Implementing and Evaluating an Intrusion Detection System for Denial of Service Attacks in IoT Environments

Bamou, Abdelouahed; Khardioui, Mohamed; Ouadghiri, My Driss El; Aghoutane, Badraddine

doi:10.1007/978-3-030-33103-0_17

Cited by 5 publications

(4 citation statements)

References 10 publications

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“…These are networks of devices connected to the Internet and controlled by the attacker [10]. Due to the vast number of connected objects dispersed geographically, and their low levels of security, DDoS attacks are the most common in the IoT [11]. They can be carried out in several ways [3][10]:…”

Section: A Attacks and Threats In Iot Systemsmentioning

confidence: 99%

IDS Based on Machine Learning in Reaction to IoT Attacks: Review and Empirical Evaluation

Bamou¹,

Ouadghiri²,

Aghoutane³

et al. 2023

Int. J. Adv. Sci. Eng. Inf. Technol.

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Recently, connected objects have been the subject of cyber-attacks at an alarming rate. These devices connected to a vast volume data stream have insufficient resources and are not manually configured. Typically, attacks target the usability and exploitation of these vulnerabilities. These attacks make the mission of traditional intrusion detection (IDS) systems more challenging to limit intrusion threats. Machine learning (ML) can solve this problem, mainly since the Internet of Things (IoT) can collect and transfer massive amounts of data. This data is the essence of ML, enabling it to build security and privacy models which can predict or classify malicious nodes and network traffic in the IoT. This article looks at the more common forms of cyberattacks, which could lead to an IoT system failure, as well as a countermeasure capable of limiting their damage. First, we present a general review of IDS and these evaluation measures as a solution to limit these attacks. After reviewing the ML domain and these often-used algorithms, on which the IDS can be based to accomplish its mission, we examine the different datasets researchers use to form their IDS. Finally, we look at a practical example of using Python to evaluate ML methods on a current dataset (TON IoT). The research is based on previous research on the topic. The results enable us to choose the appropriate algorithms for the IDS to achieve the best binary and multi-classification based on the evaluation parameters.

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Section: A Attacks and Threats In Iot Systemsmentioning

confidence: 99%

IDS Based on Machine Learning in Reaction to IoT Attacks: Review and Empirical Evaluation

Bamou¹,

Ouadghiri²,

Aghoutane³

et al. 2023

Int. J. Adv. Sci. Eng. Inf. Technol.

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show abstract

“…IoT mempunyai kemampuan yang dapat menghubungkan berbagai obyek pintar untuk berinteraksi dengan lingkungannya maupun peralatan komputasi cerdas lainnya melalui jaringan internet [1]. Saat ini sensor IoT telah disematkan pada perangkat seluler, peralatan industri, lingkungan, perangkat medis, dan lain-lain [2][3][4]. Implementasi IoT dalam berbagai aspek kehidupan manusia telah memberikan banyak keuntungan dan kemudahan.…”

Section: Pendahuluanunclassified

Deteksi Serangan Denial of Service pada Internet of Things Menggunakan Finite-State Automata

Antony

Gustriansyah

2021

matrik

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Internet of things memiliki kemampuan untuk menghubungkan obyek pintar dan memungkinkan mereka untuk berinteraksi dengan lingkungan dan peralatan komputasi cerdas lainnya melalui jaringan internet. Namun belakangan ini, keamanan jaringan internet of things mendapat ancaman akibat serangan cyber yang dapat menembus perangkat internet of things target dengan menggunakan berbagai serangan denial of service. Penelitian ini bertujuan untuk mendeteksi dan mencegah serangan denial of service berupa synchronize flooding dan ping flooding pada jaringan internet of things dengan pendekatan finite-state automata. Hasil pengujian menunjukkan bahwa pendekatan finite-state automata berhasil mendeteksi serangan synchronize flooding dan ping flooding pada jaringan internet of things, tetapi pencegahan serangan tidak secara signifikan mengurangi penggunaan prosesor dan memori. Serangan synchronize flooding menyebabkan delay saat mengaktifkan/menonaktifkan peralatan internet of things sedangkan serangan ping flooding menyebabkan error. Implementasi bash-iptables berhasil mengurangi serangan synchronize flooding dengan efisiensi waktu pencegahan sebesar 55,37% dan mengurangi serangan ping flooding sebesar 60% tetapi dengan waktu yang tidak signifikan.

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“…Several IDSs have been introduced to secure IoT systems against different attacks. For example, effective detection of the Denial of Service (DoS) attack was addressed in [1][2][3][4] to prevent exhausting IoT resources. Another common IoT attack is Distributed DoS (DDoS), which was considered in [5][6].…”

Section: Related Workmentioning

confidence: 99%

Intelligent Botnet Detection Approach in Modern Applications

Alheeti

Alsukayti

Alreshoodi

2021

Int. J. Interact. Mob. Technol.

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<p class="0abstract">Innovative applications are employed to enhance human-style life. The Internet of Things (IoT) is recently utilized in designing these environments. Therefore, security and privacy are considered essential parts to deploy and successful intelligent environments. In addition, most of the protection systems of IoT are vulnerable to various types of attacks. Hence, intrusion detection systems (IDS) have become crucial requirements for any modern design. In this paper, a new detection system is proposed to secure sensitive information of IoT devices. However, it is heavily based on deep learning networks. The protection system can provide a secure environment for IoT. To prove the efficiency of the proposed approach, the system was tested by using two datasets; normal and fuzzification datasets. The accuracy rate in the case of the normal testing dataset was 99.30%, while was 99.42% for the fuzzification testing dataset. The experimental results of the proposed system reflect its robustness, reliability, and efficiency.</p>

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Implementing and Evaluating an Intrusion Detection System for Denial of Service Attacks in IoT Environments

Cited by 5 publications

References 10 publications

IDS Based on Machine Learning in Reaction to IoT Attacks: Review and Empirical Evaluation

IDS Based on Machine Learning in Reaction to IoT Attacks: Review and Empirical Evaluation

Deteksi Serangan Denial of Service pada Internet of Things Menggunakan Finite-State Automata

Intelligent Botnet Detection Approach in Modern Applications

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