scholarly journals Different Dimensions of IOT Security

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

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 567
Author(s):  
Muhammad Husnain ◽  
Khizar Hayat ◽  
Enrico Cambiaso ◽  
Ubaid U. Fayyaz ◽  
Maurizio Mongelli ◽  
...  

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.


2021 ◽  
Author(s):  
Priyanka Gupta ◽  
Lokesh Yadav ◽  
Deepak Singh Tomar

The Internet of Things (IoT) connects billions of interconnected devices that can exchange information with each other with minimal user intervention. The goal of IoT to become accessible to anyone, anytime, and anywhere. IoT has engaged in multiple fields, including education, healthcare, businesses, and smart home. Security and privacy issues have been significant obstacles to the widespread adoption of IoT. IoT devices cannot be entirely secure from threats; detecting attacks in real-time is essential for securing devices. In the real-time communication domain and especially in IoT, security and protection are the major issues. The resource-constrained nature of IoT devices makes traditional security techniques difficult. In this paper, the research work carried out in IoT Intrusion Detection System is presented. The Machine learning methods are explored to provide an effective security solution for IoT Intrusion Detection systems. Then discussed the advantages and disadvantages of the selected methodology. Further, the datasets used in IoT security are also discussed. Finally, the examination of the open issues and directions for future trends are also provided.


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 .


2018 ◽  
Vol 7 (2.19) ◽  
pp. 26
Author(s):  
C Bala Murugan ◽  
S Koteeswaran

IoT technology and applications represents security as a significant issue for facilitating the tremendous implementation.     Devoid of IoTs technology ensures the device level confidentiality, privacy and authenticity. The applicable users are not going to     undertake answers for security in IoT in huge scale. The earlier stage deployments of IoT devices are primarily based on RFIDs         technology which results in simplest, security solutions inside the principal been devised in an advert hoc manner [8]. This brings the fact that such deployments were typically vertically incorporated, with all additives beneath the manage of a single administrative entity. In the angle of an IoT eco-system, in which unique person may be worried in a given software state of affairs. One person owing the      physical operations of  sensors, one stakeholder deals with the statistics and processing them and other numerous stakeholders supplies different services based totally on such statistics to the customers. This leads to numerous variety of safety demanding situations and security for the IoT. In this paper, we address the revisited security issues and discuss the critical safety protection conditions of Internet of Things era into a mainstream. To support this, the three key problems requiring cutting-edge techniques includes are data               confidentiality, privacy and trust.In this review, we presented net factors with architectural design goals of IoT. We surveyed security and privacy issues in IoTs. Also the discussion on several open issues based on the privacy and security is addressed. Many real time applications of IoTs in real life treats the security issues of IoT as a main factor. Thus the IoT of complicated security issues have been anticipated the researchers to address. 


Author(s):  
Awad Saad Al-Qahtani, Mohammad Ayoub Khan Awad Saad Al-Qahtani, Mohammad Ayoub Khan

The Internet of things (IOT) users lack awareness of IOT security infrastructure to handle the risks including Threats, attack and penetration associated with its use. IOT devices are main targets for cyber-attacks due to variable personally identifiable information (PII) stored and transmit in the cyber centers. The security risks of the Internet of Things aimed to damage user's security and privacy. All information about users can be collected from their related objects which are stored in the system or transferred through mediums among diverse smart objects and may exposed to exposed dangerous of attacks and threats if it lack authentication so there are essential need to make IOT security requirements as important part of its efficient implementation. These requirements include; availability, accountability, authentication, authorization, privacy and confidentiality, Integrity and Non-repudiation. The study design is a survey research to investigate the visibility of the proposed model of security management for IOT uses, the security risks of IOT devices, and the changes IOT technology on the IT infrastructure of IOT users through answering of the research questionnaires. This work proposes a model of security management for IOT to predict IOT security and privacy threats, protect IOT users from any unforeseen dangers, and determine the right security mechanisms and protocols for IOT security layers, as well as give the most convenient security mechanisms. Moreover, for enhancing the performance of IOT networks by selecting suitable security mechanisms for IOT layers to increase IOT user's security satisfaction.


Computing ◽  
2021 ◽  
Author(s):  
Sungmoon Kwon ◽  
Seongmin Park ◽  
HyungJin Cho ◽  
Youngkwon Park ◽  
Dowon Kim ◽  
...  

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.


Author(s):  
Mingfu Huang ◽  
Rushit Dave ◽  
Nyle Siddiqui ◽  
Naeem Seliya

A fully automated, self-driving car can perceive its environment, determine the optimal route, and drive unaided by human intervention for the entire journey. Connected autonomous vehicles (CAVs) have the potential to drastically reduce accidents, travel time, and the environmental impact of road travel. Such technology includes the use of several sensors, various algorithms, interconnected network connections, and multiple auxiliary systems. CAVs have been subjected to attacks by malicious users to gain/deny control of one or more of its various systems. Data security and data privacy is one such area of CAVs that has been targeted via different types of attacks. The scope of this study is to present a good background knowledge of issues pertaining to different attacks in the context of data security and privacy, as well present a detailed review and analysis of eight very recent studies on the broad topic of security and privacy related attacks. Methodologies including Blockchain, Named Data Networking, Intrusion Detection System, Cognitive Engine, Adversarial Objects, and others have been investigated in the literature and problem- and context-specific models have been proposed by their respective authors.


10.28945/4675 ◽  
2021 ◽  
Vol 16 ◽  
pp. 001-038
Author(s):  
Anshul Jain ◽  
Tanya Singh ◽  
Satyendra Kumar Sharma ◽  
Vikas Prajapati

Aim/Purpose: 5G and IoT are two path-breaking technologies, and they are like wall and climbers, where IoT as a climber is growing tremendously, taking the support of 5G as a wall. The main challenge that emerges here is to secure the ecosystem created by the collaboration of 5G and IoT, which consists of a network, users, endpoints, devices, and data. Other than underlying and hereditary security issues, they bring many Zero-day vulnerabilities, which always pose a risk. This paper proposes a security solution using network slicing, where each slice serves customers with different problems. Background: 5G and IoT are a combination of technology that will enhance the user experience and add many security issues to existing ones like DDoS, DoS. This paper aims to solve some of these problems by using network slicing and implementing an Intrusion Detection System to identify and isolate the compromised resources. Methodology: This paper proposes a 5G-IoT architecture using network slicing. Research here is an advancement to our previous implementation, a Python-based software divided into five different modules. This paper’s amplification includes induction of security using pattern matching intrusion detection methods and conducting tests in five different scenarios, with 1000 up to 5000 devices in different security modes. This enhancement in security helps differentiate and isolate attacks on IoT endpoints, base stations, and slices. Contribution: Network slicing is a known security technique; we have used it as a platform and developed a solution to host IoT devices with peculiar requirements and enhance their security by identifying intruders. This paper gives a different solution for implementing security while using slicing technology. Findings: The study entails and simulates how the IoT ecosystem can be variedly deployed on 5G networks using network slicing for different types of IoT devices and users. Simulation done in this research proves that the suggested architecture can be successfully implemented on IoT users with peculiar requirements in a network slicing environment. Recommendations for Practitioners: Practitioners can implement this solution in any live or production IoT environment to enhance security. This solution helps them get a cost-effective method for deploying IoT devices on a 5G network, which would otherwise have been an expensive technology to implement. Recommendation for Researchers: Researchers can enhance the simulations by amplifying the different types of IoT devices on varied hardware. They can even perform the simulation on a real network to unearth the actual impact. Impact on Society: This research provides an affordable and modest solution for securing the IoT ecosystem on a 5G network using network slicing technology, which will eventually benefit society as an end-user. This research can be of great assistance to all those working towards implementing security in IoT ecosystems. Future Research: All the configuration and slicing resources allocation done in this research was performed manually; it can be automated to improve accuracy and results. Our future direction will include machine learning techniques to make this application and intrusion detection more intelligent and advanced. This simulation can be combined and performed with smart network devices to obtain more varied results. A proof-of-concept system can be implemented on a real 5G network to amplify the concept further.


2021 ◽  
Author(s):  
Navroop Kaur ◽  
Meenakshi Bansal ◽  
Sukhwinder Singh S

Abstract In modern times the firewall and antivirus packages are not good enough to protect the organization from numerous cyber attacks. Computer IDS (Intrusion Detection System) is a crucial aspect that contributes to the success of an organization. IDS is a software application responsible for scanning organization networks for suspicious activities and policy rupturing. IDS ensures the secure and reliable functioning of the network within an organization. IDS underwent huge transformations since its origin to cope up with the advancing computer crimes. The primary motive of IDS has been to augment the competence of detecting the attacks without endangering the performance of the network. The research paper elaborates on different types and different functions performed by the IDS. The NSL KDD dataset has been considered for training and testing. The seven prominent classifiers LR (Logistic Regression), NB (Naïve Bayes), DT (Decision Tree), AB (AdaBoost), RF (Random Forest), kNN (k Nearest Neighbor), and SVM (Support Vector Machine) have been studied along with their pros and cons and the feature selection have been imposed to enhance the reading of performance evaluation parameters (Accuracy, Precision, Recall, and F1Score). The paper elaborates a detailed flowchart and algorithm depicting the procedure to perform feature selection using XGB (Extreme Gradient Booster) for four categories of attacks: DoS (Denial of Service), Probe, R2L (Remote to Local Attack), and U2R (User to Root Attack). The selected features have been ranked as per their occurrence. The implementation have been conducted at five different ratios of 60-40%, 70-30%, 90-10%, 50-50%, and 80-20%. Different classifiers scored best for different performance evaluation parameters at different ratios. NB scored with the best Accuracy and Recall values. DT and RF consistently performed with high accuracy. NB, SVM, and kNN achieved good F1Score.


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