Provably Secure Lightweight Mutual Authentication and Key Agreement Scheme for Cloud-Based IoT Environments.

The Internet of Things (IoT) devices is able to communicate with another IoT devices in heterogeneous environments. They are also able to create a network by their own using wireless capabilities without a centralized approach. This network is called wireless mobile ad hoc network (MANET). The MANET facilitate IoT devices to communicate with each other in an infrastructure less network of the Internet of Things environment. The IoT devices can dynamically join, create connection and exchange information among them. The role of cloud in this framework is to store and process the data by IoT devices. The communication Security has been raised as one of the most critical issues of cloud computing where resolving such an issue would result in a constant growth in the use and popularity of cloud. Our purpose of this study is to create a framework for providing the communication in an integrated framework of Cloud and MANET among IoT devices. Our main contribution links a new study that integrates cloud computing to the MANET and provides communication to the internet of things. This study can be used in the framework of the Internet of Things in future.

The development of computing technology in increasing the accessibility and agility of daily activities currently uses the Internet of Things (IoT). Over time, the increasing number of IoT device users impacts access and delivery of valuable data. This is the primary goal of cybercriminals to operate malicious software. In addition to the positive impact of using technology, it is also a negative impact that creates new problems in security attacks and cybercrimes. One of the most dangerous cyberattacks in the IoT environment is the Mirai botnet malware. The malware turns the user's device into a botnet to carry out Distributed Denial of Service (DDoS) attacks on other devices, which is undoubtedly very dangerous. Therefore, this study proposes a k-nearest neighbor algorithm to classify Mirai malware-type DDOS attacks on IoT device environments. The malware classification process was carried out using rapid miner machine learning by conducting four experiments using SYN, ACK, UDP, and UDPlain attack types. The classification results from selecting five parameters with the highest activity when the device is attacked. In order for these five parameters to be a reference in the event of a malware attack starting in the IoT environment, the results of the classification have implications for further research. In the future, it can be used as a reference in making an early warning innovative system as an early warning in the event of a Mirai botnet attack.

The proliferation of Internet of Things (IoT) devices has brought unprecedented convenience to our lives, but it has also opened the door to new security challenges. One of the most pressing threats in the IoT landscape is the proliferation of BotNets, which can compromise and control a multitude of devices for malicious purposes. In this paper, we propose a novel approach to address this issue: a Federated Machine Learning Solution for BotNet detection in IoT environments. Our method leverages the collective intelligence of distributed IoT devices while respecting privacy constraints, ensuring that sensitive data never leaves the device. We present a detailed methodology for federated model construction, including data collection, local model training, and secure aggregation. The resulting federated model offers improved accuracy and robustness in BotNet detection, as demonstrated through rigorous evaluation on the N-BaIoT dataset. Our findings underscore the effectiveness of this approach in enhancing IoT device security by detecting and mitigating BotNet threats while safeguarding data privacy. This paper contributes to the advancement of IoT security strategies and provides a framework for protecting IoT devices against evolving threats in a federated and privacy-preserving manner.

The proliferation of Internet of Things (IoT) devices has introduced significant challenges in cybersecurity, particularly in the realm of intrusion detection. While effective, traditional centralized machine learning approaches often compromise data privacy and scalability due to the need for data aggregation. In this study, we propose a federated learning framework for near-real-time intrusion detection in IoT environments. Federated learning enables decentralized model training across multiple devices without exchanging raw data, thereby preserving privacy and reducing communication overhead. Our approach builds upon a previously proposed hybrid model, which combines a machine learning model deployed on IoT devices with a second-level cloud-based analysis. This previous work required all data to be passed to the cloud in aggregate form, limiting security. We extend this model to incorporate federated learning, allowing for distributed training while maintaining high accuracy and privacy. We evaluate the performance of our federated-learning-based model against a traditional centralized model, focusing on accuracy retention, training efficiency, and privacy preservation. Our experiments utilize actual attack data partitioned across multiple nodes. The results demonstrate that this hybrid federated learning not only offers significant advantages in terms of data privacy and scalability but also retains the previous competitive accuracy. This paper also explores the integration of federated learning with cloud-based infrastructure, leveraging platforms such as Databricks and Google Cloud Storage. We discuss the challenges and benefits of implementing federated learning in a distributed environment, including the use of Apache Spark and MLlib for scalable model training. The results show that all the algorithms used maintain an excellent identification accuracy (98% for logistic R=regression, 97% for SVM, and 100% for Random Forest). We also report a very short training time (less than 11 s on a single machine). The previous very low application time is also confirmed (0.16 s for over 1,697,851 packets). Our findings highlight the potential of federated learning as a viable solution for enhancing cybersecurity in IoT ecosystems, paving the way for further research in privacy-preserving machine learning techniques.

Internet of Things (IoT) devices are increasingly deployed for different purposes such as data sensing, collecting and controlling. IoT improves user experiences by allowing a large number of smart devices to connect and share information. Many existing malware attacks, targeted at traditional computers connected to the Internet, may also be directed at IoT devices. Therefore, efficient protection at IoT devices could save millions of internet users from malicious activities. However, existing malware detection approaches suffer from high computational complexity. In this study, we propose a more accurate and fast model for detecting malware in the IoT environment. We introduce a Malware Threat Hunting System (MTHS) in the proposed model. MTHS first converts malware binary into a color image and then conducts the machine or deep learning analysis for efficient malware detection. We finally prepare a baseline to compare the performance of MTHS with traditional state-of-the-art malware detection approaches. We conduct experiments on two public datasets of Windows and Android software. The experimental results indicate that the response time and the detection accuracy of MTHS are better than those of previous machine learning and deep learning approaches.

The proliferation of Internet of Things (IoT) devices has introduced unprecedented connectivity and convenience but also heightened the vulnerability to botnet attacks. There are an increasing number of Internet of Things (IoT) devices connected to the network these days, and due to the advancement in technology, the security threads and cyberattacks, such as botnets, are emerging and evolving rapidly with high-risk attacks. These attacks disrupt IoT transition by disrupting networks and services for IoT devices. Many recent studies have proposed ML and DL techniques for detecting and classifying botnet attacks in the IoT environment. This project presents a straightforward approach to detect botnet activity within IoT networks through the utilization of machine learning techniques. By analyzing network traffic patterns and employing unsupervised learning algorithms, we demonstrate an effective method to identify and mitigate botnet threats in IoT environments. By this project we intend to offer a valuable contribution in enhancing the security of IoT ecosystem. Key Word: Internet of Things(IoT),cybersecurity, botnet attacks, machine learning(ML),UNSW-NB15 dataset, exploratory data analysis, XgBoost

The rapid proliferation of Internet of Things (IoT) devices, coupled with their inherent security vulnerabilities, has significantly expanded the attack surface, intensifying threats such as man-in-the-middle attacks, traffic hijacking, and distributed denial-of-service (DDoS) attacks, thereby posing serious risks to the security and reliability of the entire ecosystem. The network traffic associated with IoT devices is diverse and dynamic, often exhibiting complex structural features such as periodic fluctuations, varying packet sizes, and time-varying patterns that make detection challenging. Although deep learning has demonstrated strong capabilities in efficiently identifying complex and dynamic malicious traffic through powerful feature extraction and adaptive learning abilities, its high model complexity, substantial computational demands, and large parameter sizes hinder direct deployment on resource-constrained IoT devices. In order to tackle this issue, this paper proposes a malicious traffic detection framework for the Internet of Things (IoT) based on multi-teacher knowledge distillation. The proposed model, termed the Lightweight Multi-Teacher Spatiotemporal Distillation Neural Network (LMT-SDNN), employs two high-performance teacher models: Residual Inception and a One-Dimension Convolution Netural Network (1D-CNN) integrated with idirectional Long Short-Term Memory (BiLSTM), to effectively capture the complex structural features of network traffic. Furthermore, a novel Time-Related Window Loss (TRW) function is design to enhance the student’s ability to capture temporal features, thereby improving its overall performance. The effectiveness of LMT-SDNN is validated through comparisons with five baseline models on two publicly available datasets, ToN_IoT and BoT_IoT. Experimental results show that LMT-SDNN achieves a compression rate of over 99% in both model complexity and parameter count, while maintaining an accuracy exceeding 99%, indicating its strong potential for multiclass malicious traffic detection in IoT environments.

DDoS attacks often happen in cloud servers and cause a devastating problem. However, an increasing number of Internet of Things (IoT) devices makes us not ignore the influence of large-scale DDoS attacks from IoT devices. In this paper, we propose a machine learning-based on a multi-layer IoT DDoS attack detection system, including IoT devices, IoT gateways, SDN switches, and cloud servers. Firstly, we build eight smart poles with various sensors on our campus and collect sensor data as our datasets through wireless networks or wired networks. Next, we extract the features based on DDoS attack types. The feature selection can result in high accuracy DDoS attack detection in the real IoT environment. The experimental results show that our multi-layer DDoS detection system can accurately detect DDoS attacks. And the SDN controller can block venomous devices effectively according to blacklists from the results of our IoT DDoS attacks detection system.

Consumer electronics is a device designed to meet the everyday needs of individuals, including home appliances, smartphones, and laptops. The Internet of Things (IoT) environment is witnessing tremendous growth, driven by the simultaneous increase and continuous cyber-attack innovation. The imperative to fortify IoT devices against emerging threats became more prevalent as these threats became increasingly complex. In this context, threats include malware attacks, unauthorized access, and data breaches. Securing these devices requires robust encryption, regular software updates, and vigilant monitoring to protect sensitive information and ensure user safety. Threat detection using deep learning (DL) includes leveraging cutting-edge neural network models to detect and alleviate security threats within digital environments. DL, a branch of machine learning (ML), excels in processing abundant data to detect anomalies and patterns that might indicate possible threats. This technique improves classical threat detection techniques by reducing false positives and enhancing accuracy. By continuously analyzing data from various sources, such as network traffic and user behavior, DL techniques can quickly recognize and respond to emerging vulnerabilities, providing a proactive and adaptive security solution essential for protecting consumer electronics and IoT devices. This study develops a new Hybrid DL-based Threat Detection with Blockchain Technology (HDLTD-BCT) technique for secure IoT-based consumer electronics. The presented HDLTD-BCT technique can improve security among consumer electronics in the IoT environment. For this purpose, the HDLTD-BCT technique initially applies BC technology to monitor data flow in the IoT platform. The HDLTD-BCT technique scales the input data for threat detection using a min–max scalar. Besides, the detection process is performed by HDL, comprising a stacked LSTM-based denoising autoencoder (LSDAE) model. To enhance the HDL method’s performance, the Maritime Cyber Warfare Officers (MCWO) performs the hyperparameter selection process. The experimental results of the HDLTD-BCT technique can be validated utilizing the NSLKDD dataset. The simulation outcomes of the HDLTD-BCT technique exhibited a superior accuracy value of 99.68% compared to other DL models.

The internet of things (IoT) has brought the properties of convenience, intelligence, and manageability into our daily lives. Nevertheless, it also gives malicious attackers lots of opportunity to compromise our private information. Hence, the security issue over IoT has become an emergent and crucial research topic. Kalra and Sood (2015) proposed an authentication scheme for IoT device and cloud server. Unfortunately, Chang et al. (2017) have pointed out the weaknesses of Kalra and Sood's scheme and provided proper improvements. However, we have found that the improved version still exist potential risks. Thus, we aim to develop a brand-new ECC-based authentication mechanism for offering a secure session between an IoT device and a cloud server. In particular, the new method is proved secure under the examination of AVISPA, which is a formal verification tool.

Internet of Things (IoT) and Distributed Denial of Service (DDoS) is the most growing emergence catchword that has a deep relation to each other. The lack of securities in IoT devices creates a loophole to hijack those devices and use them for a cybercrime. Recently many security breaches on IoT Environment along with high volume attacks have been reported. So, securing the IoT Environment to prevent cyber-attacks like DDoS on crucial infrastructure is the first priority. Moreover, detecting DDoS attacks is also harder as sometimes DDoS attack traffic is alike normal traffic. Here, we tried to describe the long-ranging impact of DDoS attacks on IoT Environment. Some recommended security solutions and guidelines have been proposed to detect and protect IoT devices against DDoS attacks. Thus, the deployment of this security solution will keep the IoT Environment cyber threat free.

Recent advances in Internet of Things (IoT) technologies require a new type of IoT security environment. Various heterogeneous smart devices have easy access to IoT environment, and as the number of users increases, they are exposed to various threats such as malicious attacks on IoT devices and IoT infrastructure, and data tampering by malicious code. Malware detection in IoT requires data and models for continuous and changing learning of smart devices. Methods/Statistical analysis: To minimize these security threats, various malware detection techniques in the field of IoT security have been studied. Malware detection in IoT environment is important for data derivation and learning model required for continuous and changing learning of smart devices. The metadata of malware detection can be normalized by the value of device id, time, behavior, location and state. This paper proposes behavior-based malware detection using deep learning (BMD-DL). Findings: BMD-DL was able to collect metadata about behavior-based malicious behavior and learn and detect malicious codes through deep learning. In addition, through the learned model, IoT Security is provided by disconnecting malicious devices that cause malicious behavior in the IoT environment. Improvements/Applications: BMD-DL collects behavioral data generated from multiple devices in the IoT and applies the results learned through deep learning to detect persistent malware.

The Internet of Things (IoT) is a fast-expanding network of smart heterogeneous objects. It refers to the physical devices that are capable of communicating with other physical devices. Unlike the wireless sensor networks (WSNs), IoT is connected to worldwide Internet that exposes it to global intrusion in addition to wireless attacks inside an IoT network. It is protected by cryptographic and network security techniques, but they are vulnerable to internal and external attacks. The IoT devices are resource constrained in terms of limited storage, battery, limited transmission range and processing. To protect these tiny devices from the inside and outside cyberattacks, we need a lightweight security system that can run efficiently and effectively on these IoT devices. We also need a system, which can identify the type of intrusion and trigger an alarm in intrusion scenario to take appropriate preventive measure. Hence, an Intrusion Detection System (IDS) plays an important role to prevent such cyberattacks in IoT. These devices can be static or mobile in an IoT environment; this must be considered while designing IDS for IoT system. This book chapter presents various IDSs for an IoT system and their comparisons in terms of detection rate, false positive and accuracy in both static and mobility of devices.

Internet of Things (IoT) is growing too fast and it is being deployed in various domains such as Smart Homes, Smart City, Healthcare, etc. Security in IoT environments is critical, as information between IoT devices and end-users needs to be shared securely. Therefore, IoT systems require protecting two critical aspects, which are confidential data and identity management. However, IoT devices have many constraints, including limitations in computation, power, memory and energy. In this paper, we present a fast and secure authenticated key agreement protocol based on elliptic curve cryptography. The proposed protocol is used for distributed IoT applications. Therefore, a Trusted Third Party (TTP) is used to perform the registration phases for users and IoT devices. With respect to distributed networks, and after the registration phases are accomplished, the end-user can communicate and obtain information straightaway from the sensor nodes. The informal security of the proposed protocol is analyzed, and the analysis shows that our protocol can resist a variety of attacks. Our proposed protocol uses ECDigital Signature Algorithm (EC–DSA). It offers significantly improved performance, in terms of the required computational amount and energy usage, compared to many other authenticated protocols.

Delivering information from Internet of Things (IoT) devices to a cloud server possesses several security issues, e.g. information eavesdropping, modification, and theft. Therefore, communication between IoT devices and the cloud server should be protected by encryption methods. However, there are few encryption techniques options that are suitable for the need for lightweight communication as demanded by the IoT devices. Due to these circumstances, the NSA launched an encryption algorithm for IoT named Simon and Speck, which are maximally efficient while still providing the advertised level of security, as determined by the key size. This study aims to test and compare the Simon-Speck and AES encryption algorithms and their effect on networking performance on IoT devices. The parameters in this test are delay, throughput, the efficiency of memory usage from the encryption algorithm, and the value of the avalanche effect. Experimental results show that the Speck algorithm outperforms the Simon and the AES algorithms in terms of communication delay and memory usage. Regarding the avalanche effect values, the Simon algorithm possesses the highest avalanche effect value on average against the Speck and the AES algorithms.