Biblio

IoT applications are changing our daily lives. These innovative applications are supported by new communication technologies and protocols. Particularly, the information-centric network (ICN) paradigm is well suited for many IoT application scenarios that involve large-scale wireless sensor networks (WSNs). Even though the ICN approach can significantly reduce the network traffic by optimizing the process of information recovery from network nodes, it is also possible to apply data aggregation strategies. This paper proposes an unsupervised machine learning-based data aggregation strategy for multi-hop information-centric WSNs. The results show that the proposed algorithm can significantly reduce the ICN data traffic while having reduced information degradation.

The Internet of Things (IoT) paradigm, with its highly distributed and interconnected architecture, is gaining ground in Industry 4.0 and in critical infrastructures like the eHealth sector, the Smart Grid, Intelligent Power Plants and Smart Mobility. In these critical sectors, the preservation of metrological characteristics and their traceability is a strong legal requirement, just like cyber-security, since it offers the ground for liability. Any vulnerability in the system in which the metrological network is embedded can endanger human lives, the environment or entire economies. This paper presents a framework comprised of a methodology and some tools for the governance of the metrological chain. The proposed methodology combines the RAMI 4.0 model, which is a Reference Architecture used in the field of Industrial Internet of Things (IIoT), with the the Reference Model for Information Assurance & Security (RMIAS), a framework employed to guarantee information assurance and security, merging them with the well established paradigms to preserve calibration and referability of metrological instruments. Thus, metrological traceability and cyber-security are taken into account straight from design time, providing a conceptual space to achieve security by design and to support the maintenance of the metrological chain over the entire system lifecycle. The framework lends itself to be completely automatized with Model Checking to support automatic detection of non conformity and anomalies at run time.

Management control and monitoring of production activities in intelligent environments in subway mines must be aligned with the strategies and objectives of each agent. It is required that in operations, the local structure of each service is fault-tolerant and that large amounts of data are transmitted online to executives to make effective and efficient decisions. The paper proposes an architecture that enables strategic text analysis on the Internet of Things devices through task partitioning with multiple agent systems and evaluates the feasibility of the design by building a prototype that improves communication. The results validate the system's design because Raspberry Pi can execute text mining algorithms and agents in about 3 seconds for 197 texts. This work emphasizes multiple agents for text analytics because the algorithms, along with the agents, use about 70% of a Raspberry Pi CPU.

Security is a requirement in society, yet its wide implementation is held back because of high expenses, and barriers to the use of technology. Experimental implementation of security at low cost will only help in promoting the technology at more affordable prices. This paper describes the design of a security system of surveillance using Raspberry Pi and Arduino UNO. The design senses the presence of \$a\$ human in a surveillance area and immediately sets off the buzzer and simultaneously starts capturing video of the motion it had detected and stores it in a folder. When the design senses a motion, it immediately sends an SMS to the user. The user of this design can see the live video of the motion it detects using the internet connection from a remote area. Our objective of making a low-cost surveillance area security system has been mostly fulfilled. Although this is a low-cost project, features can be compared with existing commercially available systems.

IoT is still an emerging technology without a lot of standards around it, which makes it difficult to integrate it into existing businesses, what's more, with restricted assets and expanding gadgets that essentially work with touchy information. Thus, information safety has become urgent for coders and clients. Thus, painstakingly chosen and essentially tested encryption calculations should be utilized to grow the gadgets productively, to decrease the danger of leaking the delicate information. This investigation looks at the ECC calculation (Elliptic Curve Cryptography) and Rivest-Shamir-Adleman (RSA) calculation. Furthermore, adding the study of DNA encoding operation in DNA computing with ECC to avoid attackers from getting access to the valuable data.

Implementing the color images encryption is a challenging field of the research for IOT applications. An exponential growth in imaging cameras in IOT uses makes it critical to design the robust image security algorithms. It is also observed that performance of existing encryption methods degrades under the presence of noisy environments. This is the major concern of evaluating the encryption method in this paper. The prime concern of this paper is to design the fast efficient color images encryption algorithm by designing an efficient and robustness RSA encryption algorithm. Method takes the advantage of both preprocessing and the Gaussian pyramid (GP) approach for encryption. To improve the performance it is proposed to use the LAB color space and implement the RSA encryption on luminance (L) component using the GP domain. The median filter and image sharpening is used for preprocessing. The goal is to improve the performance under highly noisy imaging environment. The performance is compared based on the crypto weights and on the basis of visual artifacts and entropy analysis. The decrypted outputs are again converted to color image output. Using the LAB color space is expected to improve the entropy performance of the image. Result of proposed encryption method is evaluated under the different types of the noisy attacks over the color images and also performance is compared with state of art encryption methods. Significant improvement speed of the algorithm is compared in terms of the elapsed time

Ciphertext Policy Attribute-Based Encryption (CPABE) has gained popularity in the research area among the many proposed security models for providing fine-grained access control of data. Lightweight ECC-based CP-ABE schemes can provide feasible selective sharing from resource-constrained devices. However, the existing schemes lack support for a complete revocation mechanism at the user and attribute levels. We propose a novel scheme called Ecc Proxy based Scalable Attribute Revocation (EPSAR-CP-ABE) scheme. It extends an existing ECC-based CP-ABE scheme for lightweight IoT and smart-card devices to implement scalable attribute revocation. The scheme does not require re-distribution of secret keys and re-encryption of ciphertext. It uses a proxy server to furnish a proxy component for decryption. The dependency of the proposed scheme is minimal on the proxy server compared to the other related schemes. The storage and computational overhead due to the attribute revocation feature are negligible. Hence, the proposed EPSAR-CP-ABE scheme can be deployed practically for resource-constrained devices.

Wireless Sensor Networks (WSN) are gaining popularity as being the backbone of Cyber physical systems, IOT and various data acquisition from sensors deployed in remote, inaccessible terrains have remote deployment. However due to remote deployment, WSN is an adhoc network of large number of sensors either heli-dropped in inaccessible terrain like volcanoes, Forests, border areas are highly energy deficient and available in large numbers. This makes it the right soup to become vulnerable to various kinds of Security attacks. The lack of energy and resources makes it deprived of developing a robust security code for mitigation of various kinds of attacks. Many attempts have been made to suggest a robust security Protocol. But these consume so much energy, bandwidth, processing power, memory and other resources that the sole purpose of data gathering from inaccessible terrain from energy deprived sensors gets defeated. This paper makes an attempt to study the types of attacks on different layers of WSN and the examine the recent trends in development of various security protocols to mitigate the attacks. Further, we have proposed a simple, lightweight but powerful security protocol known as Simple Sensor Security Protocol (SSSP), which captures the uniqueness of WSN and its isolation from internet to develop an energy efficient security solution.

Internet of Things (IoT) is one relatively new technology, which aims to make our lives easier by automating our daily processes. This article would aim to deliver an idea how to prevent the IoT technology, delivering maliciously and bad things and how to scale. The intention of this research is to explain how a specific implementation of a Blockchain network, enterprise-grade permissioned distributed ledger framework called Hyperledger Fabric, can be used to resolve the security and scalability issues in an IoT network.

The Internet of Things (IoT) is clubbed by networking of sensors and other embedded electronics. As more devices are getting connected, the vulnerability of getting affected by various IoT threats also increases. Among the IoT threads, DDoS attacks are causing serious issues in recent years. In IoT, these attacks are challenging to detect and isolate. Thus, an effective Intrusion Detection System (IDS) is essential to defend against these attacks. The traditional IDS is based on manual blacklisting. These methods are time-consuming and will not be effective to detect novel intrusions. At present, IDS are automated and programmed to be dynamic which are aided by machine learning & deep learning models. The performance of these models mainly depends on the data used to train the model. Majority of IDS study is performed with non-compatible and outdated datasets like KDD 99 and NSL KDD. Research on specific DDoS attack datasets is very less. Therefore, in this paper, we first aim to examine the effect of existing datasets in the IoT environment. Then, we propose a real-time data collection framework for DNS amplification attacks in IoT. The generated network packets containing DDoS attack is captured through port mirroring.

Internet of Things (IoT) devices have rooted themselves in the everyday life of billions of people. Thus, researchers have applied automated bug finding techniques to improve their overall security. However, due to the difficulties in extracting and emulating custom firmware, black-box fuzzing is often the only viable analysis option. Unfortunately, this solution mostly produces invalid inputs, which are quickly discarded by the targeted IoT device and do not penetrate its code. Another proposed approach is to leverage the companion app (i.e., the mobile app typically used to control an IoT device) to generate well-structured fuzzing inputs. Unfortunately, the existing solutions produce fuzzing inputs that are constrained by app-side validation code, thus significantly limiting the range of discovered vulnerabilities.In this paper, we propose a novel approach that overcomes these limitations. Our key observation is that there exist functions inside the companion app that can be used to generate optimal (i.e., valid yet under-constrained) fuzzing inputs. Such functions, which we call fuzzing triggers, are executed before any data-transforming functions (e.g., network serialization), but after the input validation code. Consequently, they generate inputs that are not constrained by app-side sanitization code, and, at the same time, are not discarded by the analyzed IoT device due to their invalid format. We design and develop Diane, a tool that combines static and dynamic analysis to find fuzzing triggers in Android companion apps, and then uses them to fuzz IoT devices automatically. We use Diane to analyze 11 popular IoT devices, and identify 11 bugs, 9 of which are zero days. Our results also show that without using fuzzing triggers, it is not possible to generate bug-triggering inputs for many devices.

Internet of Things (IoT) devices are ubiquitous, with web cameras, smart refrigerators, and digital assistants appearing in homes, offices, and public spaces. However, these devices are lacking in security measures due to their low time to market and insufficient funding for security research and development. In order to improve the security of IoTs, we have defined novel security metrics based on generic IoT characteristics. Furthermore, we have developed automation for experimentation with IoT devices that results to repeatable and reproducible calculations of security metrics within a realistic IoT testbed. Our results demonstrate that repeatable IoT security measurements are feasible with automation. They prove quantitatively intuitive hypotheses. For example, an large number of inbound / outbound network connections contributes to higher probability of compromise or measuring password strength leads to a robust estimation of IoT security.

The growing adoption of IoT devices is creating a huge positive impact on human life. However, it is also making the network more vulnerable to security threats. One of the major threats is malicious traffic injection attack, where the hacked IoT devices overwhelm the application servers causing large-scale service disruption. To address such attacks, we propose a Software Defined Networking based predictive alarm manager solution for malicious traffic detection and mitigation at the IoT Gateway. Our experimental results with the proposed solution confirms the detection of malicious flows with nearly 95% precision on average and at its best with around 99% precision.

The rapid expansion and diversity of technology throughout society have impacted the growing knowledge gap in conducting analysis on IoT devices. The IoT digital forensic field lacks the necessary tools and guidance to perform digital forensics on these devices. This is mainly attributed to their level of complexity and heterogeneity that is abundant within IoT devices-making the use of a JTAG technique one of the only ways to acquire information stored on an IoT device effectively. Nonetheless, utilizing a JTAG technique can be challenging, especially when having multiple devices with each possibly having its own configuration. To alleviate these issues within the field, we propose the development of an Internet of Things - Forensics Recovery Assistant (IoT-FRA). The IoT-FRA will offer the capabilities of an expert system to assist inexperienced users in performing forensics recovery of IoT devices through a JTAG technique and analysis on the device's capabilities to develop an organized method that will prioritize IoT devices to be analyzed.

Fog Computing was envisioned to solve problems like high latency, mobility, bandwidth, etc. that were introduced by Cloud Computing. Fog Computing has enabled remotely connected IoT devices and sensors to be managed efficiently. Nonetheless, the Fog-Cloud paradigm suffers from various security and privacy related problems. Blockchain ensures security in a trustless way and therefore its applications in various fields are increasing rapidly. In this work, we propose a Fog-Cloud architecture that enables Blockchain to ensure security, scalability, and privacy of remotely connected IoT devices. Furthermore, our proposed architecture also efficiently manages common problems like ever-increasing latency and energy consumption that comes with the integration of Blockchain in Fog-Cloud architecture.

IPsec is widely used for internet security because it offers confidentiality, integrity, and authenticity also protects from replay attacks. IP Security depends on numerous frameworks, organization propels, and cryptographic techniques. IPsec is a heavyweight complex security protocol suite. Because of complex architecture and implementation processes, security implementers prefer TLS. Because of complex implementation, it is impractical to manage over the IoT devices. We propose a simplified and lite version of internet security protocol implemented with only ESP. For encryption, we use AES, RAS-RLP public key cryptography.

Verifying the integrity of IoT data in cloud-based IoT architectures is crucial for building reliable IoT applications. Traditional data integrity verification methods rely on a Trusted Third Party (TTP) that has issues of risk and operational cost by centralization. Distributed Ledger Technology (DLT) has a high potential to verify IoT data integrity and overcome the problems with TTPs. However, the existing DLTs have low transaction throughput, high computational and storage overhead, and are unsuitable for IoT environments, where a massive scale of data is generated. Recently, Directed Acyclic Graph (DAG) based DLTs have been proposed to address the low transaction throughput of linear DLTs. However, the integration of IoT Gateways (GWs) into the peer to peer (P2P) DLT network is challenging because of their low storage and computational capacity. This paper proposes Lightweight and Scalable DAG based distributed ledger for IoT (LSDI) that can work with resource-constrained IoT GWs to provide fast and scalable IoT data integrity verification. LSDI uses two key techniques: Pruning and Clustering, to reduce 1) storage overhead in IoT GWs by removing sufficiently old transactions, and 2) computational overhead of IoT GWs by partitioning a large P2P network into smaller P2P networks. The evaluation results of the proof of concept implementation showed that the proposed LSDI system achieves high transaction throughput and scalability while efficiently managing storage and computation overhead of the IoT GWs.

Internet of Things (IoT) devices use different types of media and protocols to communicate to Internet, but security is compromised since the devices are not using encryption, authentication and integrity. Virtual Private Network of Things (VPNoT) is a new technology designed to create end to end encrypted tunnels for IoT devices, in this case, the VPNoT device is based on OpenVPN that provides confidentiality and integrity, also based on Raspberry Pi as the hardware and Linux as the operating system, both provide connectivity using different types of media to access Internet and network management. IoT devices and sensors can be connected to the VPNoT device so an encrypted tunnel is created to an IoT Server. VPNoT device uses a profile generated by the server, then all devices form a virtual private network (VPN). VPNoT device can act like a router when necessary and this environment works for IPv6 and IPv4 with a great advantage that OpenVPN traverses NAT permitting private IoT servers be accessible to the VPN. The annual cost of the improvement is about \$455 USD per year for 10 VPNoT devices.

The ecosystem of the Internet of Things (IoT) continues growing now and covers more and more fields. One of these areas is the Industrial Internet of Things (IIoT) which integrates sensors and actuators, business applications, open web applications, multimedia security systems, positioning, and tracking systems. Each of these components creates its own data stream and has its own parameters of the probability distribution when transmitting information packets. One such distribution, specific to the TrumpfTruPrint 1000 IIoT system, is the beta distribution. We described issues of the processing of such a data flow by an agent model of the \$5\textbackslashtextbackslashtimes5\$ NoC switch fabric. The concepts of modern telecommunication networks 5G/6G imply the processing of “small” data in the place of their origin, not excluding the centralized processing of big data. This process, which involves the transmission, distribution, and processing of data, involves a large number of devices: routers, multiprocessor systems, multi-core systems, etc. We assumed that the data stream is processed by a device with the network structure, such as NoC, and goes to its built-in router. We carried out a study how the average queues of the \$5\textbackslashtextbackslashtimes5\$ router change with changes in the parameters of a data stream that has a beta distribution.

Effective identification of Internet of things devices in cyberspace is of great significance to the protection of Cyberspace Security. However, there are a large number of such devices in cyberspace, which can not be identified by the existing methods of identifying IoT devices because of the lack of key information such as manufacturer name and device name in the response message. Their existence brings hidden danger to Cyberspace Security. In order to identify the IoT devices with missing key information in these response messages, this paper proposes an IoT device identification method, IoTCatcher. IoTCatcher uses HTTP response message and the structure and style characteristics of HTML document, and based on natural language processing technology and text similarity technology, classifies and compares the IoT devices whose response message lacks key information, so as to generate their device finger information. This paper proves that the recognition precision of IoTCatcher is 95.29%, and the recall rate is 91.01%. Compared with the existing methods, the overall performance is improved by 38.83%.

This paper examines the fast approaching highly secure and content centric data sharing architecture Named Data Networking. The content name plays the key role in NDN. Most of the users are interested only in the content or information and thereby the host centric internet architecture is losing its importance. Different naming conventions and caching strategies used in Named Data Networking based applications have been discussed in this study. The convergence of NDN with the vehicular networks and the ongoing studies in it will make the path to Intelligent Transportation system more optimized and efficient. It describes the future internet and this idea has taken root in most of the upcoming IOT applications which are going to conquer every phase of life. Though it is in its infancy stage of development, NDN will soon take over traditional IP Architecture.

With the rise of Internet of Things (IoT) devices, home network management and security are becoming complex. There is an urgent requirement to make smart home network management more efficient. This work proposes an SDN-based architecture to secure smart home networks through K-Nearest Neighbor (KNN) based device classifications and malicious traffic detection. The efficiency is enhanced by offloading the computation-intensive KNN model to a Field Programmable Gate Arrays (FPGA). Furthermore, we propose a custom KNN solution that exhibits the best performance on an FPGA compared with four alternative KNN instances (i.e., 78% faster than a parallel Bubble Sort-based implementation and 99% faster than three other sorting algorithms). Moreover, with 36,225 training samples, the proposed KNN solution classifies a test query with 95% accuracy in approximately 4 ms on an FPGA compared to 57 seconds on a CPU platform. This highlights the promise of FPGA-based platforms for edge computing applications in the smart home.

The exponential increase in the use of Internet of Things (IoT) devices has been accompanied by a spike in cyberattacks on IoT networks. In this research, we investigate the Bot-IoT dataset with a focus on classifying IoT reconnaissance attacks. Reconnaissance attacks are a foundational step in the cyberattack lifecycle. Our contribution is centered on the building of predictive models with the aid of Random Undersampling (RUS) and ensemble Feature Selection Techniques (FSTs). As far as we are aware, this type of experimentation has never been performed for the Reconnaissance attack category of Bot-IoT. Our work uses the Area Under the Receiver Operating Characteristic Curve (AUC) metric to quantify the performance of a diverse range of classifiers: Light GBM, CatBoost, XGBoost, Random Forest (RF), Logistic Regression (LR), Naive Bayes (NB), Decision Tree (DT), and a Multilayer Perceptron (MLP). For this study, we determined that the best learners are DT and DT-based ensemble classifiers, the best RUS ratio is 1:1 or 1:3, and the best ensemble FST is our ``6 Agree'' technique.

The number of devices in the Internet of Things (IoT) immensely grew in recent years. A frequent challenge in the assurance of the dependability of IoT systems is that components of the system appear as a black box. This paper presents a semi-automatic testing methodology for black-box systems that combines automata learning and fuzz testing. Our testing technique uses stateful fuzzing based on a model that is automatically inferred by automata learning. Applying this technique, we can simultaneously test multiple implementations for unexpected behavior and possible security vulnerabilities.We show the effectiveness of our learning-based fuzzing technique in a case study on the MQTT protocol. MQTT is a widely used publish/subscribe protocol in the IoT. Our case study reveals several inconsistencies between five different MQTT brokers. The found inconsistencies expose possible security vulnerabilities and violations of the MQTT specification.

This paper presents an IoT enabled real time occupancy semantic search system leveraging ETSI defined context information and interface meta model standard- ``Next Generation Service Interface for Linked Data'' (NGSI-LD). It facilitates interoperability, integration and federation of information exchange related to spatial infrastructure among geo-distributed deployed IoT entities, different stakeholders, and process domains. This system, in the presented use case, solves the problem of adhoc booking of meetings in real time through semantic discovery of spatial data and metadata related to room occupancy and thus enables optimum utilization of spatial infrastructure in university campuses. Therefore, the proposed system has the capability to save on effort, cost and productivity in institutional spatial management contexts in the longer run and as well provide a new enriched user experience in smart public buildings. Additionally, the system empowers different stakeholders to plan, forecast and fulfill their spatial infrastructure requirements through semantic data search analysis and real time data driven planning. The initial performance results of the system have shown quick response enabled semantic discovery of data and metadata (textless2 seconds mostly). The proposed system would be a steppingstone towards smart management of spatial infrastructure which offers scalability, federation, vendor agnostic ecosystem, seamless interoperability and integration and security by design. The proposed system provides the fundamental work for its extension and potential in relevant spatial domains of the future.