scholarly journals Towards 5G-based IoT security analysis against Vo5G eavesdropping

Computing ◽  
2021 ◽  
Author(s):  
Sungmoon Kwon ◽  
Seongmin Park ◽  
HyungJin Cho ◽  
Youngkwon Park ◽  
Dowon Kim ◽  
...  
Keyword(s):  
Intrusion Detection System ◽  
Smart Home ◽  
Detection System ◽  
Security Analysis ◽  
Iot Security ◽  
Mobile Carrier ◽  
Home Building ◽  
Verification Methods ◽  
Machine Type Communications ◽  
Iot Devices

AbstractWith the advent of 5G technology, the enhanced Mobile Broadband technology is translating 5G-based Internet of Things (IoT) such as smart home/building into reality. With such advances, security must mitigate greater risks associated with faster and more accessible technology. The 5G-based IoT security analysis is crucial to IoT Technology, which will eventually expand extensively into massive machine-type communications and Ultra-Reliable Low Latency Communications. This paper analyses the countermeasures and verification methods of eavesdropping vulnerabilities within IoT devices that use the current 5G Non-Standalone (NSA) network system. The network hierarchical structure of 5G-based IoT was evaluated for vulnerability analysis, performed separately for 5G Access Stratum (AS), Non-Access Stratum (NAS), and Internet Protocol (IP) Multimedia Subsystem (IMS). AS keystream reuse, NAS null-ciphering, and IMS IPsec off vulnerabilities were tested on mobile carrier networks to validate it on the 5G NSA network as well. A countermeasure against each vulnerability was presented, and our Intrusion Detection System based on these countermeasures successfully detected the presented controlled attacks.

scholarly journals A Study of Soft Computing Based IoT Device Security System

2020 ◽  
Vol 9 (11) ◽  
pp. 5-10
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Intrusion Detection System ◽  
Detection System ◽  
Security System ◽  
Security Risks ◽  
Iot Security ◽  
Security Attack ◽  
Artificial Neural ◽  
Iot Devices

The ubiquitous computing environment has increased interest in IoT technology. As IoT has open characteristics in the fields of industry, increased accessibility has raised the possibility of threats. As the IoT network was small on scale, there was risk of security. IoT development brought the network environment by combining networks, therefore risk of security attack compared to small network. The response time while operating IoT devices to detect intrusion through hacking, the artificial neural network responses using mobile devices. This process help to deal with hacking. By detecting virus in real time, this process help to prevent intrusion. As IoT security risks, we suggested an intrusion detection system using artificial neural network model in this study. The system which is developed in this can be adjusted to fit situations of IoT by facilitating modification of critical values. The research which detects anomaly through the response to be used for information security system which utilize IoT .

scholarly journals Deep Learning-Based Security Behaviour Analysis in IoT Environments: A Survey

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Yawei Yue ◽  
Shancang Li ◽  
Phil Legg ◽  
Fuzhong Li
Keyword(s):  
Deep Learning ◽  
Smart Home ◽  
Ad Hoc ◽  
Security Analysis ◽  
Home Healthcare ◽  
Security And Privacy ◽  
Iot Security ◽  
Privacy Concerns ◽  
Iot Devices ◽  
Primary Focus

Internet of Things (IoT) applications have been used in a wide variety of domains ranging from smart home, healthcare, smart energy, and Industrial 4.0. While IoT brings a number of benefits including convenience and efficiency, it also introduces a number of emerging threats. The number of IoT devices that may be connected, along with the ad hoc nature of such systems, often exacerbates the situation. Security and privacy have emerged as significant challenges for managing IoT. Recent work has demonstrated that deep learning algorithms are very efficient for conducting security analysis of IoT systems and have many advantages compared with the other methods. This paper aims to provide a thorough survey related to deep learning applications in IoT for security and privacy concerns. Our primary focus is on deep learning enhanced IoT security. First, from the view of system architecture and the methodologies used, we investigate applications of deep learning in IoT security. Second, from the security perspective of IoT systems, we analyse the suitability of deep learning to improve security. Finally, we evaluate the performance of deep learning in IoT system security.

scholarly journals A Supervised Intrusion Detection System for Smart Home IoT Devices

2019 ◽  
Vol 6 (5) ◽  
pp. 9042-9053 ◽  
Author(s):  
Eirini Anthi ◽  
Lowri Williams ◽  
Malgorzata Slowinska ◽  
George Theodorakopoulos ◽  
Pete Burnap
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Smart Home ◽  
Detection System ◽  
Iot Devices

scholarly journals Preventing MQTT Vulnerabilities Using IoT-Enabled Intrusion Detection System

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 567
Author(s):  
Muhammad Husnain ◽  
Khizar Hayat ◽  
Enrico Cambiaso ◽  
Ubaid U. Fayyaz ◽  
Maurizio Mongelli ◽  
...  
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Communication Protocol ◽  
Detection System ◽  
Communication Technologies ◽  
Cyber Attacks ◽  
Iot Security ◽  
Iot Applications ◽  
Iot Devices ◽  
Main Component

The advancement in the domain of IoT accelerated the development of new communication technologies such as the Message Queuing Telemetry Transport (MQTT) protocol. Although MQTT servers/brokers are considered the main component of all MQTT-based IoT applications, their openness makes them vulnerable to potential cyber-attacks such as DoS, DDoS, or buffer overflow. As a result of this, an efficient intrusion detection system for MQTT-based applications is still a missing piece of the IoT security context. Unfortunately, existing IDSs do not provide IoT communication protocol support such as MQTT or CoAP to validate crafted or malformed packets for protecting the protocol implementation vulnerabilities of IoT devices. In this paper, we have designed and developed an MQTT parsing engine that can be integrated with network-based IDS as an initial layer for extensive checking against IoT protocol vulnerabilities and improper usage through a rigorous validation of packet fields during the packet-parsing stage. In addition, we evaluate the performance of the proposed solution across different reported vulnerabilities. The experimental results demonstrate the effectiveness of the proposed solution for detecting and preventing the exploitation of vulnerabilities on IoT protocols.

scholarly journals Different Dimensions of IOT Security

2020 ◽  
Vol 8 (5) ◽  
pp. 860-863
Keyword(s):  
Intrusion Detection System ◽  
Detection System ◽  
Cyber Attacks ◽  
Security And Privacy ◽  
Security Threats ◽  
Iot Security ◽  
Different Types ◽  
Different Dimensions ◽  
Iot Devices ◽  
Privacy Issues

IOT is wirelessly connecting things to the internet using sensors, RFID’s and remotely accessing and managing them over our phone or through our voice. IOT uses various communication protocols such as Zigbee, 6LowPan, Bluetooth and has bi directional communication for exchange of information. The database for IOT is cloud which is also vulnerable to security threats. The increasing amount of popularity of IoT and its pervasive usage has made it more recurrent to prominent cyber-attacks such as botnet attack, IoT ransom ware, DOS attack, RFID hack. The challenges faced by IoT are to stop hackers from stealing data, having unattended access to the device and performing malicious activities. There are many techniques which can be used to secure IoT devices such as using a secure encrypted Wi-Fi network, using digital signature for authenticity, updating to latest patches, installing Intrusion Detection System. We’ll also be assessing various IoT devices and threats associated with them in real time environment and the level of harm these threats can cause to the device if they are not properly mitigated or eradicated. In this paper we’ll also be addressing different types of risks associated with different IOT devices and approaches to solve the security and privacy issues

scholarly journals A Host-Based Anomaly Detection Framework Using XGBoost and LSTM for IoT Devices

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Xiali Wang ◽  
Xiang Lu
Keyword(s):  
Anomaly Detection ◽  
Detection System ◽  
Gradient Boosting ◽  
System Call ◽  
Iot Security ◽  
Extreme Gradient Boosting ◽  
Ip Camera ◽  
Iot Devices ◽  
System Call Sequences ◽  
Abnormal Behaviors

The Internet of Things (IoT) is rapidly spreading in various application scenarios through its salient features in ubiquitous device connections, ranging from agriculture and industry to transportation and other fields. As the increasing spread of IoT applications, IoT security is gradually becoming one of the most significant issues to guard IoT devices against various cybersecurity threats. Usually, IoT devices are the main components responsible for sensing, computing, and transmitting; in this case, how to efficiently protect the IoT device itself away from cyber attacks, like malware, virus, and worm, becomes the vital point in IoT security. This paper presents a brand new architecture of intrusion detection system (IDS) for IoT devices, which is designed to identify device- or host-oriented attacks in a lightweight manner in consideration of limited computation resources on IoT devices. To this end, in this paper, we propose a stacking model to couple the Extreme Gradient Boosting (XGBoost) model and the Long Short-Term Memory (LSTM) model together for the abnormal state analysis on the IoT devices. More specifically, we adopt the system call sequence as the indicators of abnormal behaviors. The collected system call sequences are firstly processed by the famous n-gram model, which is a common method used for host-based intrusion detections. Then, the proposed stacking model is used to identify abnormal behaviors hidden in the system call sequences. To evaluate the performance of the proposed model, we establish a real-setting IP camera system and place several typical IoT attacks on the victim IP camera. Extensive experimental evaluations show that the stacking model has outperformed other existing anomaly detection solutions, and we are able to achieve a 0.983 AUC score in real-world data. Numerical testing demonstrates that the XGBoost-LSTM stacking model has excellent performance, stability, and the ability of generalization.

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