Consumer IoT devices may suffer malware attacks, and be recruited into botnets or worse. There is evidence that generic advice to device owners to address IoT malware can be successful, but this does not account for emerging forms of persistent IoT malware. Less is known about persistent malware, which resides on persistent storage, requiring targeted manual effort to remove it. This paper presents a field study on the removal of persistent IoT malware by consumers. We partnered with an ISP to contrast remediation times of 760 customers across three malware categories: Windows malware, non-persistent IoT malware, and persistent IoT malware. We also contacted ISP customers identified as having persistent IoT malware on their network-attached storage devices, specifically QSnatch. We found that persistent IoT malware exhibits a mean infection duration many times higher than Windows or Mirai malware; QSnatch has a survival probability of 30% after 180 days, whereby most if not all other observed malware types have been removed. For interviewed device users, QSnatch infections lasted longer, so are apparently more difficult to get rid of, yet participants did not report experiencing difficulty in following notification instructions. We see two factors driving this paradoxical finding: First, most users reported having high technical competency. Also, we found evidence of planning behavior for these tasks and the need for multiple notifications. Our findings demonstrate the critical nature of interventions from outside for persistent malware, since automatic scan of an AV tool or a power cycle, like we are used to for Windows malware and Mirai infections, will not solve persistent IoT malware infections.

Blockchain has the potential to enhance supply chain management systems by providing stronger assurance in transparency and traceability of traded commodities. However, blockchain does not overcome the inherent issues of data trust in IoT enabled supply chains. Recent proposals attempt to tackle these issues by incorporating generic trust and reputation management methods, which do not entirely address the complex challenges of supply chain operations and suffers from significant drawbacks. In this paper, we propose DeTRM, a decentralised trust and reputation management solution for supply chains, which considers complex supply chain operations, such as splitting or merging of product lots, to provide a coherent trust management solution. We resolve data trust by correlating empirical data from adjacent sensor nodes, using which the authenticity of data can be assessed. We design a consortium blockchain, where smart contracts play a significant role in quantifying trustworthiness as a numerical score from different perspectives. A proof-of-concept implementation in Hyperledger Fabric shows that DeTRM is feasible and only incurs relatively small overheads compared to the baseline.

The rapid growth of number of devices that are connected to internet of things (IoT) networks, increases the severity of security problems that need to be solved in order to provide safe environment for network data exchange. The discovery of new vulnerabilities is everyday challenge for security experts and many novel methods for detection and prevention of intrusions are being developed for dealing with this issue. To overcome these shortcomings, artificial intelligence (AI) can be used in development of advanced intrusion detection systems (IDS). This allows such system to adapt to emerging threats, react in real-time and adjust its behavior based on previous experiences. On the other hand, the traffic classification task becomes more difficult because of the large amount of data generated by network systems and high processing demands. For this reason, feature selection (FS) process is applied to reduce data complexity by removing less relevant data for the active classification task and therefore improving algorithm's accuracy. In this work, hybrid version of recently proposed sand cat swarm optimizer algorithm is proposed for feature selection with the goal of increasing performance of extreme learning machine classifier. The performance improvements are demonstrated by validating the proposed method on two well-known datasets - UNSW-NB15 and CICIDS-2017, and comparing the results with those reported for other cutting-edge algorithms that are dealing with the same problems and work in a similar configuration.

Advanced metamorphic malware and ransomware use techniques like obfuscation to alter their internal structure with every attack. Therefore, any signature extracted from such attack, and used to bolster endpoint defense, cannot avert subsequent attacks. Therefore, if even a single such malware intrudes even a single device of an IoT network, it will continue to infect the entire network. Scenarios where an entire network is targeted by a coordinated swarm of such malware is not beyond imagination. Therefore, the IoT era also requires Industry-4.0 grade AI-based solutions against such advanced attacks. But AI-based solutions need a large repository of data extracted from similar attacks to learn robust representations. Whereas, developing a metamorphic malware is a very complex task and requires extreme human ingenuity. Hence, there does not exist abundant metamorphic malware to train AI-based defensive solutions. Also, there is currently no system that could generate enough functionality preserving metamorphic variants of multiple malware to train AI-based defensive systems. Therefore, to this end, we design and develop a novel system, named X-Swarm. X-Swarm uses deep policy-based adversarial reinforcement learning to generate swarm of metamorphic instances of any malware by obfuscating them at the opcode level and ensuring that they could evade even capable, adversarial-attack immune endpoint defense systems.

The Internet of Things (IoT) is advancing technology by creating smart surroundings that make it easier for humans to do their work. This technological advancement not only improves human life and expands economic opportunities, but also allows intruders or attackers to discover and exploit numerous methods in order to circumvent the security of IoT networks. Hence, security and privacy are the key concerns to the IoT networks. It is vital to protect computer and IoT networks from many sorts of anomalies and attacks. Traditional intrusion detection systems (IDS) collect and employ large amounts of data with irrelevant and inappropriate attributes to train machine learning models, resulting in long detection times and a high rate of misclassification. This research presents an advance approach for the design of IDS for IoT networks based on the Particle Swarm Optimization Algorithm (PSO) for feature selection and the Extreme Gradient Boosting (XGB) model for PSO fitness function. The classifier utilized in the intrusion detection process is Random Forest (RF). The IoTID20 is being utilized to evaluate the efficacy and robustness of our suggested strategy. The proposed system attains the following level of accuracy on the IoTID20 dataset for different levels of classification: Binary classification 98 %, multiclass classification 83 %. The results indicate that the proposed framework effectively detects cyber threats and improves the security of IoT networks.

Metaverse technologies depend on various advanced human-computer interaction (HCI) devices to be supported by extended reality (XR) technology. Many new HCI devices are supported by wireless Internet of Things (IoT) networks, where a reliable routing scheme is essential for seamless data trans-mission. Routing Protocol for Low power and Lossy networks (RPL) is a key routing technology used in IPv6-based low power and lossy networks (LLNs). However, in the networks that are configured, such as small wireless devices applying the IEEE 802.15.4 standards, due to the lack of a system that manages the identity (ID) at the center, the maliciously compromised nodes can make fabricated IDs and pretend to be a legitimate node. This behavior is called Sybil attack, which is very difficult to respond to since attackers use multiple fabricated IDs which are legally disguised. In this paper, Sybil attack countermeasures on RPL-based networks published in recent studies are compared and limitations are analyzed through simulation performance analysis.

Security and Controls with Data privacy in Internet of Things (IoT) devices is not only a present and future technology that is projected to connect a multitude of devices, but it is also a critical survival factor for IoT to thrive. As the quantity of communications increases, massive amounts of data are expected to be generated, posing a threat to both physical device and data security. In the Internet of Things architecture, small and low-powered devices are widespread. Due to their complexity, traditional encryption methods and algorithms are computationally expensive, requiring numerous rounds to encrypt and decode, squandering the limited energy available on devices. A simpler cryptographic method, on the other hand, may compromise the intended confidentiality and integrity. This study examines two lightweight encryption algorithms for Android devices: AES and RSA. On the other hand, the traditional AES approach generates preset encryption keys that the sender and receiver share. As a result, the key may be obtained quickly. In this paper, we present an improved AES approach for generating dynamic keys.

Forensic Science comprises a set of technical-scientific knowledge used to solve illicit acts. The increasing use of mobile devices as the main computing platform, in particular smartphones, makes existing information valuable for forensics. However, the blocking mechanisms imposed by the manufacturers and the variety of models and technologies make the task of reconstructing the data for analysis challenging. It is worth mentioning that the conclusion of a case requires more than the simple identification of evidence, as it is extremely important to correlate all the data and sources obtained, to confirm a suspicion or to seek new evidence. This work carries out a systematic review of the literature, identifying the different types of existing image acquisition and the main extraction and encryption methods used in smartphones with the Android operating system.

MQTT is widely adopted by IoT devices because it allows for the most efficient data transfer over a variety of communication lines. The security of MQTT has received increasing attention in recent years, and several studies have demonstrated the configurations of many MQTT brokers are insecure. Adversaries are allowed to exploit vulnerable brokers and publish malicious messages to subscribers. However, little has been done to understanding the security issues on the device side when devices handle unauthorized MQTT messages. To fill this research gap, we propose a fuzzing framework named ShadowFuzzer to find client-side vulnerabilities when processing incoming MQTT messages. To avoiding ethical issues, ShadowFuzzer redirects traffic destined for the actual broker to a shadow broker under the control to monitor vulnerabilities. We select 15 IoT devices communicating with vulnerable brokers and leverage ShadowFuzzer to find vulnerabilities when they parse MQTT messages. For these devices, ShadowFuzzer reports 34 zero-day vulnerabilities in 11 devices. We evaluated the exploitability of these vulnerabilities and received a total of 44,000 USD bug bounty rewards. And 16 CVE/CNVD/CN-NVD numbers have been assigned to us.

In the recent years, we have witnessed quite notable cyber-attacks targeting industrial automation control systems. Upgrading their cyber security is a challenge, not only due to long equipment lifetimes and legacy protocols originally designed to run in air-gapped networks. Even where multiple data sources are available and collection established, data interpretation usable across the different data sources remains a challenge. A modern hydro power plant contains the data sources that range from the classical distributed control systems to newer IoT- based data sources, embedded directly within the plant equipment and deeply integrated in the process. Even abundant collected data does not solve the security problems by itself. The interpretation of data semantics is limited as the data is effectively siloed. In this paper, the relevance of semantic integration of diverse data sources is presented in the context of a hydro power plant. The proposed semantic integration would increase the data interoperability, unlocking the data siloes and thus allowing ingestion of complementary data sources. The principal target of the data interoperability is to support the data-enhanced cyber security in an operational hydro power plant context. Furthermore, the opening of the data siloes would enable additional usage of the existing data sources in a structured semantically enriched form.

As the effects of climate change are becoming more and more evident, the importance of improved situation awareness is also gaining more attention, both in the context of preventive environmental monitoring and in the context of acute crisis response. One important aspect of situation awareness is the correct and thorough monitoring of air pollutants. The monitoring is threatened by sensor faults, power or network failures, or other hazards leading to missing or incorrect data transmission. For this reason, in this work we propose two complementary approaches for predicting missing sensor data and a combined technique for detecting outliers. The proposed solution can enhance the performance of low-cost sensor systems, closing the gap of missing measurements due to network unavailability, detecting drift and outliers thus paving the way to its use as an alert system for reportable events. The techniques have been deployed and tested also in a low power microcontroller environment, verifying the suitability of such a computing power to perform the inference locally, leading the way to an edge implementation of a virtual sensor digital twin.

Blockchain and artificial intelligence are two technologies that, when combined, have the ability to help each other realize their full potential. Blockchains can guarantee the accessibility and consistent admittance to integrity safeguarded big data indexes from numerous areas, allowing AI systems to learn more effectively and thoroughly. Similarly, artificial intelligence (AI) can be used to offer new consensus processes, and hence new methods of engaging with Blockchains. When it comes to sensitive data, such as corporate, healthcare, and financial data, various security and privacy problems arise that must be properly evaluated. Interaction with Blockchains is vulnerable to data credibility checks, transactional data leakages, data protection rules compliance, on-chain data privacy, and malicious smart contracts. To solve these issues, new security and privacy-preserving technologies are being developed. AI-based blockchain data processing, either based on AI or used to defend AI-based blockchain data processing, is emerging to simplify the integration of these two cutting-edge technologies.

The integration of the Internet-of-Vehicles (IoV) and fog computing benefits from cooperative computing and analysis of environmental data while avoiding network congestion and latency. However, when private data is shared across fog nodes or the cloud, there exist privacy issues that limit the effectiveness of IoV systems, putting drivers' safety at risk. To address this problem, we propose a framework called PPIoV, which is based on Federated Learning (FL) and Blockchain technologies to preserve the privacy of vehicles in IoV.Typical machine learning methods are not well suited for distributed and highly dynamic systems like IoV since they train on data with local features. Therefore, we use FL to train the global model while preserving privacy. Also, our approach is built on a scheme that evaluates the reliability of vehicles participating in the FL training process. Moreover, PPIoV is built on blockchain to establish trust across multiple communication nodes. For example, when the local learned model updates from the vehicles and fog nodes are communicated with the cloud to update the global learned model, all transactions take place on the blockchain. The outcome of our experimental study shows that the proposed method improves the global model's accuracy as a result of allowing reputed vehicles to update the global model.

Nowadays, IoT networks and devices exist in our everyday life, capturing and carrying unlimited data. However, increasing penetration of connected systems and devices implies rising threats for cybersecurity with IoT systems suffering from network attacks. Artificial Intelligence (AI) and Machine Learning take advantage of huge volumes of IoT network logs to enhance their cybersecurity in IoT. However, these data are often desired to remain private. Federated Learning (FL) provides a potential solution which enables collaborative training of attack detection model among a set of federated nodes, while preserving privacy as data remain local and are never disclosed or processed on central servers. While FL is resilient and resolves, up to a point, data governance and ownership issues, it does not guarantee security and privacy by design. Adversaries could interfere with the communication process, expose network vulnerabilities, and manipulate the training process, thus affecting the performance of the trained model. In this paper, we present a federated learning model which can successfully detect network attacks in IoT systems. Moreover, we evaluate its performance under various settings of differential privacy as a privacy preserving technique and configurations of the participating nodes. We prove that the proposed model protects the privacy without actually compromising performance. Our model realizes a limited performance impact of only ∼ 7% less testing accuracy compared to the baseline while simultaneously guaranteeing security and applicability.

Intrusion Detection System (IDS) is one of the applications to detect intrusions in the network. IDS aims to detect any malicious activities that protect the computer networks from unknown persons or users called attackers. Network security is one of the significant tasks that should provide secure data transfer. Virtualization of networks becomes more complex for IoT technology. Deep Learning (DL) is most widely used by many networks to detect the complex patterns. This is very suitable approaches for detecting the malicious nodes or attacks. Software-Defined Network (SDN) is the default virtualization computer network. Attackers are developing new technology to attack the networks. Many authors are trying to develop new technologies to attack the networks. To overcome these attacks new protocols are required to prevent these attacks. In this paper, a unique deep intrusion detection approach (UDIDA) is developed to detect the attacks in SDN. Performance shows that the proposed approach is achieved more accuracy than existing approaches.

DDoS is a major issue in network security and a threat to service providers that renders a service inaccessible for a period of time. The number of Internet of Things (IoT) devices has developed rapidly. Nevertheless, it is proven that security on these devices is frequently disregarded. Many detection methods exist and are mostly focused on Machine Learning. However, the best method has not been defined yet. The aim of this paper is to find the optimal volumetric DDoS attack detection method by first comparing different existing machine learning methods, and second, by building an adaptive lightweight heuristics model relying on few traffic attributes and simple DDoS detection rules. With this new simple model, our goal is to decrease the classification time. Finally, we compare machine learning methods with our adaptive new heuristics method which shows promising results both on the accuracy and performance levels.

With the rise of IoT applications, about 20.4 billion devices will be online in 2020, and that number will rise to 75 billion a month by 2025. Different sensors in IoT devices let them get and process data remotely and in real time. Sensors give them information that helps them make smart decisions and manage IoT environments well. IoT Security is one of the most important things to think about when you're developing, implementing, and deploying IoT platforms. People who use the Internet of Things (IoT) say that it allows people to communicate, monitor, and control automated devices from afar. This paper shows how to use Deep learning and machine learning to make an IDS that can be used on IoT platforms as a service. In the proposed method, a cnn mapped the features, and a random forest classifies normal and attack classes. In the end, the proposed method made a big difference in all performance parameters. Its average performance metrics have gone up 5% to 6%.

For the Internet of things (IoT) secure data aggregation issues, data privacy-preserving and limited computation ability and energy of nodes should be tradeoff. Based on analyzing the pros-and-cons of current works, a low energy- consuming secure data aggregation method (LCSDA) was proposed. This method uses shortest path principle to choose neighbor nodes and generates the data aggregation paths in the cluster based on prim minimum spanning tree algorithm. Simulation results show that this method could effectively cut down energy consumption and reduce the probability of cluster head node being captured, in the same time preserving data privacy.

The proliferation of linked devices in decisive infrastructure fields including health care and the electric grid is transforming public perceptions of critical infrastructure. As the world grows more mobile and connected, as well as as the Internet of Things (IoT) expands, the growing interconnectivity of new critical sectors is being fuelled. Interruptions in any of these areas can have ramifications across numerous sectors and potentially the world. Crucial industries are critical to contemporary civilization. In today's hyper-connected world, critical infrastructure is more vulnerable than ever to cyber assaults, whether they are state-sponsored, carried out by criminal organizations, or carried out by individuals. In a world where more and more gadgets are interconnected, hackers have more and more entry points via which they may damage critical infrastructure. Significant modifications to an organization's main technological systems have created a new threat surface. The study's goal is to raise awareness about the challenges of protecting digital infrastructure in the future while it is still in development. Fog architecture is designed based on functionality once the infrastructure that creates large data has been established. There's also an in-depth look of fog-enabled IoT network security requirements. The next section examines the security issues connected with fog computing, as well as the privacy and trust issues raised by fog-enabled Internet of Things (IoT). Block chain is also examined to see how it may help address IoT security problems, as well as the complimentary interrelationships between block-chain and fog computing. Additionally, Formalizes big data security goal and scope, develops taxonomy for identifying risks to fog-based Internet of Things systems, compares current development contributions to security service standards, and proposes interesting study areas for future studies, all within this framework

The Internet of Things devices is rapidly becoming widespread, as are IoT services. Their achievement has not gone unnoticed, as threats as well as attacks towards IoT devices as well as services continue to grow. Cyber attacks are not unique to IoT, however as IoT becomes more ingrained in our lives as well as communities, it is imperative to step up as well as take cyber defense seriously. As a result, there is a genuine need to protect IoT, which necessitates a thorough understanding of the dangers and attacks against IoT infrastructure. The purpose of this study is to define threat types, as well as to assess and characterize intrusions and assaults against IoT devices as well as services

In recent years, the need for seamless connectivity has increased across various network platforms with demands coming from industries, home, mobile, transportation and office networks. The 5th generation (5G) network is being deployed to meet such demand of high-speed seamless network device connections. The seamless connectivity 5G provides could be a security threat allowing attacks such as distributed denial of service (DDoS) because attackers might have easy access into the network infrastructure and higher bandwidth to enhance the effects of the attack. The aim of this research is to provide a security solution for 5G technology to DDoS attacks by managing the response to threats posed by DDoS. Deploying a security policy language which is reactive and event-oriented fits into a flexible, efficient, and lightweight security approach. A policy in our language consists of an event whose occurrence triggers a policy rule where one or more actions are taken.

With the growing number of IoT applications and devices, IoT security breaches are a dangerous reality. Cost pressure and complexity of security tests for embedded systems and networked infrastructure are often the excuse for skipping them completely. In our paper we introduce SecLab security test lab to overcome that problem. Based on a flexible and lightweight architecture, SecLab allows developers and IoT security specialists to harden their systems with a low entry hurdle. The open architecture supports the reuse of existing external security test libraries and scalability for the assessment of complex IoT Systems. A reference implementation of security tests in a realistic IoT application scenario proves the approach.

The electrical grid connects all the generating stations to supply uninterruptible power to the consumers. With the advent of technology, smart sensors and communication are integrated with the existing grid to behave like a smart system. This smart grid is a two-way communication that connects the consumers and producers. It is a connected smart network that integrates electricity generation, transmission, substation, distribution, etc. In this smart grid, clean, reliable power with a high-efficiency rate of transmission is available. In this paper, a highly efficient smart management system of a smart grid with overall protection is proposed. This management system checks and monitors the parameters periodically. This future technology also develops a smart transformer with ac and dc compatibility, for self-protection and for the healing process.

In order to solve the problem of high data collision probability, high access delay and high-power consumption in random access process of power Internet of Things, an access scheme for large-scale micro-power wireless sensors based on slot-scheduling and hybrid mode is presented. This scheme divides time into different slots and designs a slot-scheduling algorithm according to network workload and power consumption. Sensors with different service priorities are arranged in different time slots for competitive access, using appropriate random-access mechanism. And rationally arrange the number of time slots and competing end-devices in different time slots. This scheme is able to meet the timeliness requirements of different services and reduce the overall network power consumption when dealing with random access scenarios of large-scale micro-power wireless sensor network. Based on the simulation results of actual scenarios, this access scheme can effectively reduce the overall power consumption of the network, and the high priority services can meet the timeliness requirements on the premise of lower power consumption, while the low priority services can further reduce power consumption.

For some countries around the world, meeting demand is a serious concern. Power supply market is increasingly increasing, posing a big challenge for various countries throughout the world. The increasing expansion in the market for power needs upgrading system dependability to increase the smart grid's resilience. This smart electric grid has a sensor that analyses grid power availability and sends regular updates to the organisation. The internet is currently being utilized to monitor processes and place orders for running variables from faraway places. A large number of scanners have been used to activate electrical equipment for domestic robotics for a long period in the last several days. Conversely, if it is not correctly implemented, it will have a negative impact on cost-effectiveness as well as productivity. For something like a long time, home automation has relied on a large number of sensor nodes to control electrical equipment. Since there are so many detectors, this isn't cost-effective. In this article, develop and accept a wireless communication component and a management system suitable for managing independent efficient network units from voltage rises and voltage control technologies in simultaneous analyzing system reliability in this study. This research paper has considered secondary method to collect relevant and in-depth data related to the wireless sensor network and its usage in smart grid monitoring.