A Novel Negative and Positive Selection Algorithm to Detect Unknown Malware in the IoT

Alrubayyi, Hadeel; Goteng, Gokop; Jaber, Mona; Kelly, James G.

doi:10.1109/infocomwkshps51825.2021.9484483

Cited by 9 publications

(17 citation statements)

References 19 publications

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“…The challenge of adapting cybersecurity measures to the dynamic and evolving nature of hospital networks has been a focal point of recent research [21,22,23]. Studies have particularly concentrated on developing adaptive algorithms capable of accommodating frequent changes in network configurations, such as the addition or removal of medical devices and applications [24,8]. Researchers have employed a variety of statistical methods and advanced machine learning techniques to enhance the detection of anomalies in real-time [25,26,27].…”

Section: Anomaly Detection Techniques In Dynamic Networkmentioning

confidence: 99%

Novel Detection of Hospital Malware Using Network Pattern Analysis

郝品量,

Liu,

Wang

2024

Preprint

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Advancements in machine learning offer potent solutions for cybersecurity, particularly in sensitive environments like hospital networks where the integrity and availability of data are paramount. In response to the escalating sophistication of cyber threats, a novel system utilizing adaptive machine learning algorithms was developed to detect malware with enhanced accuracy and responsiveness. This system demonstrated a notable capability to identify various types of malware, including zero-day attacks, achieving a detection accuracy of 92%. Comparative evaluations showed that this system significantly outperforms traditional signature-based systems and other contemporary machine learning-based approaches, which typically show lower accuracy and adaptability. The study revealed the importance of integrating real-time data analysis to adaptively refine detection algorithms, allowing the system to effectively respond to evolving threats, which not only enhances the security of hospital networks but also supports the broader application of intelligent, predictive cybersecurity measures in healthcare. Looking ahead, future work will focus on expanding the dataset to include more varied network conditions, integrating more diverse data sources, and employing federated learning techniques. These enhancements aim to improve scalability, enhance data privacy, and foster a collaborative security framework among healthcare institutions, further strengthening defenses against an ever-evolving landscape of cyber threats.

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Section: Anomaly Detection Techniques In Dynamic Networkmentioning

confidence: 99%

Novel Detection of Hospital Malware Using Network Pattern Analysis

郝品量,

Liu,

Wang

2024

Preprint

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“…Besides, Yu et al [ 43 ] suggested a home security smart analytics system utilizing a home router that classifies and identifies malware based on protocol, address, and main content of packets as features. Alrubayyi et al [ 44 ] designed an algorithm based on forward and backward selection techniques. The algorithm used 16-bit strings generated from target port-based network traffic as features and matched them with existing features to detect malware.…”

Section: Related Workmentioning

confidence: 99%

MDABP: A Novel Approach to Detect Cross-Architecture IoT Malware Based on PaaS

Zhao

Kuerban

2023

Sensors

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With the development of internet technology, the Internet of Things (IoT) has been widely used in several aspects of human life. However, IoT devices are becoming more vulnerable to malware attacks due to their limited computational resources and the manufacturers’ inability to update the firmware on time. As IoT devices are increasing rapidly, their security must classify malicious software accurately; however, current IoT malware classification methods cannot detect cross-architecture IoT malware using system calls in a particular operating system as the only class of dynamic features. To address these issues, this paper proposes an IoT malware detection approach based on PaaS (Platform as a Service), which detects cross-architecture IoT malware by intercepting system calls generated by virtual machines in the host operating system acting as dynamic features and using the K Nearest Neighbors (KNN) classification model. A comprehensive evaluation using a 1719 sample dataset containing ARM and X86-32 architectures demonstrated that MDABP achieves 97.18% average accuracy and a 99.01% recall rate in detecting samples in an Executable and Linkable Format (ELF). Compared with the best cross-architecture detection method that uses network traffic as a unique type of dynamic feature with an accuracy of 94.5%, practical results reveal that our method uses fewer features and has higher accuracy.

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“…There are multiple methods to combat/detect malware attacks but these are often too complex to run on lightweight IoT devices (Alrubayyi et al, 2021a). An artificial immune system method for malware detection is proposed in (Alrubayyi et al, 2021b) which is highly effective and suitable for Intelligent IoT systems as it has low complexity and the ability to detect unseen malware. However, malware detection does not address the risk of manin-the-middle, a security breach that targets the communication network for eavesdropping or altering the transmitted data; in this context, private/personal data.…”

Section: Challenges Of Meeting Multi-objective Sdgsmentioning

confidence: 99%

IoT and machine learning for enabling sustainable development goals

Jaber

2023

Front. Comms. Net.

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The United Nations drafted an agenda for 2030 to achieve sustainable development with 17 well-defined goals which are an urgent call for action requiring collaboration and innovation across countries and organisations. The year 2023 marks the midpoint toward fulfilling the proposed agenda but the world is still behind in attaining any of the set goals. This article discusses the opportunities offered by the rise of the Internet of Things and advances in artificial intelligence in ushering these goals with tangible use cases that relate to health, energy, and cities. Moreover, the article exposes the challenges that emerge from applying these technologies in the realisation of sustainable development goals (SDG) with underlined risks related to bias, security, data privacy, and the multi-objective optimisation of often competing SDGs.

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A Novel Negative and Positive Selection Algorithm to Detect Unknown Malware in the IoT

Cited by 9 publications

References 19 publications

Novel Detection of Hospital Malware Using Network Pattern Analysis

Novel Detection of Hospital Malware Using Network Pattern Analysis

MDABP: A Novel Approach to Detect Cross-Architecture IoT Malware Based on PaaS

IoT and machine learning for enabling sustainable development goals

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