A study of IoT malware activities using association rule learning for darknet sensor data

Ozawa, Seiichi; Ban, Tao; Hashimoto, Naoki; Nakazato, Junji; Shimamura, Jumpei

doi:10.1007/s10207-019-00439-w

Cited by 30 publications

(17 citation statements)

References 12 publications

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“…Additionally, fuzzy association rules are employed to construct a rule-based intrusion detection system [70]. In [75], an FP-tree association rule-based study was carried out to investigate malware behaviors. A belief rule-based anomaly detection under uncertainty has been presented in [76].…”

Section: Rule-based Modeling In Cybersecuritymentioning

confidence: 99%

Machine Learning for Intelligent Data Analysis and Automation in Cybersecurity: Current and Future Prospects

Sarker

2022

Ann. Data. Sci.

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Due to the digitization and Internet of Things revolutions, the present electronic world has a wealth of cybersecurity data. Efficiently resolving cyber anomalies and attacks is becoming a growing concern in today’s cyber security industry all over the world. Traditional security solutions are insufficient to address contemporary security issues due to the rapid proliferation of many sorts of cyber-attacks and threats. Utilizing artificial intelligence knowledge, especially machine learning technology, is essential to providing a dynamically enhanced, automated, and up-to-date security system through analyzing security data. In this paper, we provide an extensive view of machine learning algorithms, emphasizing how they can be employed for intelligent data analysis and automation in cybersecurity through their potential to extract valuable insights from cyber data. We also explore a number of potential real-world use cases where data-driven intelligence, automation, and decision-making enable next-generation cyber protection that is more proactive than traditional approaches. The future prospects of machine learning in cybersecurity are eventually emphasized based on our study, along with relevant research directions. Overall, our goal is to explore not only the current state of machine learning and relevant methodologies but also their applicability for future cybersecurity breakthroughs.

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Section: Rule-based Modeling In Cybersecuritymentioning

confidence: 99%

Machine Learning for Intelligent Data Analysis and Automation in Cybersecurity: Current and Future Prospects

Sarker

2022

Ann. Data. Sci.

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“…Additionally, fuzzy association rules are employed to construct a rule-based intrusion detection system [118]. In [71], an FP-tree association rule-based study was carried out to investigate malware behaviors. A belief rule-based anomaly detection under uncertainty has been presented in [121].…”

Section: Rule-based Modeling In Cybersecuritymentioning

confidence: 99%

Machine Learning for Intelligent Data Analysis and Automation in Cybersecurity: Current and Future Prospects

Sarker¹

2022

Preprint

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Due to the digitization and Internet of Things revolutions, the present electronic world has a wealth of cybersecurity data. Efficiently resolving cyber anomalies and attacks is becoming a growing concern in today's cyber security industry all over the world. Traditional security solutions are insufficient to address contemporary security issues due to the rapid proliferation of many sorts of cyber-attacks and threats. Utilizing artificial intelligence knowledge, especially machine learning technology, is essential to providing a dynamically enhanced, automated, and up-to-date security system through analyzing security data. In this paper, we provide an extensive view of machine learning algorithms, emphasizing how they can be employed for intelligent data analysis and automation in cybersecurity through their potential to extract valuable insights from cyber data. We also explore a number of potential real-world use cases where data-driven intelligence, automation, and decision-making enable next-generation cyber protection that is more proactive than traditional approaches. The future prospects of machine learning in cybersecurity are eventually emphasized based on our study, along with relevant research directions. Overall, our goal is to explore not only the current state of machine learning and relevant methodologies but also their applicability for future cybersecurity breakthroughs.

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“…Saldırı IoT cihazlar aracılığıyla yapılmış en büyük siber saldırılardan biri olmuştur. 21 Ekim 2016'da Alan Adı Sistemi sağlayıcısı Dyn tarafından işletilen sistemleri hedef alan saldırının, Mirai kötü amaçlı yazılımından etkilenmiş yazıcılar, IP kameralar, ev ağ geçitleri ve bebek izleme monitörleri gibi internete bağlı çok sayıda cihazdan oluşan bir botnet aracılığıyla gerçekleştirildiği ifade edilmiştir [26]. Endüstri 4.0 alanında yaşanan siber güvenlik olayları oldukça fazladır.…”

Section: Si̇ber Güvenli̇kunclassified

The Forgotten Component in the Development Process of Industry 4.0: Cyber Security

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et al. 2021

Düzce Üniversitesi Bilim Ve Teknoloji Dergisi

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İnsanlık tarihi teknolojik açıdan önemli aşamalardan geçmiştir ve geçmeye devam etmektedir. Bu aşamaların en önemlilerinden birisi de Endüstri 4.0 sürecidir. Bu süreçle birlikte geleneksel sanayi üretimi yerini bilişim sistemlerinin ön planda olduğu bir yaklaşıma bırakmıştır. Endüstriyel haberleşme sistemleri ilk üretildikleri zamanlarda tamamen izole olarak yönetilmişlerdir. Zamanla iç ağlara (intranet) devamında ise internete bağlanmaları ile Endüstri 4.0 süreci başlamıştır. Bu süreçte siber-fiziksel sistemlerin ortaya çıkmasıyla birlikte, bu sistemler siber tehditlere karşı açık hale gelmişlerdir. Bu zafiyetler akıllı sistemleri ve Endüstri 4.0 sistemlerini saldırganların gözdesi haline getirmiştir. Her ne kadar bu sistemler verimlilik, sürat, işlerlik gibi insan hayatına önemli katkılar sağlasa da siber güvenlik açıklıkları uygun şekilde değerlendirilmezse, Endüstri 4.0'ın gerçek potansiyeline asla ulaşılamayabilir. Bu nedenle, çalışmada Endüstri 4.0'ın tüm bileşenleri ele alınarak, Siber Güvenlik bileşeni üzerine odaklanılmıştır. Çalışma, Endüstri 4.0'ın siber güvenlik boyutuna yönelik çalışmalara önemli katkı sağlayacaktır.

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A study of IoT malware activities using association rule learning for darknet sensor data

Cited by 30 publications

References 12 publications

Machine Learning for Intelligent Data Analysis and Automation in Cybersecurity: Current and Future Prospects

Machine Learning for Intelligent Data Analysis and Automation in Cybersecurity: Current and Future Prospects

Machine Learning for Intelligent Data Analysis and Automation in Cybersecurity: Current and Future Prospects

The Forgotten Component in the Development Process of Industry 4.0: Cyber Security

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