The spread of Internet of Things (IoT) devices in our homes, healthcare, industries etc. are more easily infiltrated than desktop computers have resulted in a surge in botnet attacks based on IoT devices, which may jeopardize the IoT security. Hence, there is a need to detect these attacks and mitigate the damage. Existing systems rely on supervised learning-based intrusion detection methods, which require a large labelled data set to achieve high accuracy. Botnets are onerous to detect because of stealthy command & control protocols and large amount of network traffic and hence obtaining a large labelled data set is also difficult. Due to unlabeled Network traffic, the supervised classification techniques may not be used directly to sort out the botnet that is responsible for the attack. To overcome this limitation, a semi-supervised Deep Learning (DL) approach is proposed which uses Semi-supervised GAN (SGAN) for IoT botnet detection on N-BaIoT dataset which contains "Bashlite" and "Mirai" attacks along with their sub attacks. The results have been compared with the state-of-the-art supervised solutions and found efficient in terms of better accuracy which is 99.89% in binary classification and 59% in multi classification on larger dataset, faster and reliable model for IoT Botnet detection.

This paper dives into the growing world of IoT botnets that have taken the world by storm in the past five years. Though alone an IP camera cannot produce enough traffic to be considered a DDoS. But a botnet that has over 150,000 connected IP cameras can generate as much as 1 Tbps in traffic. Botnets catch many by surprise because their attacks and infections may not be as apparent as a DDoS, some other cases include using these cameras and printers for extracting information or quietly mine cryptocurrency at the IoT device owner's expense. Here we analyze damages on IoT hacking and define botnet architecture. An overview of Mirai botnet and cryptojacking provided to better understand the IoT botnets.

The ubiquitous nature of the Internet of Things (IoT) devices and their wide-scale deployment have remarkably attracted hackers to exploit weakly-configured and vulnerable devices, allowing them to form large IoT botnets and launch unprecedented attacks. Modeling the behavior of IoT botnets leads to a better understanding of their spreading mechanisms and the state of the network at different levels of the attack. In this paper, we propose a generic model to capture the behavior of IoT botnets. The proposed model uses Markov Chains to study the botnet behavior. Discrete Event System Specifications environment is used to simulate the proposed model.

Chaos is an interesting phenomenon for nonlinear systems that emerges due to its complex and unpredictable behavior. With the escalated use of low-powered edge-compute devices, data security at the edge develops the need for security in communication. The characteristic that Chaos synchronizes over time for two different chaotic systems with their own unique initial conditions, is the base for chaos implementation in communication. This paper proposes an encryption architecture suitable for communication of on-chip sensors to provide a POC (proof of concept) with security encrypted on the same chip using different chaotic equations. In communication, encryption is achieved with the help of microcontrollers or software implementations that use more power and have complex hardware implementation. The small IoT devices are expected to be operated on low power and constrained with size. At the same time, these devices are highly vulnerable to security threats, which elevates the need to have low power/size hardware-based security. Since the discovery of chaotic equations, they have been used in various encryption applications. The goal of this research is to take the chaotic implementation to the CMOS level with the sensors on the same chip. The hardware co-simulation is demonstrated on an FPGA board for Chua encryption/decryption architecture. The hardware utilization for Lorenz, SprottD, and Chua on FPGA is achieved with Xilinx System Generation (XSG) toolbox which reveals that Lorenz’s utilization is 9% lesser than Chua’s.

Distributed ledger technologies (DLTs) based on Directed Acyclic Graphs (DAGs) have been gaining much attention due to their performance advantage over the traditional blockchain. IOTA is an example of DAG-based DLT that has shown its significance in the Internet of Things (IoT) environment. Despite that, IOTA is vulnerable to double-spend attacks, which threaten the immutability of the ledger. In this paper, we propose an efficient yet simple method for detecting a parasite chain, which is one form of attempting a double-spend attack in the IOTA network. In our method, a score function measuring the importance of each transaction in the IOTA network is employed. Any abrupt change in the importance of a transaction is reflected in the 1st and 2nd order derivatives of this score function, and therefore used in the calculation of an anomaly score. Due to how the score function is formulated, this anomaly score can be used in the detection of a particular type of parasite chain, characterized by sudden changes in the in-degree of a transaction in the IOTA graph. The experimental results demonstrate that the proposed method is accurate and linearly scalable in the number of edges in the network.

The 2021 T-Mobile breach conducted by John Erin Binns resulted in the theft of 54 million customers' personal data. The attacker gained entry into T-Mobile's systems through an unprotected router and used brute force techniques to access the sensitive information stored on the internal servers. The data stolen included names, addresses, Social Security Numbers, birthdays, driver's license numbers, ID information, IMEIs, and IMSIs. We analyze the data breach and how it opens the door to identity theft and many other forms of hacking such as SIM Hijacking. SIM Hijacking is a form of hacking in which bad actors can take control of a victim's phone number allowing them means to bypass additional safety measures currently in place to prevent fraud. This paper thoroughly reviews the attack methodology, impact, and attempts to provide an understanding of important measures and possible defense solutions against future attacks. We also detail other social engineering attacks that can be incurred from releasing the leaked data.

Recently, a mechanism that randomly shuffles the data sent and allows securing the communication without the need to encrypt all the information has been proposed. This proposal is ideal for IoT systems with low computational capacity. In this work, we analyze the strength of this proposal from a brute-force attack approach to obtain the original message without knowledge of the applied disordering. It is demonstrated that for a set of 10x10 16-bit data, the processing time and the required memory are unfeasible with current technology. Therefore, it is safe.

The development of IoT has penetrated various sectors. The development of IoT devices continues to increase and is predicted to reach 75 billion by 2025. However, the development of IoT devices is not followed by security developments. Therefore, IoT devices can become gateways for cyber attacks, including brute force and sniffing attacks. Authentication mechanisms can be used to ward off attacks. However, the implementation of authentication mechanisms on IoT devices is challenging. IoT devices are dominated by constraint devices that have limited computing. Thus, conventional authentication mechanisms are not suitable for use. Two-factor authentication using RFID and fingerprint can be a solution in providing an authentication mechanism. Previous studies have proposed a two-factor authentication mechanism using RFID and fingerprint. However, previous research did not pay attention to message exchange security issues and did not provide mutual authentication. This research proposes a secure mutual authentication protocol using two-factor RFID and fingerprint using MQTT protocol. Two processes support the authentication process: the registration process and authentication. The proposed protocol is tested based on biometric security by measuring the false acceptance rate (FAR) and false rejection rate (FRR) on the fingerprint, measuring brute force attacks, and measuring sniffing attacks. The test results obtained the most optimal FAR and FRR at the 80% threshold. Then the equal error rate (ERR) on FAR and FRR is around 59.5%. Then, testing brute force and sniffing attacks found that the proposed protocol is resistant to both attacks.

IoT has been an efficient technology for interconnecting different physical objects with the internet. Several cyber-attacks have resulted in compromise in security. Blockchain distributed ledger provide immutability that can answer IoT security concerns. The paper aims at highlighting the challenges & problems currently associated with IoT implementation in real world and how these problems can be minimized by implementing Blockchain based solutions and smart contracts. Blockchain helps in creation of new highly robust IoT known as Blockchain of Things(BCoT). We will also examine presently employed projects working with integrating Blockchain & IoT together for creating desired solutions. We will also try to understand challenges & roadblocks preventing the further implementation of both technologies merger.

Aiming at the current troubles encountered by enterprise employees in their daily work when operating business systems due to web compatibility issues, a dual-core secure browser is designed and developed in the paper based on summarizing the current development status of multi-core browsers, key difficulties and challenges in the field. Based on the Chromium open-source project, the design of a dual-core browser auto-adaptation method is carried out. Firstly, dual-core encapsulation technology is implemented, followed by a study of the core auto-adaptation algorithm, and then a core cookie sharing function is developed based on Hook technology. In addition, the security of the browser is reinforced by designing a cookie manager, adding behavior monitoring functions, and unified platform control to enhance confidentiality and security, providing a safe and secure interface for employees' work and ubiquitous IoT access. While taking security into account, the browser realizes the need for a single browser compatible with all business system web pages of the enterprise, enhancing the operating experience of the client. Finally, the possible future research directions in this field are summarized and prospected.

In order to solve the problem that the traditional “centralized” access control technology can no longer guarantee the security of access control in the current Internet of Things (IoT)environment, a dynamic access control game mechanism based on trust is proposed. According to the reliability parameters of the recommended information obtained by the two elements of interaction time and the number of interactions, the user's trust value is dynamically calculated, and the user is activated and authorized to the role through the trust level corresponding to the trust value. The trust value and dynamic adjustment factor are introduced into the income function to carry out game analysis to avoid malicious access behavior of users. The hybrid Nash equilibrium strategy of both sides of the transaction realizes the access decision-making work in the IoT environment. Experimental results show that the game mechanism proposed in this paper has a certain restraining effect on malicious nodes and can play a certain incentive role in the legitimate access behavior of IoT users.

In the context of big data era, in order to prevent malicious access and information leakage during data services, researchers put forward a location big data encryption method based on privacy protection in practical exploration. According to the problems arising from the development of information network in recent years, users often encounter the situation of randomly obtaining location information in the network environment, which not only threatens their privacy security, but also affects the effective transmission of information. Therefore, this study proposed the privacy protection as the core position of big data encryption method, must first clear position with large data representation and positioning information, distinguish between processing position information and the unknown information, the fuzzy encryption theory, dynamic location data regrouping, eventually build privacy protection as the core of the encryption algorithm. The empirical results show that this method can not only effectively block the intrusion of attack data, but also effectively control the error of position data encryption.

The exponential growth of IoT-type systems has led to a reconsideration of the field of database management systems in terms of storing and handling high-volume data. Recently, many real-time Database Management Systems(DBMS) have been developed to address issues such as security, managing concurrent access to stored data, and optimizing data query performance. This paper studies methods that allow to reduce the temporal validity range for common DBMS. The primary purpose of IoT edge devices is to generate data and make it available for machine learning or statistical algorithms. This is achieved inside the Knowledge Discovery in Databases process. In order to visualize and obtain critical Data Mining results, all the device-generated data must be made available as fast as possible for selection, preprocessing and data transformation. In this research we investigate if IoT edge devices can be used with common DBMS proper configured in order to access data fast instead of working with Real Time DBMS. We will study what kind of transactions are needed in large IoT ecosystems and we will analyze the techniques of controlling concurrent access to common resources (stored data). For this purpose, we built a series of applications that are able to simulate concurrent writing operations to a common DBMS in order to investigate the performance of concurrent access to database resources. Another important procedure that will be tested with the developed applications will be to increase the availability of data for users and data mining applications. This will be achieved by using field indexing.

With the rapid innovation of cloud computing technologies, which has enhanced the application of the Internet of Things (IoT), smart health (s-health) is expected to enhance the quality of the healthcare system. However, s-health records (SHRs) outsourcing, storage, and sharing via a cloud server must be protected and users attribute privacy issues from the public domain. Ciphertext policy attribute-based encryption (CP-ABE) is the cryptographic primitive which is promising to provide fine-grained access control in the cloud environment. However, the direct application of traditional CP-ABE has brought a lot of security issues like attributes' privacy violations and vulnerability in the future by potential powerful attackers like side-channel and cold-bot attacks. To solve these problems, a lot of CP-ABE schemes have been proposed but none of them concurrently support partially policy-hidden and leakage resilience. Hence, we propose a new Smart Health Records Sharing Scheme that will be based on Partially Policy-Hidden CP-ABE with Leakage Resilience which is resilient to bound leakage from each of many secret keys per user, as well as many master keys, and ensure attribute privacy. Our scheme hides attribute values of users in both secret key and ciphertext which contain sensitive information in the cloud environment and are fully secure in the standard model under the static assumptions.

Cyber-Physical Systems (CPS) are complex systems of computational, physical, and human components integrated to achieve some function over one or more networks. The use of distributed simulation, or co-simulation, is one method often used to analyze the behavior and properties of these systems. High-Level Architecture (HLA) is an IEEE co-simulation standard that supports the development and orchestration of distributed simulations. However, a simple HLA federation constructed with the component simulations (i.e., federates) does not satisfy several requirements that arise in real-world use cases such as the shared use of limited physical and computational resources, the need to selectively hide information from participating federates, the creation of reusable federates and federations for supporting configurable shared services, achieving performant distributed simulations, organizing federations across different model types or application concerns, and coordinating federations across organizations with different information technology policies. This paper describes these core requirements that necessitate the use of multiple HLA federations and presents various mechanisms for constructing such integrated HLA federations. An example use case is implemented using a model-based rapid simulation integration framework called the Universal CPS Environment for Federation (UCEF) to illustrate these requirements and demonstrate techniques for integrating multiple HLA federations.

The (IoT) paradigm’s fundamental goal is to massively connect the “smart things” through standardized interfaces, providing a variety of smart services. Cyber-Physical Systems (CPS) include both physical and cyber components and can apply to various application domains (smart grid, smart transportation, smart manufacturing, etc.). The Digital Twin (DT) is a cyber clone of physical objects (things), which will be an essential component in CPS. This paper designs a systematic taxonomy to explore different attacks on DT-based CPS and how they affect the system from a four-layer architecture perspective. We present an attack space for DT-based CPS on four layers (i.e., object layer, communication layer, DT layer, and application layer), three attack objects (i.e., confidentiality, integrity, and availability), and attack types combined with strength and knowledge. Furthermore, some selected case studies are conducted to examine attacks on representative DT-based CPS (smart grid, smart transportation, and smart manufacturing). Finally, we propose a defense mechanism called Secured DT Development Life Cycle (SDTDLC) and point out the importance of leveraging other enabling techniques (intrusion detection, blockchain, modeling, simulation, and emulation) to secure DT-based CPS.

Information leaks are a top concern to industry and government leaders. The Internet of Things (IoT) is a rapidly growing technology capable of sensing real-world events. IoT devices lack a common security standard and typically use lightweight security solutions, exposing the sensitive real-world data they gather. Covert channels are a practical method of exfiltrating data from these devices.This research presents a novel IoT covert timing channel (CTC) that encodes data within preexisting network information, namely ports or addresses. This method eliminates the need for inter-packet delays (IPD) to encode data. Seven different encoding methods are implemented between two IoT protocols, TCP/IP and ZigBee. The TCP/IP covert channel is created by mimicking a Ring smart doorbell and implemented using Amazon Web Services (AWS) servers to generate traffic. The ZigBee channel is built by copying a Philips Hue lighting system and executed on an isolated local area network (LAN). Variants of the CTC focus either on Stealth or Bandwidth. Stealth methods mimic legitimate traffic captures to make them difficult to detect while the Bandwidth methods forgo this approach for maximum throughput. Detection results are presented using shape-based and regularity-based detection tests.The Stealth results have a throughput of 4.61 bits per second (bps) for TCP/IP and 3.90 bps for ZigBee. They also evade shape and regularity-based detection tests. The Bandwidth methods average 81.7 Kbps for TCP/IP and 9.76 bps for ZigBee but are evident in detection tests. The results show that CTC using address or port encoding can have superior throughput or detectability compared to IPD-based CTCs.

As IoT technologies mature, they are increasingly finding their way into more sensitive domains, such as Medical and Industrial IoT, in which safety and cyber-security are paramount. While the number of deployed IoT devices continues to increase annually, they still present severe cyber-security vulnerabilities, turning them into potential targets and entry points to support further attacks. Naturally, as these nodes are compromised, attackers aim at setting up stealthy communication behaviours, to exfiltrate data or to orchestrate nodes of a botnet in a cloaked fashion. Network covert channels are increasingly being used with such malicious intents. The IEEE 802.15.4 is one of the most pervasive protocols in IoT, and a fundamental part of many communication infrastructures. Despite this fact, the possibility of setting up such covert communication techniques on this medium has received very little attention. We aim at analysing the performance and feasibility of such covert-channel implementations upon the IEEE 802.15.4 protocol. This will enable a better understanding of the involved risk and help supporting the development of further cyber-security mechanisms to mitigate this threat.

A methodology for studying the level of security for various types of CPS through the analysis of the consequences was developed during the research process. An analysis of the architecture of cyber-physical systems was carried out, vulnerabilities and threats of specific devices were identified, a list of possible information attacks and their consequences after the exploitation of vulnerabilities was identified. The object of research is models of cyber-physical systems, including IoT devices, microcomputers, various sensors that function through communication channels, organized by cyber-physical objects. The main subjects of this investigation are methods and means of security testing of cyber-physical systems (CPS). The main objective of this investigation is to update the problem of security in cyber-physical systems, to analyze the security of these systems. In practice, the testing methodology for the cyber-physical system “Smart Factory” was implemented, which simulates the operation of a real CPS, with different types of links and protocols used.

Security of Internet of Things (IoT) is one of the most prevalent crucial challenges ever since. The diversified devices and their specification along with resource constrained protocols made it more complex to address over all security need of IoT. Denial of Service attacks, being the most powerful and frequent attacks on IoT have been considered so forth. However, the attack happens on multiple layers and thus a single detection technique for each layer is not sufficient and effective to combat these attacks. Current study focuses on cross layer intrusion detection system (IDS) for detection of multiple Denial of Service (DoS) attacks. Presently, two attacks at Transmission Control Protocol (TCP) and Routing Protocol are considered for Low power and Lossy Networks (RPL) and a neural network-based IDS approach has been proposed for the detection of such attacks. The attacks are simulated on NetSim and detection and the performance shows up to 80% detection probabilities.

The Internet of Things is a developing technology that converts physical objects into virtual objects connected to the internet using wired and wireless network architecture. Use of cross-layer techniques in the internet of things is primarily driven by the high heterogeneity of hardware and software capabilities. Although traditional layered architecture has been effective for a while, cross-layer protocols have the potential to greatly improve a number of wireless network characteristics, including bandwidth and energy usage. Also, one of the main concerns with the internet of things is security, and machine learning (ML) techniques are thought to be the most cuttingedge and viable approach. This has led to a plethora of new research directions for tackling IoT's growing security issues. In the proposed study, a number of cross-layer approaches based on machine learning techniques that have been offered in the past to address issues and challenges brought on by the variety of IoT are in-depth examined. Additionally, the main issues are mentioned and analyzed, including those related to scalability, interoperability, security, privacy, mobility, and energy utilization.

As a mature and open mobile operating system, Android runs on many IoT devices, which has led to Android-based IoT devices have become a hotbed of malware. Existing static detection methods for malware using artificial intelligence algorithms focus only on the java code layer when extracting API features, however there is a lot of malicious behavior involving native layer code. Thus, to make up for the neglect of the native code layer, we propose a heterogeneous information network-based Android malware detection method with cross-layer features. We first translate the semantic information of apps and API calls into the form of meta-paths, and construct the adjacency of apps based on API calls, then combine information from different meta-paths using multi-core learning. We implemented our method on the dataset from VirusShare and AndroZoo, and the experimental results show that the accuracy of our method is 93.4%, which is at least 2% higher than other related methods using heterogeneous information networks for malware detection.

Owing to the decreasing costs of distributed energy resources (DERs) as well as decarbonization policies, power systems are undergoing a modernization process. The large deployment of DERs together with internet of things (IoT) devices provide a platform for peer-to-peer (P2P) energy trading in active distribution networks. However, P2P energy trading with IoT devices have driven the grid more vulnerable to cyber-physical threats. To this end, in this paper, a resilience-oriented P2P energy exchange model is developed considering three phase unbalanced distribution systems. In addition, various scenarios for vulnerability assessment of P2P energy exchanges considering adverse prosumers and consumers, who provide false information regarding the price and quantity with the goal of maximum financial benefit and system operation disruption, are considered. Techno-economic survivability analysis against these attacks are investigated on a IEEE 13-node unbalanced distribution test system. Simulation results demonstrate that adverse peers can affect the physical operation of grid, maximize their benefits, and cause financial loss of other agents.

The Internet of Things is an emerging technology for recent marketplace. In IoT, the heterogeneous devices are connected through the medium of the Internet for seamless communication. The devices used in IoT are resource-constrained in terms of memory, power and processing. Due to that, IoT system is unable to implement hi-end security for malicious cyber-attacks. The recent era is all about connecting IoT devices in various domains like medical, agriculture, transport, power, manufacturing, supply chain, education, etc. and thus need to be prevented from attacks and analyzed after attacks for legal action. The legal analysis of IoT data, devices and communication is called IoT forensics which is highly indispensable for various types of attacks on IoT system. This paper will review types of IoT attacks and its preventive measures in cyber security. It will also help in ascertaining IoT forensics and its challenges in detail. This paper will conclude with the high requirement of cyber security in IoT domains with implementation of standard rules for IoT forensics.

Model compression is one of the most preferred techniques for efficiently deploying deep neural networks (DNNs) on resource- constrained Internet of Things (IoT) platforms. However, the simply compressed model is often vulnerable to adversarial attacks, leading to a conflict between robustness and efficiency, especially for IoT devices exposed to complex real-world scenarios. We, for the first time, address this problem by developing a novel framework dubbed Magical-Decomposition to simultaneously enhance both robustness and efficiency for hardware. By leveraging a hardware-friendly model compression method called singular value decomposition, the defending algorithm can be supported by most of the existing DNN hardware accelerators. To step further, by using a recently developed DNN interpretation tool, the underlying scheme of how the adversarial accuracy can be increased in the compressed model is highlighted clearly. Ablation studies and extensive experiments under various attacks/models/datasets consistently validate the effectiveness and scalability of the proposed framework.