Biblio

A cyber-physical system (CPS) is a term that implements mainly three parts, Physical elements, communication networks, and control systems. Currently, CPS includes the Internet of Things (IoT), Internet of Vehicles (IoV), and many other systems. These systems face many security challenges and different types of attacks, such as Jamming, DDoS.CPS attacks tend to be much smarter and more dynamic; thus, it needs defending strategies that can handle this level of intelligence and dynamicity. Last few years, many researchers use machine learning as a base solution to many CPS security issues. This paper provides a survey of the recent works that utilized the Q-Learning algorithm in terms of security enabling and privacy-preserving. Different adoption of Q-Learning for security and defending strategies are studied. The state-of-the-art of Q-learning and CPS systems are classified and analyzed according to their attacks, domain, supported techniques, and details of the Q-Learning algorithm. Finally, this work highlight The future research trends toward efficient utilization of Q-learning and deep Q-learning on CPS security.

Recently, the Internet of Things (IoT) is growing rapidly. IoT sensors are attached to various devices, and information is detected, collected and utilized through various wired and wireless communication environments. As the IoT is used in various places, IoT devices face a variety of malicious attacks such as MITM and reverse engineering. To prevent these, encryption is required for device-to-device communication, and keys required for encryption must be properly managed. We propose a scheme to generate seed needed for key generation and a scheme to manage the public key using blockchain.

Surveillance systems are widely used in airports, streets, banks, military areas, borders, hospitals, and schools. There are two types of surveillance systems which are real-time systems and offline surveillance systems. Usually, security people track videos on time in monitoring rooms to find out abnormal human activities. Real-time human tracking from videos is very expensive especially in airports, borders, and streets due to the huge number of surveillance cameras. There are a lot of research works have been done for automated surveillance systems. In this paper, we presented a new surveillance system to recognize human activities from several cameras using machine learning algorithms. Sequences of images are collected from cameras using the internet of things technology from indoor or outdoor areas. A feature vector is created for each recognized moving object, then machine learning algorithms are applied to extract moving object activities. The proposed abnormal event detection system gives very promising results which are more than 96% accuracy in Multilayer Perceptron, Iterative Classifier Optimizer, and Random Forest algorithms.

The author considers the Internet of Things security problems, namely, the organization of secure access control when using the MQTT protocol. Security mechanisms and methods that are employed or supported by the MQTT protocol have been analyzed. Thus, the protocol employs authentication by the login and password. In addition, it supports cryptographic processing over transferring data via the TLS protocol. Third-party services on OAuth protocol can be used for authentication. The authorization takes place by configuring the ACL-files or via third-party services and databases. The author suggests a device discretionary access control model of machine-to-machine interaction under the MQTT protocol, which is based on the HRU-model. The model entails six operators: the addition and deletion of a subject, the addition and deletion of an object, the addition and deletion of access privileges. The access control model is presented in a form of an access matrix and has three types of privileges: read, write, ownership. The model is composed in a way that makes it compatible with the protocol of a widespread version v3.1.1. The available types of messages in the MQTT protocol allow for the adjustment of access privileges. The author considered an algorithm with such a service data unit build that the unit could easily be distinguished in the message body. The implementation of the suggested model will lead to the minimization of administrator's involvement due to the possibility for devices to determine access privileges to the information resource without human involvement. The author suggests recommendations for security policies, when organizing an informational exchange in accordance with the MQTT protocol.

Securing the supply chain of information and communications technology (ICT) has recently emerged as a critical concern for national security and integrity. With the proliferation of Internet of Things (IoT) devices and their increasing role in controlling real world infrastructure, there is a need to analyze risks in networked systems beyond established security analyses. Existing methods in literature typically leverage attack and fault trees to analyze malicious activity and its impact. In this paper, we develop RIoTS, a security risk assessment framework borrowing from system reliability theory to incorporate the supply chain. We also analyze the impact of grouping within suppliers that may pose hidden risks to the systems from malicious supply chain actors. The results show that the proposed analysis is able to reveal hidden threats posed to the IoT ecosystem from potential supplier collusion.

Technological progress in cloud networking, 5G networks, and the IoT (Internet of Things) are remarkable. In addition, demands for flexible construction of SoEs (Systems on Engagement) for various type of businesses are increasing. In such environments, dynamic changes of network rules, such as access control (AC) or packet forwarding, are required to ensure function and security in networks. On the other hand, it is becoming increasingly difficult to grasp the exact situation in such networks by utilizing current well-known network verification technologies since a huge number of network rules are complexly intertwined. To mitigate these issues, we have proposed a scalable network verification approach utilizing the concept of "Packet Equivalence Class (PEC)," which enable precise network function verification by strictly recognizing the impact range of each network rule. However, this approach is still not scalable for very large-scale networks which consist of tens of thousands of routers. In this paper, we enhanced our impact range detection algorithm for practical large-scale networks. Through evaluation in the network with more than 80,000 AC rules, we confirmed that our enhanced algorithm can achieve precise impact range detection in under 600 seconds.

The Internet of Things is progressively turning into a pervasive computing service, needing enormous volumes of data storage and processing. However, due to the distinctive properties of resource constraints, self-organization, and short-range communication in Internet of Things (IoT), it always adopts to cloud for outsourced storage and computation. This integration of IoT with cloud has a row of unfamiliar security challenges for the data at rest. Cloud computing delivers highly scalable and flexible computing and storage resources on pay-per-use policy. Cloud computing services for computation and storage are getting increasingly popular and many organizations are now moving their data from in-house data centers to the Cloud Storage Providers (CSPs). Time varying workload and data intensive IoT applications are vulnerable to encounter challenges while using cloud computing services. Additionally, the encryption techniques and third-party auditors to maintain data integrity are still in their developing stage and therefore the data at rest is still a concern for IoT applications. In this paper, we perform an analysis study to investigate the challenges and strategies adapted by Cloud Computing to facilitate a safe transition of IoT applications to the Cloud.

Security is the basic topic for the normal operation of the power Internet of Things, and its growing scale determines the trend of dynamic deployment and flexible expansion in the future to meet the ever-changing needs. While large-scale networks have a high cost of hardware resources, so the security protection of the ubiquitous power Internet of Things must be lightweight. In this paper, we propose to build a platform of power Internet of things based on SDN (Software Defined Network) technology and extend the openflow protocol by adding some types of actions and meters to achieve the purpose of on-demand monitoring, dynamic defense and flexible response. To achieve the purpose of lightweight protection, we take advantage of DFI(Deep Flow Inspection) technology to collect and analyze traffic in the Internet of Things, and form a security prevention and control strategy model suitable for the power Internet of Things, without in-depth detection of payload and without the influence of ciphertext.

With the improvement of people's living standards, more and more network users access the network, including a large number of infrastructure, these devices constitute the Internet of things(IoT). With the rapid expansion of devices in the IoT, the data transmission between the IoT has become more complex, and the security issues are facing greater challenges. SDN as a mature network architecture, its security has been affirmed by the industry, it separates the data layer from the control layer, thus greatly improving the security of the network. In this paper, we apply the SDN to the IoT, and propose a IoT network architecture based on SDN. In this architecture, we not only make use of the security features of SDN, but also deploy different security modules in each layer of SDN to integrate, analyze and plan various data through the IoT, which undoubtedly improves the security performance of the network. In the end, we give a comprehensive introduction to the system and verify its performance.

The Internet of Things (IoT) integrates the Internet and electronic devices belonging to different domains, such as smart home automation, industrial processes, military applications, health, and environmental monitoring. Usually, IoT devices have limited resources and Low Power and Lossy Networks (LLNs) are being used to interconnect such devices. Routing Protocol for Low-Power and Lossy Networks (RPL) is one of the preferred routing protocols for this type of network, since it was specially developed for LLNs, also known as IPv6 over Low-Power Wireless Personal Area Networks (6LoWPAN). In this paper the most well-known routing attacks against 6LoWPAN networks were studied and implemented through simulation, conducting a behavioral analysis of network components (resources, topology, and data traffic) under attack condition. In order to achieve a better understanding on how attacks in 6LoWPAN work, we first conducted a study on 6LoWPAN networks and RPL protocol functioning. Furthermore, we also studied a series of well-known routing attacks against this type of Wireless Sensor Networks and these attacks were then simulated using Cooja simulator provided by Contiki operating system. The results obtained after the simulations are discussed along with other previous researches. This analysis may be of real interest when it comes to identify indicators of compromise for each type of attack and appropriate countermeasures for prevention and detection of these attacks.

Inter-networked control systems make smart buildings increasingly efficient but can lead to severe operational disruptions and infrastructure damage. It is vital the security state of smart buildings is properly assessed so that thorough and cost effective risk management can be established. This paper uniquely reports on an actual risk assessment performed in 2018 on one of the world's most densely monitored, state-of-the-art, smart buildings. From our observations, we suggest that current practice may be inadequate due to a number of challenges and deficiencies, including the lack of a recognised smart building risk assessment methodology. As a result, the security posture of many smart buildings may not be as robust as their risk assessments suggest. Crucially, we highlight a number of key recommendations for a more comprehensive risk assessment process for smart buildings. As a whole, we believe this practical experience report will be of interest to a range of smart building stakeholders.

With large scale generation and exchange of data between IoT devices and constrained IoT security to protect data communication, it becomes easy for attackers to compromise data routes. In IoT networks, IPv6 Routing Protocol is the de facto routing protocol for Low Power and Lossy Networks (RPL). RPL offers limited security against several RPL-specific and WSN-inherited attacks in IoT applications. Additionally, IoT devices are limited in memory, processing, and power to operate properly using the traditional Internet and routing security solutions. Several mitigation schemes for the security of IoT networks and routing, have been proposed including Machine Learning-based, IDS-based, and Trust-based approaches. In existing trust-based methods, mobility of nodes is not considered at all or its insufficient for mobile sink nodes, specifically for security against RPL attacks. This research work proposes a conceptual design, named SMTrust, for security of routing protocol in IoT, considering the mobility-based trust metrics. The proposed solution intends to provide defense against popular RPL attacks, for example, Blackhole, Greyhole, Rank, Version Number attacks, etc. We believe that SMTrust shall provide better network performance for attacks detection accuracy, mobility and scalability as compared to existing trust models, such as, DCTM-RPL and SecTrust-RPL. The novelty of our solution is that it considers the mobility metrics of the sensor nodes as well as the sink nodes, which has not been addressed by the existing models. This consideration makes it suitable for mobile IoT environment. The proposed design of SMTrust, as secure routing protocol, when embedded in RPL, shall ensure confidentiality, integrity, and availability among the sensor nodes during routing process in IoT communication and networks.

An Internet of Things (IoT) architecture comprised of cloud, fog and resource constrained IoT end devices. The exponential development of IoT has increased the processing and footprint overhead in IoT end devices. All the components of IoT end devices that establish Chain of Trust (CoT) to ensure security are termed as Trusted Computing Base (TCB). The increased overhead in the IoT end device has increased the demand to increase the size of TCB surface area hence increases complexity of TCB surface area and also the increased the visibility of TCB surface area to the external world made the IoT end devices architecture over-architectured and unsecured. The TCB surface area minimization that has been remained unfocused reduces the complexity of TCB surface area and visibility of TCB components to the external un-trusted world hence ensures security in terms of confidentiality, integrity, authenticity (CIA) at the IoT end devices. The TCB minimization thus will convert the over-architectured IoT end device into lightweight and secured architecture highly desired for resource constrained IoT end devices. In this paper we review the IoT end device architectures proposed in the recent past and concluded that these architectures of resource constrained IoT end devices are over-architectured due to larger TCB and ignored bugs and vulnerabilities in TCB hence un-secured. We propose the Novel levelled architecture with TCB minimization by replacing oversized hypervisor with lightweight Micro(μ)-hypervisor i.e. μ-visor and transferring μ-hypervisor based virtualization over fog node for light weight and secured IoT End device architecture. The bug free TCB components confirm stable CoT for guaranteed CIA resulting into robust Trusted Execution Environment (TEE) hence secured IoT end device architecture. Thus the proposed resulting architecture is secured with minimized SRAM and flash memory combined footprint 39.05% of the total available memory per device. In this paper we review the IoT end device architectures proposed in the recent past and concluded that these architectures of resource constrained IoT end devices are over-architectured due to larger TCB and ignored bugs and vulnerabilities in TCB hence un-secured. We propose the Novel levelled architecture with TCB minimization by replacing oversized hypervisor with lightweight Micro(μ)-hypervisor i.e. μ-visor and transferring μ-hypervisor based virtualization over fog node for light weight and secured IoT End device architecture. The bug free TCB components confirm stable CoT for guaranteed CIA resulting into robust Trusted Execution Environment (TEE) hence secured IoT end device architecture. Thus the proposed resulting architecture is secured with minimized SRAM and flash memory combined footprint 39.05% of the total available memory per device.

Wireless sensor network (WSN) is a wireless self-organizing multi-hop network that can sense and collect the information of the monitored environment through a certain number of sensor nodes which deployed in a certain area and transmit the collected information to the client. Due to the limited power and data capacity stored by the micro sensor, it is weak in communication with other nodes, data storage and calculation, and is very vulnerable to attack and harm to the entire network. The Sybil attack is a classic example. Sybil attack refers to the attack in which malicious nodes forge multiple node identities to participate in network operation. Malicious attackers can forge multiple node identities to participate in data forwarding. So that the data obtained by the end user without any use value. In this paper, we propose a three-tier detection scheme for the Sybil node in the severe environment. Every sensor node will determine whether they are Sybil nodes through the first-level and second-level high-energy node detection. Finally, the base station determines whether the Sybil node detected by the first two stages is true Sybil node. The simulation results show that our proposed scheme significantly improves network lifetime, and effectively improves the accuracy of Sybil node detection.

Conversational agents (CAs), often referred to as chatbots, are being widely deployed within existing commercial frameworks and online service websites. As society moves further into incorporating data rich systems, like the internet of things (IoT), into daily life, it is expected that conversational agents will take on an increasingly important role to help users manage these complex systems. In this, the concept of personality is becoming increasingly important, as we seek for more human-friendly ways to interact with these CAs. In this work a conceptual framework is proposed that considers how existing standard psychological and persona models could be mapped to different kinds of CA functionality outside of strictly dialogue. As CAs become more diverse in their abilities, and more integrated with different kinds of systems, it is important to consider how function can be impacted by the design of agent personality, whether intentionally designed or not. Based on this framework, derived archetype classes of CAs are presented as starting points that can hopefully aid designers, developers, and the curious, into thinking about how to work toward better CA personality development.

The Internet of Things (IoT) is a system paradigm that recently introduced, which includes different smart devices and applications, especially, in smart cities, e.g.; manufacturing, homes, and offices. To improve their awareness capabilities, it is attractive to add more sensors to their framework. In this paper, we propose adding a new sensor as a wearable sensor connected wirelessly with a neural network located on the base station (WSNB). WSNB enables the added sensor to refine their labels through active learning. The new sensors achieve an average accuracy of 93.81%, which is 4.5% higher than the existing method, removing human support and increasing the life cycle for the sensors by using neural network approach in the base station.

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.

Blockchain technology is the cornerstone of digital trust and systems' decentralization. The necessity of eliminating trust in computing systems has triggered researchers to investigate the applicability of Blockchain to decentralize the conventional security models. Specifically, researchers continuously aim at minimizing trust in the well-known Public Key Infrastructure (PKI) model which currently requires a trusted Certificate Authority (CA) to sign digital certificates. Recently, the Automated Certificate Management Environment (ACME) was standardized as a certificate issuance automation protocol. It minimizes the human interaction by enabling certificates to be automatically requested, verified, and installed on servers. ACME only solved the automation issue, but the trust concerns remain as a trusted CA is required. In this paper we propose decentralizing the ACME protocol by using the Blockchain technology to enhance the current trust issues of the existing PKI model and to eliminate the need for a trusted CA. The system was implemented and tested on Ethereum Blockchain, and the results showed that the system is feasible in terms of cost, speed, and applicability on a wide range of devices including Internet of Things (IoT) devices.

In order to the development of IoT, IETF developed a standard named 6LoWPAN for increase the usage of IPv6 to the tiny and smart objects with low power. Generally, the 6LoWPAN radio link needs end to end (e2e) security for its IPv6 communication process. 6LoWPAN requires light weight variant of security solutions in IPSec. A new security approach of 6LoWPAN at adaptation layer to provide e2e security with light weight IPSec. The existing security protocol IPsec is not suitable for its 6LoWPAN IoT environment because it has heavy restrictions on memory, power, duty cycle, additional overhead transmission. The IPSec had packet overhead problem due to share the secret key between two communicating peers by IKE (Internet Key Exchange) protocol. Hence the existing security protocol IPSec solutions are not suitable for lightweight-based security need in 6LoWPAN IoT. This paper describes 6LowPSec protocol with AES-CCM (Cipher block chaining Message authentication code with Counter mode) cryptographic algorithm with key size of 128 bits with minimum power consumption and duty cycle.

Integrated with various electronic control units (ECUs), vehicles are becoming more intelligent with the assistance of essential connections. However, the interaction with the outside world raises great concerns on cyber-attacks. As a main standard for in-vehicle network, Controller Area Network (CAN) does not have any built-in security mechanisms to guarantee a secure communication. This increases risks of denial of service, remote control attacks by an attacker, posing serious threats to underlying vehicles, property and human lives. As a result, it is urgent to develop an effective in-vehicle network intrusion detection system (IDS) for better security. In this paper, we propose a Feature-based Sliding Window (FSW) to extract the feature of CAN Data Field and CAN IDs. Then we construct a convolutional encoder network (CEN) to detect network intrusion of CAN networks. The proposed FSW-CEN method is evaluated on real-world datasets. The experimental results show that compared to traditional data processing methods and convolutional neural networks, our method is able to detect attacks with a higher accuracy in terms of detection accuracy and false negative rate.

With the expansion of Internet of Things (IoT) that relies on heterogeneous, dynamic, and massively deployed devices, device management (DM) (i.e., remote administration such as firmware update, configuration, troubleshooting and tracking) is required for proper quality of service and user experience, deployment of new functions, bug corrections and security patches distribution. Existing industrial DM platforms and approaches do not suit IoT devices and are already showing their limits with a few static home devices (e.g., routers, TV Decoders). Indeed, undetected buggy firmware deployment and manual target device identification are common issues in existing systems. Besides, these platforms are manually operated by experts (e.g., system administrators) and require extensive knowledge and skills. Such approaches cannot be applied on massive and diverse devices forming the IoT. To tackle these issues, our work in an industrial research context proposes to apply autonomic computing to DM platforms operation and impact tracking. Specifically, our contribution relies on automated device targeting (i.e., aiming only suitable devices) and impact-aware DM (i.e., error and anomalies detection preceding patch generalization on all suitable devices of a given fleet). Our solution is composed of three coordinated autonomic loops and allows more accurate and faster irregularity diagnosis, vertical scaling along with simpler IoT DM platform administration. For experimental validation, we developed a prototype that demonstrates encouraging results compared to simulated legacy telecommunication operator approaches (namely Orange).

The generation, transmission, distribution, and storage of electric power is becoming increasingly decentralized. Advances in Distributed Energy Resources (DERs) are rapidly changing the nature of the power grid. Moreover, the accommodation of these new technologies by the legacy grid requires that an increasing number of devices be Internet connected so as to allow for sensor and actuator information to be collected, transmitted, and processed. With the wide adoption of the Internet of Things (IoT), the cybersecurity vulnerabilities of smart grid devices that can potentially affect the stability, reliability, and resilience of the power grid need to be carefully examined and addressed. This is especially true in situations in which smart grid devices are deployed with default configurations or without reasonable protections against malicious activities. While much work has been done to characterize the vulnerabilities associated with Supervisory Control and Data Acquisition (SCADA) and Industrial Control System (ICS) devices, this paper demonstrates that similar vulnerabilities associated with the newer class of IoT smart grid devices are becoming a concern. Specifically, this paper first performs an evaluation of such devices using the Shodan platform and text processing techniques to analyze a potential vulnerability involving the lack of password protection. This work further explores several Shodan search terms that can be used to identify additional smart grid components that can be evaluated in terms of cybersecurity vulnerabilities. Finally, this paper presents recommendations for the more secure deployment of such smart grid devices.

Existing cyber security solutions have been basically developed using knowledge-based models that often cannot trigger new cyber-attack families. With the boom of Artificial Intelligence (AI), especially Deep Learning (DL) algorithms, those security solutions have been plugged-in with AI models to discover, trace, mitigate or respond to incidents of new security events. The algorithms demand a large number of heterogeneous data sources to train and validate new security systems. This paper presents the description of new datasets, the so-called ToNİoT, which involve federated data sources collected from Telemetry datasets of IoT services, Operating system datasets of Windows and Linux, and datasets of Network traffic. The paper introduces the testbed and description of TONİoT datasets for Windows operating systems. The testbed was implemented in three layers: edge, fog and cloud. The edge layer involves IoT and network devices, the fog layer contains virtual machines and gateways, and the cloud layer involves cloud services, such as data analytics, linked to the other two layers. These layers were dynamically managed using the platforms of software-Defined Network (SDN) and Network-Function Virtualization (NFV) using the VMware NSX and vCloud NFV platform. The Windows datasets were collected from audit traces of memories, processors, networks, processes and hard disks. The datasets would be used to evaluate various AI-based cyber security solutions, including intrusion detection, threat intelligence and hunting, privacy preservation and digital forensics. This is because the datasets have a wide range of recent normal and attack features and observations, as well as authentic ground truth events. The datasets can be publicly accessed from this link [1].

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.

Internet of Things (IoT), commonly referred to a physical object connected to network, refers to a paradigm in information technology integrating the advances in terms of sensing, computation and communication to improve the service in daily life. This physical object consists of sensors and actuators that are capable of changing the data to offer the improvement of service quality in daily life. When a data exchange occurs, the exchanged data become sensitive; making them vulnerable to any security attacks, one of which, for example, is Sybil attack. This paper aimed to propose a method of trustworthiness management based upon the authentication and trust value. Once performing the test on three scenarios, the system was found to be capable of detecting the Sybil attack rapidly and accurately. The average of time to detect the Sybil attacks was 9.3287 seconds and the average of time required to detect the intruder object in the system was 18.1029 seconds. The accuracy resulted in each scenario was found 100% indicating that the detection by the system to Sybil attack was 100% accurate.