Safeguarding Internet of Things (IoT) devices has arisen as a significant security challenge, particularly in smart homes and e-healthcare systems. Firewall devices are susceptible to cyber-attacks. This is because of their limited processing power and lack of other Safeguarding features (Franco, 2370). They have become a vital security measure to safeguard Internet of Things (IoT) devices and networks from reaching unauthorized hands, data breaches, and other safety risks. The primary essential function of a firewall is to serve as a barrier between a private internal network and the public internet, which enables a network not accessed by outsiders to filter and block incoming and outgoing traffic to prevent unwanted access. For IoT devices, firewalls serve as the first line of defense, protecting them from various potential security intimidations. “The Internet of Things (IoT) has brought many benefits to our daily lives, but security concerns have also risen with the increasing number of connected devices. Firewalls have been widely used in traditional networks to protect against malicious attacks, and their application in IoT can enhance security. This thesis aims to investigate firewall effectiveness in IoT, including the benefits and limitations. By analyzing different types of IoT firewalls, this thesis aims to provide recommendations for implementing a secure and effective firewall for IoT devices.”
“Security Challenges in Adopting Internet of Things for Smart Network” by Gupta et., al 2021.
This article by Gupta discusses IoT (Internet of Things) gadgets proliferating in smart homes, raising security and privacy concerns. New research suggests a firewall solution for IoT devices in smart homes to address this issue (Gupta). The proposed remedy monitors network activity and stops suspicious packets to prevent attacks from entering the system. To identify unusual traffic, the firewall system leverages machine learning techniques and learns from prior attacks to increase accuracy. This implies that the system can change its rules to respond to new threats. Network traffic is analyzed by machine learning algorithms, which compare it to a baseline of typical traffic patterns. When traffic deviates from the baseline, the system marks it as suspicious and stops it. The firewall system also can send users immediate alerts about potential threats, enabling them to take appropriate action. Email, SMS, and other notification channels are all acceptable signal transmission methods. The proposed firewall solution is specifically developed for IoT devices in smart homes and can be readily integrated into existing networks. It provides an additional layer of security that complements existing security measures, such as encryption and authentication.
In addition, the article discusses Safeguarding IoT devices in smart homes by designing a lightweight and energy-efficient anomaly detection-based firewall. Firewalls use machine learning algorithms to detect and prevent cyber-attacks on IoT devices (Huma). The machine-learning model is tested using a real-world attack dataset that evaluates the approach’s effectiveness. The experimental results demonstrate that the proposed firewall achieves high accuracy in detecting and preventing attacks while consuming minimal resources. Designing energy-efficient and lightweight security solutions for IoT devices often requires more processing power and battery life. The proposed firewall can be used as an additional layer of security in smart homes to protect IoT devices from cyber threats and provide a secure environment for users.
“Developing a Model of Cloud Computing Protection System for the Internet of Things” by 0lexander et al., 2020
The journal by Olexander focuses on the growth of IoT devices which has led to security concerns that have become a significant challenge in developing and deploying innovative home applications. The potential for unauthorized access and data fissures have posed an extensive threat to the privacy and safety of savvy home users (Belej). Therefore, a lightweight and energy-efficient security guard (firewall) is vital for supporting low-powered IoT devices and providing adequate protection against such threats. Firewall design that utilizes Network Address Translation (NAT) and Application Layer Gateway (ALG) techniques enhances the security of IoT nodes in smart homes.
In addition, the journal focuses on the approach that contains significant advantages over traditional centralized firewalling. The distributed firewalling approach enhances security by ensuring enough security checkpoints within the network, making it harder for attackers to penetrate the system (Belej). In addition, the scheme policy mechanism enhances user control and customization, allowing them to tailor their security settings to their unique requirements. The hybrid firewall solution for IoT devices in healthcare systems addresses this issue. The approach combines the benefits of rule-based and machine-learning techniques, improving the firewall’s effectiveness. Rule-based methods are used to detect known threats, while machine learning techniques are used to identify unknown threats. In machine learning, the firewall can learn from preceding attacks and familiarize itself with new ones, provided that there is better protection against evolving threats. In addition, the hybrid approach helps to reduce false positives, a significant issue in rule-based systems. The proposed hybrid firewall solution can offer a more comprehensive approach to protecting healthcare networks from cyber threats.
“A segregated architecture for a trust-based network of Internet of things” by Davide et al., 2019
This paper presents that the security guard can prohibit unwanted access to specific devices by masking the IP addresses of the devices behind a single IP address using the system address translation approach. The firewall can analyze and manage traffic between devices and the internet thanks to the application layer gateway approach, adding another layer of defense against threats (Ferraris). A proposed firewall’s security and energy efficiency metric is used to assess its efficacy. The outcomes show that the firewall is an excellent choice for low-powered IoT devices in smart homes since it prevents outsiders’ unwanted access to a network and consumes very little energy. The lightweight construction also lessens the processing burden on the devices, which has little effect on performance.
The article also discusses the propagation of internet-connected devices, which have brought convenience and comfort to people’s homes. However, it has also created new security challenges that firewalls can safeguard, ensuring the network is protected from unauthorized access. Smart homes are particularly vulnerable to cyber-attacks due to many devices that can be easily compromised (Davide). In response, the distribution of firewalling approach enhances the security of smart homes. The proposed process involves the deployment of multiple firewalls at different locations within the home network, creating a defense-in-depth security model. Each firewall is responsible for a specific network segment and provides comprehensive protection. The new mechanism for managing firewall rules that allow users to define policies is based on contextual information. For instance, users can set rules applied during specific times to particular IoT devices.
“A cognitive protection system for the Internet of things.” By Siegel et al., 2019
The article by Siegel discusses a new approach to securing e-healthcare systems using a firewall that utilizes machine learning techniques. The firewall is designed to be adaptable to new and evolving types of cyber-attacks, which is crucial for the constantly changing cybersecurity landscape (Siegel, 45). The authors used a dataset of attacks to train the machine learning algorithm. This approach ensures that the firewall can recognize and respond to real-world threats rather than rely solely on hypothetical scenarios. The authors also tested the accuracy of the firewall and found that it is highly effective in detecting and preventing attacks, with minimal false alarms. The use of machine learning in cybersecurity is becoming increasingly popular as it allows for creating of more advanced and sophisticated security measures. By analyzing vast amounts of data, machine learning procedures can recognize designs and anomalies that would be difficult or impossible for humans to detect. E-healthcare systems are particularly vulnerable to cyber threats due to the sensitive nature of the information they handle. Implementing a firewall that uses machine learning techniques is a significant step towards enhancing the security of these systems.
The article also presents a novel approach to IoT security in e-healthcare systems by proposing a machine learning-based firewall that uses supervised and unsupervised learning algorithms. The hybrid approach aims to improve the firewall’s effectiveness by combining the strengths of both algorithms (Joshua, 47). The proposed firewall is designed to be adaptive and capable of detecting new and unknown threats, essential for e-healthcare systems that handle sensitive patient data. The authors conducted experiments to evaluate the firewall’s performance and compared its security and accuracy with other state-of-the-art approaches. The results showed that the machine learning-based firewall protects e-healthcare systems from cyber-attacks while maintaining low false-positive rates. The hybrid approach was superior to using only supervised or unsupervised learning algorithms.
The IoT has transformed how we interrelate with everyday devices in our homes, from controlling the temperature and lighting to monitoring home security systems. However, with this increased connectivity comes increased vulnerability to cyber threats (Siegel, 45). Malicious actors can easily access sensitive information or take control of IoT devices, posing a severe threat to the security and privacy of smart homes. “A cognitive protection system for the internet of things.” Siegel et al., 2019 address the issue and propose a blockchain-based firewall solution. This solution utilizes a distributed ledger to store network policies and blocks malicious traffic, ensuring that only authorized devices can access the network. Smart contracts enforce these policies, which are executed automatically without human intervention.
“A survey of honeypots and honeynets for the internet of things, industrial internet of things, and cyber-physical systems.” By Javier et al., 2021
The article by Javier discusses that, in current years, the growth of IoT nodes has raised the danger of security attacks on networks. To address this issue, the proposed architecture of an IoT network distributed firewall distributes firewall functionality to multiple IoT devices (Franco 2370). This approach to securing IoT networks is more resilient and flexible, overcoming the limitations of traditional centralized firewalls, which can suffer from single points of failure and performance bottlenecks. The distributed firewall architecture suggested in this work has been tested to establish its usefulness in security and scalability. The results demonstrate that the architecture can secure IoT devices and networks against threats and scale to massive IoT deployments. The distributed architecture of the firewall, in particular, enables faster detection and reaction to possible security threats, boosting the network’s overall security posture.
The article by Javier also presents the use of blockchain technology provides several advantages over traditional firewall solutions. Firstly, the distributed nature of the blockchain ensures that network policies are stored securely and cannot be tampered with by any single entity (Javier, 2374). Additionally, smart contracts provide guidelines that are enforced transparently and objectively without a central authority. The increasing use of IoT devices in healthcare systems has raised concerns about the security of sensitive patient data. Firewalls are essential to any security system, as they are a barrier between the internal network and external threats. However, more than traditional firewalls may be required to protect against the growing number of sophisticated cyber-attacks.
The prevalence of innovative home technology has led to growing concerns over the security of IoT devices. To address these concerns, a dynamic firewall rule management approach uses machine learning techniques to analyze network traffic and automatically generate firewall rules (Javier, 2380). The strategy aims to provide better safety for smart homes while reducing the overhead of managing firewall rules. The proposed method involves monitoring network traffic in real-time using machine learning algorithms. The algorithms analyze traffic patterns and identify potential security threats. Based on this analysis, the approach automatically generates and updates firewall rules to protect the smart home from these threats. The dynamic nature of the system ensures that the firewall rules are always up-to-date and can adapt to changing network conditions. The effectiveness is evaluated using real-world smart home traffic data. The results show that the dynamic firewall rule management approach provides better security than traditional rule-based approaches. The method detected and blocked malicious traffic while allowing legitimate traffic to pass through. Additionally, the system reduced the overhead of managing firewall rules, as the automated generation and updating of regulations eliminated the need for manual rule management.
In conclusion, the propagation of Internet of Things (IoT) devices in smart homes and e-healthcare systems has raised significant safeguarding concerns. Internet of Things devices is highly vulnerable to cyber-attacks due to their limited computational power, resource constraints, and lack of robust security features. As a result, an urgent need is to develop an effective security solution to ensure that the network is protected from outsiders. Several researchers have highlighted the importance of firewalls for safeguarding local networks and IoT devices and proposed innovative firewall designs to address the unique security challenges of IoT devices. In addition, the researchers have emphasized machine learning algorithms, distributed architectures, and lightweight structures, which improve the security, scalability, and energy efficiency of Internet of Things devices. Machine learning algorithms develop intelligent firewalls that detect and prevent real-time attacks on IoT devices. Distributed firewall architectures are proposed to improve scalability and handle many IoT devices in smart homes and e-healthcare systems. Lightweight firewall designs are being developed to minimize IoT devices’ computational overhead and energy consumption.
Works Cited
Belej, Olexander, et al. “Developing a Model of Cloud Computing Protection System for the Internet of Things.” 2020 IEEE XVIth International Conference on the Perspective Technologies and Methods in MEMS Design (MEMSTECH). IEEE, 2020.
Ferraris, Davide, et al. “A segregated architecture for a trust-based network of internet of things.” 2019 16th IEEE Annual Consumer Communications & Networking Conference (CCNC). IEEE, 2019.
Franco, Javier, et al. “A survey of honeypots and honeynets for the internet of things, industrial internet of things, and cyber-physical systems.” IEEE Communications Surveys & Tutorials 23.4 2021: 2351–2383.
Gupta, Huma, and Sanjeev Sharma. “Security Challenges in Adopting Internet of Things for Smart Network.” 2021 10th IEEE International Conference on Communication Systems and Network Technologies (CSNT). IEEE, 2021.
Siegel, Joshua, and Sanjay Sarma. “A cognitive protection system for the internet of things.” IEEE Security & Privacy 17.3 2019: 40–48.
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