Cybersecurity Threats, Countermeasures and Mitigation Techniques on the IoT: Future Research Directions

Altulaihan, Esra; Almaiah, Mohammed Amin; Aljughaiman, Ahmed

doi:10.3390/electronics11203330

Cited by 29 publications

(23 citation statements)

References 64 publications

(101 reference statements)

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“…This paper builds upon our previous study about the threats to IoT, in which we found that DoS attacks pose the most threat to IoT networks and that detection techniques are required to address this challenge [ 9 ].…”

Section: Introductionmentioning

confidence: 94%

Anomaly Detection IDS for Detecting DoS Attacks in IoT Networks Based on Machine Learning Algorithms

Altulaihan,

Almaiah,

Aljughaiman

2024

Sensors

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Widespread and ever-increasing cybersecurity attacks against Internet of Things (IoT) systems are causing a wide range of problems for individuals and organizations. The IoT is self-configuring and open, making it vulnerable to insider and outsider attacks. In the IoT, devices are designed to self-configure, enabling them to connect to networks autonomously without extensive manual configuration. By using various protocols, technologies, and automated processes, self-configuring IoT devices are able to seamlessly connect to networks, discover services, and adapt their configurations without requiring manual intervention or setup. Users’ security and privacy may be compromised by attackers seeking to obtain access to their personal information, create monetary losses, and spy on them. A Denial of Service (DoS) attack is one of the most devastating attacks against IoT systems because it prevents legitimate users from accessing services. A cyberattack of this type can significantly damage IoT services and smart environment applications in an IoT network. As a result, securing IoT systems has become an increasingly significant concern. Therefore, in this study, we propose an IDS defense mechanism to improve the security of IoT networks against DoS attacks using anomaly detection and machine learning (ML). Anomaly detection is used in the proposed IDS to continuously monitor network traffic for deviations from normal profiles. For that purpose, we used four types of supervised classifier algorithms, namely, Decision Tree (DT), Random Forest (RF), K Nearest Neighbor (kNN), and Support Vector Machine (SVM). In addition, we utilized two types of feature selection algorithms, the Correlation-based Feature Selection (CFS) algorithm and the Genetic Algorithm (GA) and compared their performances. We also utilized the IoTID20 dataset, one of the most recent for detecting anomalous activity in IoT networks, to train our model. The best performances were obtained with DT and RF classifiers when they were trained with features selected by GA. However, other metrics, such as training and testing times, showed that DT was superior.

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Section: Introductionmentioning

confidence: 94%

Anomaly Detection IDS for Detecting DoS Attacks in IoT Networks Based on Machine Learning Algorithms

Altulaihan,

Almaiah,

Aljughaiman

2024

Sensors

Self Cite

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“…al. [8] study that looks at the dangers and threats to devices connected to the internet, like smartwatches, home security systems, and smart fridges. This systematic review examines different ways to understand and manage these risks to keep our devices and information safe.…”

Section: Related Workmentioning

confidence: 99%

A Systematic Literature Review: AI, DL and Machine Learning inCyber Risk Management

Hamid,

Rahman

2024

Preprint

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The integration of AI and ML in both climate change and cyber risk management has significantly bolstered their effectiveness. These advanced technologies enable organizations to handle extensive datasets, whether related to climate patterns or cyber activities, with greater efficiency and making more informed decisions. AI and machine learning are utilized in cyber security to predict potential threats, automate tasks, and manage cyber security risks. Ensuring the accuracy of information, comprehending the functionality of the model, and safeguarding individuals' data pose challenges. It is very important to use personal information ethically and protect it. Researchers should prioritize accuracy in the information utilized, clarity in AI models, and fairness in AI decision-making in the future. New technologies like edge computing, federated learning, explainable AI, and quantum computing are changing the way we manage risk and keep our digital information safe. These new advancements provide opportunities to gain a better understanding of risks, identify threats more rapidly, and improve decision-making. Still, there are challenges concerning data accuracy, understanding model operations, and privacy issues that must be resolved before these technologies can effectively address cyber risks.

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“…This prevents legitimate users from accessing the services or resources they need. Attackers may use various techniques, including SYN flood attacks, where a surge of SYN requests is sent without completing the handshake with ACK responses, or ICMP flooding, which overwhelms the target with ping requests [62], [63]. By exploiting vulnerabilities inherent in the way TCP/IP protocols operate, DoS attacks can cause significant disruptions, making the mitigation and prevention of such attacks a critical concern for network administrators and security professionals.…”

Section: Figure 3 Dos Attackmentioning

confidence: 99%

Performance, privacy and security issues at the TCP internet layer

Ogweno Jeremiah okeyo

2024

World J. Adv. Res. Rev.

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The rapid growth of internet-based services and applications has revolutionized the way we communicate, interact, and conduct business. However, this widespread connectivity also brings forth numerous challenges related to performance and privacy security at the internet layer. This paper aims to provide a comprehensive analysis of the various issues that arise in this context, highlighting their impact on user experience, data protection, and network efficiency, to address performance concerns; the research paper investigates the factors affecting internet Layer. Through extensive research studies, the paper examines the impact of network congestion, routing inefficiencies, and protocol limitations on the overall performance of internet-based services. This research delves into the vulnerabilities and risks associated with the internet layer. It investigates the potential threats posed by malicious actors, including hackers, data breaches, and unauthorized access to sensitive information. The findings of this research contribute to a comprehensive understanding of the interplay between performances and privacy security issues at the internet layer. By identifying the key challenges and potential solutions, this study provides valuable insights for network administrators, service providers, policymakers, and end-users to optimize performance while ensuring robust privacy and security measures. Ultimately, this research aims to foster a safer and more efficient internet ecosystem for all stakeholders.

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Cybersecurity Threats, Countermeasures and Mitigation Techniques on the IoT: Future Research Directions

Cited by 29 publications

References 64 publications

Anomaly Detection IDS for Detecting DoS Attacks in IoT Networks Based on Machine Learning Algorithms

Anomaly Detection IDS for Detecting DoS Attacks in IoT Networks Based on Machine Learning Algorithms

A Systematic Literature Review: AI, DL and Machine Learning inCyber Risk Management

Performance, privacy and security issues at the TCP internet layer

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