement of IoT security.

Electrocardiogram (ECG) biometric authentication (EBA) is a promising approach for human identification, particularly in consumer devices, due to the individualized, ubiquitous, and easily identifiable nature of ECG signals. Thus, computing architectures for EBA must be accurate, fast, energy efficient, and secure. In this article, first, we implement an EBA algorithm to achieve 100% accuracy in user authentication. Thereafter, we extensively analyze the algorithm to show the distinct variance in execution requirements and reveal the latency bottleneck across the algorithm's different steps. Based on our analysis, we propose a domain-specific architecture (DSA) to satisfy the execution requirements of the algorithm's different steps and minimize the latency bottleneck. We explore different variations of the DSA, including one that features the added benefit of ensuring constant timing across the different EBA steps, in order to mitigate the vulnerability to timing-based side-channel attacks. Our DSA improves the latency compared to a base ARM-based processor by up to 4.24×, while the constant timing DSA improves the latency by up to 19%. Also, our DSA improves the energy by up to 5.59×, as compared to the base processor.

As a typical representative of a new generation military information technology, the value and significance of Internet of Battlefield Things (IoBT) has been widely recognized by the world's military forces. At the same time, Internet of Battlefield Things (IoBT) is facing serious scalability and security challenges. This paper presents the basic concept and six-domain model of IoBT, explains the integration security framework of IoBT and blockchain. Furthermore, we design and build a novel IoT framework called IoBTChain based on blockchain and smart contracts, which adopts a credit-based resource management system to control the amount of resources that an IoBT device can obtain from a cloud server based on pre-defined priority rules, application types, and behavior history. We illustrate the deployment procedure of blockchain and smart contracts, the device registration procedure on blockchain, the IoBT behavior regulation workflow and the pricing-based resource allocation algorithm.

The recent advancements in ubiquitous sensing powered by Wireless Computing Technologies (WCT) and Cloud Computing Services (CCS) have introduced a new thinking ability amongst researchers and healthcare professionals for building secure and connected healthcare systems. The integration of Internet of Things (IoT) in healthcare services further brings in several challenges with it, mainly including encrypted communication through vulnerable wireless medium, authentication and access control algorithms and ownership transfer schemes (important patient information). Major concern of such giant connected systems lies in creating the data handling strategies which is collected from the billions of heterogeneous devices distributed across the hospital network. Besides, the resource constrained nature of IoT would make these goals difficult to achieve. Motivated by aforementioned deliberations, this paper introduces a novel approach in designing a security framework for edge-computing based connected healthcare systems. An efficient, multi-factor access control and ownership transfer mechanism for edge-computing based futuristic healthcare applications is the core of proposed framework. Data scalability is achieved by employing distributed approach for clustering techniques that analyze and aggregate voluminous data acquired from heterogeneous devices individually before it transits the to the cloud. Moreover, data/device ownership transfer scheme is considered to be the first time in its kind. During ownership transfer phase, medical server facilitates user to transfer the patient information/ device ownership rights to the other registered users. In order to avoid the existing mistakes, we propose a formal and informal security analysis, that ensures the resistance towards most common IoT attacks such as insider attack, denial of distributed service (DDoS) attack and traceability attacks.

The concept behind the Internet of Things (IoT) is taking everything and connecting to the internet so that all devices would be able to send and receive data online. Internet of Vehicles (IoV) is a key component of smart city which is an outcome of IoT. Nowadays the concept of IoT has plaid an important role in our daily life in different sectors like healthcare, agriculture, smart home, wearable, green computing, smart city applications, etc. The emerging IoV is facing a lack of rigor in data processing, limitation of anonymity, privacy, scalability, security challenges. Due to vulnerability IoV devices must face malicious hackers. Nowadays with the help of blockchain (BC) technology energy system become more intelligent, eco-friendly, transparent, energy efficient. This paper highlights two major challenges i.e. scalability and security issues. The flavor of edge computing (EC) considered here to deal with the scalability issue. A BC is a public, shared database that records transactions between two parties that confirms owners through cryptography. After a transaction is validated and cryptographically verified generates “block” on the BC and transactions are ordered chronologically and cannot be altered. Implementing BC and smart contracts technologies will bring security features for IoV. It plays a role to implement the rules and policies to govern the IoV information and transactions and keep them into the BC to secure the data and for future uses.

Fog Computing provides users with the cloud facilities at the network edge. It may be assumed to be a virtual platform with adequate storage., computation and processing facilities for latency-sensitive applications. The basic difference lies with the fact that this platform is decentralized in nature. In addition., the fog systems or devices process data locally., are conveyable and are capable of being installed on heterogenous hardware. This versatility in its behavior and it being at the network edge turns the attention towards the security of the users sensitive data (in transition or at rest). In this paper., the authors have emphasized on the security of the fog level in typical Fog- IoT architecture. Various security factors (along with their subfactors) persisting at fog level are identified and discussed in detail. The authors have presented a hierarchy of fog computing security factors that is expected to help in considering security in a systematic and efficient manner. Further., the authors have also ranked the same through Analytical Hierarchy Process (AHP) and compared the results with Fuzzy-AHP (F-AHP). The results are found to be highly correlated.

Large scale deployment of IoT devices has made Bluetooth Protocol (IEEE 802.15.1) the wireless protocol of choice for close-range communications. Devices such as keyboards, smartwatches, headphones, computer mouse, and various wearable connecting devices use Bluetooth network for communication. Moreover, Bluetooth networks are widely used in medical devices like heart monitors, blood glucose monitors, asthma inhalers, and pulse oximeters. Also, Bluetooth has replaced cables for wire-free equipment in a surgical environment. In hospitals, devices communicate with one another, sharing sensitive and critical information over Bluetooth scatter-networks. Thus, it is imperative to secure the Bluetooth networks against attacks like Man in the Middle attack (MITM), eavesdropping attacks, and Denial of Service (DoS) attacks. This paper presents a Multi-Level Bluetooth Intrusion Detection System (ML-BIDS) to detect malicious attacks against Bluetooth devices. In the ML-IDS framework, we perform continuous device identification and authorization in Bluetooth networks following the zero-trust principle [ref]. The ML-BIDS framework includes an anomaly-based intrusion detection system (ABIDS) to detect attacks on the Bluetooth protocol. The ABIDS tracks the normal behavior of the Bluetooth protocol by comparing it with the Bluetooth protocol state machine. Bluetooth frame flows consisting of Bluetooth frames received over 10 seconds are split into n-grams to track the current state of the protocol in the state machine. We evaluated the performance of several machine learning algorithms like C4.5, Adaboost, SVM, Naive Bayes, Jrip, and Bagging to classify normal Bluetooth protocol flows from abnormal Bluetooth protocol flows. The ABIDS detects attacks on Bluetooth protocols with a precision of up to 99.6% and recall up to 99.6%. The ML-BIDS framework also performs whitelisting of the devices on the Bluetooth network to prevent unauthorized devices from connecting to the network. ML-BIDS uses a combination of the Bluetooth Address, mac address, and IP address to uniquely identify a Bluetooth device connecting to the network, and hence ensuring only authorized devices can connect to the Bluetooth network.

Providing an acceptable level of security for Internet of Things (IoT)-based critical infrastructures, such as the connected vehicles, considers as an open research issue. Nowadays, blockchain overcomes a wide range of network limitations. In the context of IoT and blockchain, Byzantine Fault Tolerance (BFT)-based consensus protocol, that elects a set of authenticated devices/nodes within the network, considers as a solution for achieving the desired energy efficiency over the other consensus protocols. In BFT, the elected devices are responsible for ensuring the data blocks' integrity and preventing the concurrently appended blocks that might contain some malicious data. In this paper, we evaluate the fault-tolerance with different network settings, i.e., the number of connected vehicles. We verify and validate the proposed model with MATLAB/Simulink package simulations. The results show that our proposed hybrid scenario performed over the non-hybrid scenario taking throughput and latency in the consideration as the evaluated metrics.

Nowadays, Internet Of Everything (IOE) has demanded for a wide range of applications areas. IOE is started to replaces an Internet Of things (IOT). IOE is a combination of massive number of computing elements and sensors, people, processes and data through the Internet infrastructure. Device to Device communication and interfacing of Wireless Sensor network with IOE can makes any system as a Smart System. With the increased the use of Internet and Internet connected devices has opportunities for hackers to launch attacks on unprecedented scale and impact. The IOE can serve the varied security in the various sectors like manufacturing, agriculture, smart grid, payments, IoT gateways, healthcare and industrial ecosystems. To secure connections among people, process, data, and things, is a major challenge in Internet of Everything.. This paper focuses on various security Issues and Authentication Schemes in the IOE systems.

Suppose we are given a large number of sequences on a given alphabet, and an adversary is interested in identifying (de-anonymizing) a specific target sequence based on its patterns. Our goal is to thwart such an adversary by obfuscating the target sequences by applying artificial (but small) distortions to its values. A key point here is that we would like to make no assumptions about the statistical model of such sequences. This is in contrast to existing literature where assumptions (e.g., Markov chains) are made regarding such sequences to obtain privacy guarantees. We relate this problem to a set of combinatorial questions on sequence construction based on which we are able to obtain provable guarantees. This problem is relevant to important privacy applications: from fingerprinting webpages visited by users through anonymous communication systems to linking communicating parties on messaging applications to inferring activities of users of IoT devices.

Traditional security solutions that rely on public key infrastructure present scalability and transparency challenges when deployed in Internet of Things (IoT). In this paper, we develop a blockchain based authentication mechanism for IoT that can be integrated into the traditional transport layer security protocols such as Transport Layer Security (TLS) and Datagram Transport Layer Security (DTLS). Our proposed mechanism is an alternative to the traditional Certificate Authority (CA)-based Public Key Infrastructure (PKI) that relies on x.509 certificates. Specifically, the proposed solution enables the modified TLS/DTLS a viable option for resource constrained IoT devices where minimizing memory utilization is critical. Experiments show that blockchain based authentication can reduce dynamic memory usage by up to 20%, while only minimally increasing application image size and time of execution of the TLS/DTLS handshake.

In the Internet of Things (IoT), devices can interconnect and communicate autonomously, which requires devices to authenticate each other to exchange meaningful information. Otherwise, these things become vulnerable to various attacks. The conventional security protocols are not suitable for IoT applications due to the high computation and storage demand. Therefore, we proposed a blockchain-enabled secure storage and communication scheme for IoT applications, called BSS. The scheme ensures identification, authentication, and data integrity. Our scheme uses the security advantages of blockchain and helps to create safe zones (trust batch) where authenticated objects interconnect securely and do communication. A secure and robust trust mechanism is employed to build these batches, where each device has to authenticate itself before joining the trust batch. The obtained results satisfy the IoT security requirements with 60% reduced computation, storage and communication cost compared with state-of-the-art schemes. BSS also withstands various cyberattacks such as impersonation, message replay, man-in-the-middle, and botnet attacks.

Blockchain has received tremendous attention in non-monetary applications including the Internet of Things (IoT) due to its salient features including decentralization, security, auditability, and anonymity. Most conventional blockchains rely on computationally expensive validator selection and consensus algorithms, have limited throughput, and high transaction delays. In this paper, we propose tree-chain a scalable fast blockchain instantiation that introduces two levels of randomization among the validators: i) transaction level where the validator of each transaction is selected randomly based on the most significant characters of the hash function output (known as consensus code), and ii) blockchain level where validator is randomly allocated to a particular consensus code based on the hash of their public key. Tree-chain introduces parallel chain branches where each validator commits the corresponding transactions in a unique ledger.

Internet of Things (IoT) is experiencing significant growth in the safety-critical applications which have caused new security challenges. These devices are becoming targets for different types of physical attacks, which are exacerbated by their diversity and accessibility. Therefore, there is a strict necessity to support embedded software developers to identify and remediate the vulnerabilities and create resilient applications against such attacks. In this paper, we propose a hardware security vulnerability assessment based on fault injection of an embedded application. In our security assessment, we apply a fault injection attack by using our clock glitch generator on a critical medical IoT device. Furthermore, we analyze the potential risks of ignoring these attacks in this embedded application. The results will inform the embedded software developers of various security risks and the required steps to improve the security of similar MCU-based applications. Our hardware security assessment approach is easy to apply and can lead to secure embedded IoT applications against fault attacks.

This paper describes a system that comprises of control, safety and security subsystem for industries and homes. The entire system is based on the Bolt IoT platform. Using this system, the user can control the devices such as LEDs, speed of the fan or DC motor, monitor the temperature of the premises with an alert sub-system for critical temperatures through SMS and call, monitor the presence of anyone inside the premises with an alert sub-system about any intrusion through SMS and call. If the system is used specifically in any industry then instead of monitoring the temperature any other physical quantity, which is critical for that industry, can be monitored using suitable sensors. In addition, the cloud connectivity is provided to the system using the Bolt IoT module and temperature data is sent to the cloud where using machine-learning algorithm the future temperature is predicted to avoid any accidents in the future.

Securing cyber-physical systems (CPS) and Internet of Things (IoT) systems requires the identification of how interdependence among existing atomic vulnerabilities may be exploited by an adversary to stitch together an attack that can compromise the system. Therefore, accurate attack graphs play a significant role in systems security. A manual construction of the attack graphs is tedious and error-prone, this paper proposes a model-checking-based automated attack graph generator and visualizer (A2G2V). The proposed A2G2V algorithm uses existing model-checking tools, an architecture description tool, and our own code to generate an attack graph that enumerates the set of all possible sequences in which atomic-level vulnerabilities can be exploited to compromise system security. The architecture description tool captures a formal representation of the networked system, its atomic vulnerabilities, their pre-and post-conditions, and security property of interest. A model-checker is employed to automatically identify an attack sequence in the form of a counterexample. Our own code integrated with the model-checker parses the counterexamples, encodes those for specification relaxation, and iterates until all attack sequences are revealed. Finally, a visualization tool has also been incorporated with A2G2V to generate a graphical representation of the generated attack graph. The results are illustrated through application to computer as well as control (SCADA) networks.

The occurrences of accidents in mining industries owing to the fragile health conditions of mine workers are reportedly increasing. Health conditions measured as heart rate or pulse, glycemic index, and blood pressure are often crucial parameters that lead to failure in proper reasoning when not within acceptable ranges. These parameters, such as heartbeat rate can be measured continuously using sensors. The data can be monitored remotely and, when found to be of concern, can send necessary alarms to the mine manager. The early alarm notification enables the mine manager with better preparedness for managing the reach of first aid to the accident spot and thereby reduce mine fatalities drastically. This paper presents a framework for deterring accidents in mines with the help of the Grey Wolf Optimization approach.

Low-power wireless network technology is one of the main key characteristics in communication systems that are needed by the Internet of Things (IoT). Nowadays, the 6LoWPAN standard is one of the communication protocols which has been identified as an important protocol in IoT applications. Networking technology in 6LoWPAN transfer IPv6 packets efficiently in link-layer framework that is well-defined by IEEE 802.14.5 protocol. 6Lo WPAN development is still having problems such as threats and entrust crises. The most important part when developing this new technology is the challenge to secure the network. Data security is viewed as a major consideration in this network communications. Many researchers are working to secure 6LoWPAN communication by analyzing the architecture and network features. 6LoWPAN security weakness or vulnerability is exposed to various forms of network attack. In this paper, the security solutions for 6LoWPAN have been investigated. The requirements of safety in 6LoWPAN are also presented.

This paper reviews the definitions and characteristics of military effects, the Internet of Battlefield Things (IoBT), and their impact on decision processes in a Multi-Domain Operating environment (MDO). The aspects of contemporary military decision-processes are illustrated and an MDO Effect Loop decision process is introduced. We examine the concept of IoBT effects and their implications in MDO. These implications suggest that when considering the concept of MDO, as a doctrine, the technological advances of IoBTs empower enhancements in decision frameworks and increase the viability of novel operational approaches and options for military effects.