As the number of Internet of Things (IoT) devices connected to the network rapidly increases, network attacks such as flooding and Denial of Service (DoS) are also increasing. These attacks cause network disruption and denial of service to IoT devices. However, a large number of heterogenous devices deployed in the IoT environment make it difficult to detect IoT attacks using traditional rule-based security solutions. It is challenging to develop optimal security models for each type of the device. Machine learning (ML) is an alternative technique that allows one to develop optimal security models based on empirical data from each device. We employ the ML technique for IoT attack detection. We focus on botnet attacks targeting various IoT devices and develop ML-based models for each type of device. We use the N-BaIoT dataset generated by injecting botnet attacks (Bashlite and Mirai) into various types of IoT devices, including a Doorbell, Baby Monitor, Security Camera, and Webcam. We develop a botnet detection model for each device using numerous ML models, including deep learning (DL) models. We then analyze the effective models with a high detection F1-score by carrying out multiclass classification, as well as binary classification, for each model.

Intelligent Detection of IoT Botnets Using Machine Learning and Deep Learning

A cascaded federated deep learning based framework for detecting wormhole attacks in IoT networks

In Mobile Ad hoc Network (MANET) enabled Internet of Things (IoT) agricultural field monitoring, sensor devices are automatically connected and form an independent network that serves as a cloud for many services such as monitoring, securing, and properly maintaining. Cloud-based services in MANET models can prove to be an extremely effective way of smart agricultural functionalities for device-to-device information exchange. Security is a serious issue with Cloud-MANET-based IoT since nodes are scattered, mobile, and lacking centralized administrator, which makes it possible for data tampering and illegal actions on cloud servers. Therefore, these types of networks are more vulnerable to Denial of Service (DoS) attacks such as Blackhole and Wormhole. The MANET Enabled IoT-Agricultural Field Monitoring environment is deployed through a case study. The effect of Blackhole and Wormhole attacks is analyzed using the Ad hoc On-demand Distance Vector (AODV) routing protocol with the help of Network Simulator 3 (NS-3) in order to determine which has the most impact on network performance. We computed performance constraints such as throughput, packet delivery ratio (PDR), end-to-end delay (EED), and Jitter-Sum of preprocessed data gathered with the flow-monitor module of NS-3. The effect of attacks on MANET Enabled IoT-Agricultural Field Monitoring is compared on the varying number of nodes participating in the Cloud-MANET-based IoT network. The throughput and goodput capability of every node is computed through the trace metric package. This method is also highly useful for future Cloud-MANET-Based IoT smart agricultural field security research.

Comparison of Blackhole and Wormhole Attacks in Cloud MANET Enabled IoT for Agricultural Field Monitoring

Wireless sensor networks (WSNs) are comprised of large collections of small devices having low operating power, low memory space, and limited processing capabilities referred to as sensor nodes. The nodes in WSNs are capable of sensing, recording, and monitoring environmental conditions. Nowadays, a variety of WSNs applications can be found in many areas such as in healthcare, agriculture, industries, military, homes, offices, hospitals, smart transportation, and smart buildings. Though WSNs offer many useful applications, they suffer from many deployment issues. The security issue is one of them. The security of WSNs is considerable because of the use of unguided medium and their deployment in harsh, physically unprotected, and unattended environments. This chapter aims to discuss various security objectives and security attacks on WSNs and summarizes the discussed attacks according to their categories. The chapter also discusses different security protocols presented to prevent, detect, and recover the WSNs from various security attacks.

Security of Wireless Sensor Networks: The Current Trends and Issues

Driven by the rapid escalation of its utilization, as well as ramping commercialization, Internet of Things (IoT) devices increasingly face security threats. Apart from denial of service, privacy, and safety concerns, compromised devices can be used as enablers for committing a variety of crime and e-crime. Despite ongoing research and study, there remains a significant gap in the thorough analysis of security challenges, feasible solutions, and open secure problems for IoT. To bridge this gap, we provide a comprehensive overview of the state of the art in IoT security with a critical investigation-based approach. This includes a detailed analysis of vulnerabilities in IoT-based systems and potential attacks. We present a holistic review of the security properties required to be adopted by IoT devices, applications, and services to mitigate IoT vulnerabilities and, thus, successful attacks. Moreover, we identify challenges to the design of security protocols for IoT systems in which constituent devices vary markedly in capability (such as storage, computation speed, hardware architecture, and communication interfaces). Next, we review existing research and feasible solutions for IoT security. We highlight a set of open problems not yet addressed among existing security solutions. We provide a set of new perspectives for future research on such issues including secure service discovery, on-device credential security, and network anomaly detection. We also provide directions for designing a forensic investigation framework for IoT infrastructures to inspect relevant criminal cases, execute a cyber forensic process, and determine the facts about a given incident. This framework offers a means to better capture information on successful attacks as part of a feedback mechanism to thwart future vulnerabilities and threats. This systematic holistic review will both inform on current challenges in IoT security and ideally motivate their future resolution.

A Holistic Analysis of Internet of Things (IoT) Security: Principles, Practices, and New Perspectives

The Internet of Things (IoT) is nothing but a collection of wireless and wired devices, commonly termed as nodes operated remotely. This operation is done by assuming these nodes as the sensors in a wireless sensor network (WSN) administered through a base station. We start with briefing about IoT and then briefing IoT layer models. After this, we discuss attacks with regard to IoT namely Sinkhole attack, Sybil attack, HELLO flood attack, Acknowledgement spoofing attack and their respective detection methods. This paper is systematic review of existing mechanism for the detection of wormhole attack and a new method is proposed.  

/pdf/wormhole-attack-a-major-security-concern-in-internet-of-19trzg939z.pdf

Wormhole Attack: a Major Security Concern in Internet of Things (Iot)

IoT (Internet of Things) technology is used here to monitor underground mining locations. Developing sensor based device is highly challenging and hence accelerometer is used to monitor the deep mining region. Each and every analyzed boundary/qualities/characteristics are then sent to the Micro Controller Unit (MCU). The MCU unit is utilized to perform a basic analysis. When a critical situation is detected, an alarm will be given and the corresponding details will be sent directly to the web server. The collected data are then displayed on the webserver page of the underground control centre to present the screen and take quick action to prevent serious damage. Simultaneously, the Zigbee handset module is employed in the proposed model to communicate data from the mining section to take immediate action and reach the location. Finally, the experimental results show the increase in coal mining safety with the proposed underground coal mine monitoring system.

IoT based Coal Mining Monitoring and Control

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Wormhole Attack: a Major Security Concern in Internet of Things (Iot)

IoT based Coal Mining Monitoring and Control