APAE: an IoT intrusion detection system using asymmetric parallel auto-encoder

Basati, Amir; Faghih, Mohammad Mehdi

doi:10.1007/s00521-021-06011-9

Cited by 27 publications

(5 citation statements)

References 34 publications

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“…• Secure coding practices for IoT devices and applications: Following secure coding guidelines and best practices is essential to minimize the introduction of vulnerabilities during the development process. It includes practices such as input validation [79], output encoding [80], and proper handling of user input to prevent common attack vectors such as injection attacks or cross-site scripting [81]. [83] to identify weaknesses and potential entry points for attackers.…”

Section: B Secure Software Developmentmentioning

confidence: 99%

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Securing the Internet of Things in Artificial Intelligence Era: A Comprehensive Survey

Humayun,

Tariq,

Alfayad

et al. 2024

IEEE Access

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The Internet of Things (IoT) has revolutionized various domains, enabling interconnected devices to communicate and exchange data. The integration of Artificial Intelligence (AI) in IoT systems further enhances their capabilities and potential benefits. Unfortunately, in the era of AI, ensuring the privacy and security of the IoT faces novel and specific challenges. IoT security is imperative, necessitating comprehensive strategies, including comprehension of IoT security challenges, implementation of AI methodologies, adoption of resilient security frameworks, and handling of privacy and ethical concerns to construct dependable and secure IoT systems. It is vital to note that the term 'security' encompasses a more comprehensive view than cyberattacks alone. Therefore, with an emphasis on securing against cyberattacks, this comprehensive survey also includes physical security threats on the IoT. It investigates the complexities and solutions for IoT systems, placing particular emphasis on AI-based security techniques. The paper undertakes a categorization of the challenges associated with ensuring IoT security, investigates the utilization of AI in IoT security, presents security frameworks and strategies, underscores privacy and ethical considerations, and provides insights derived from practical case studies. Furthermore, the survey sheds light on emerging trends concerning IoT security in the AI era. This survey provides significant contributions to the understanding of establishing dependable and secure IoT systems through an exhaustive examination of the present condition of IoT security and the ramifications of AI on it.

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Section: B Secure Software Developmentmentioning

confidence: 99%

“…• Enhanced protection against various attack vectors • Comprehensive approach to IoT security • Strong foundation for device security [26], [79], [80], [83]…”

Section: Ref Security Framework and Approachesmentioning

confidence: 99%

Securing the Internet of Things in Artificial Intelligence Era: A Comprehensive Survey

Humayun,

Tariq,

Alfayad

et al. 2024

IEEE Access

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“…Based on the findings of the paper, it appears that it would be possible to detect both short-term attacks and more intense ones. Basati and Faghih's approach [96] proposes a real-time network intrusion detection system that employs two AEs in parallel to monitor the network and a feature reduction deep AE to identify the most distinct features.…”

Section: Other Security Applicationsmentioning

confidence: 99%

IoT anomaly detection methods and applications: A survey

2022

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“…Also considered were data from GPRS, NSL-KDD, and UNSW-NB15. is classifier is put up against others like Multilayer Perceptrons [28], NBTrees [29], a Random Tree ensemble [30], and Nave Bayes [31]. Study indicated that random forest-based IDSs beat other classifiers in terms of performance.…”

Section: Literature Surveymentioning

confidence: 99%

A Novel Intelligent-Based Intrusion Detection System Approach Using Deep Multilayer Classification

Ugendhar

Illuri²,

Vulapula

et al. 2022

Mathematical Problems in Engineering

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Cybersecurity in information technology (IT) infrastructures is one of the most significant and complex issues of the digital era. Increases in network size and associated data have directly affected technological breakthroughs in the Internet and communication areas. Malware attacks are becoming increasingly sophisticated and hazardous as technology advances, making it difficult to detect an incursion. Detecting and mitigating these threats is a significant issue for standard analytic methods. Furthermore, the attackers use complex processes to remain undetected for an extended period. The changing nature and many cyberattacks require a quick, adaptable, and scalable defense system. For the most part, traditional machine learning-based intrusion detection relies on only one algorithm to identify intrusions, which has a low detection rate and cannot handle large amounts of data. To enhance the performance of intrusion detection systems, a new deep multilayer classification approach is developed. This approach comprises five modules: preprocessing, autoencoding, database, classification, and feedback. The classification module uses an autoencoder to decrease the number of dimensions in a reconstruction feature. Our method was tested against a benchmark dataset, NSL-KDD. Compared to other state-of-the-art intrusion detection systems, our methodology has a 96.7% accuracy.

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APAE: an IoT intrusion detection system using asymmetric parallel auto-encoder

Cited by 27 publications

References 34 publications

Securing the Internet of Things in Artificial Intelligence Era: A Comprehensive Survey

Securing the Internet of Things in Artificial Intelligence Era: A Comprehensive Survey

IoT anomaly detection methods and applications: A survey

A Novel Intelligent-Based Intrusion Detection System Approach Using Deep Multilayer Classification

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