OPTIMIST: Lightweight and Transparent IDS With Optimum Placement Strategy to Mitigate Mixed-Rate DDoS Attacks in IoT Networks

Bhale, Pradeepkumar; Chowdhury, Roy; Biswas, Santosh; Nandi, Sukumar

doi:10.1109/jiot.2023.3234530

Cited by 17 publications

(14 citation statements)

References 38 publications

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“…The results show that ML-based black-box attack detectors are incapable of classifying adversarial data. P. Bhale et al 15 suggested that WGAN be used to make ADAs so that an ML model can be trained efficiently. The outcomes show that the proposed approach is energy-efficient and suitable for the IoT ecosystem.…”

Section: Related Workmentioning

confidence: 99%

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Effective injection of adversarial botnet attacks in IoT ecosystem using evolutionary computing

Bhale

Biswas

Nandi

2023

Internet Technology Letters

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With the widespread adoption of Internet of Things (IoT) technologies, botnet attacks have become the most prevalent cyberattack. In order to combat botnet attacks, there has been a considerable amount of research on botnet attacks in IoT ecosystems by graph‐based machine learning (GML). The majority of GML models are vulnerable to adversarial attacks (ADAs). These ADAs were created to assess the robustness of existing ML‐based security solutions. In this letter, we present a novel adversarial botnet attack (ADBA) that modifies the graph data structure using genetic algorithms (GAs) to trick the graph‐based botnet attack detection system. According to the experiment results and comparative analysis, the proposed ADBA can be executed on resource‐constrained IoT nodes. It offers a substantial performance gain of 2.15 s, 52 kb, 92 817 mJ, 97.8%, and 27.74%–41.82% over other approaches in term of Computing Time (CT), Memory Usage (MU), Energy Usage (EU), Attack Success Rate (ASR) and Accuracy (ACC) metrics, respectively.

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Section: Related Workmentioning

confidence: 99%

“…The size command and powertrac tools extract MU and EU from Contiki Cooja simulations. 15 The FIT IoT-LAB testbed 15 uses Sysstat to get system-level data like EU and MU.…”

Section: Ta B L Ementioning

confidence: 99%

Effective injection of adversarial botnet attacks in IoT ecosystem using evolutionary computing

Bhale

Biswas

Nandi

2023

Internet Technology Letters

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“…IDS can be placed at the host side called host-based IDS and IDS placed at the network side is NIDS many researchers have come up with an idea of optimal IDS placement as proposed. 2 To safeguard IoT networks Intrusion Detection Systems (NIDS) play an important role by continuously monitoring the network traffic and reducing the security risk. Attacks are classified into binary and multiclass, in binary class classification is done as an anomaly or normal whereas, in multiclass classification, attack instances may be classified into four types based on the nature of the attack: User-to-Root (U2R) attacks, Remote-to-Local (R2L) attacks, Denial-of-Service (DoS) attacks, and Probes.…”

Section: Introductionmentioning

confidence: 99%

“…So effective detection and securing the network from attacks has become a difficult and increasingly significant area of research. IDS can be placed at the host side called host‐based IDS and IDS placed at the network side is NIDS many researchers have come up with an idea of optimal IDS placement as proposed 2 . To safeguard IoT networks Intrusion Detection Systems (NIDS) play an important role by continuously monitoring the network traffic and reducing the security risk.…”

Section: Introductionmentioning

confidence: 99%

Coarse and fine feature selection for Network Intrusion Detection Systems (IDS) in IoT networks

Habeeb,

Babu

2024

Trans Emerging Tel Tech

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Network Intrusion Detection Systems (NIDSs) are important in safeguarding networks from known and unknown attacks. Many research efforts have recently been made to create NIDS systems based on Machine Learning (ML) methods, addressing a significant challenge in designing standard NIDS the lack of standardized feature sets in the dataset. Given the recent development of the Internet of Things (IoT) in wireless communication, our proposed method introduces a novel solution to enhance intrusion detection systems. This proposed solution feature selection is carried out in two stages, coarse and fine selection. In the first stage of the coarse selection process, we conduct correlation analysis to identify relationships within the feature set. The second stage employs fine selection using the Whale Optimization Algorithm (WOA) with Genetic Algorithm hybridization (CFWOAGA). The fitness of each selected feature is assessed using the K‐Nearest Neighbors (KNN) algorithm. In our proposed work we integrate WOA with hybrid GA to extend the search space and avoid local optima problems via crossover and mutation operations. These selected features are critical for detecting any intrusion, we use an ML classifier to identify whether there is an attack or normal in the network and we evaluate the performance of each classifier. We evaluate the performance of our classifier using the BoT‐IoT 2020 standard dataset while limiting the selected features to 32 for reduced computational complexity, these selected 32 features are based upon considerations of system optimization and efficiency, making a balance between computational efficiency and model performance. The experimental findings show better model accuracy compared to the WOA technique and a significant drop in the False Alarm Rate (FAR). In conclusion, our proposed CFWOA method achieved an accuracy of 98.9%, while an updated version with the genetic algorithm demonstrated further improvement at 99.5%. Notably, there was a substantial improvement in FAR with our proposed method.

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“…Thus, the present work addresses the version number attack (VNA), where the malicious behavior appears when an intruder tries to falsify the present version number to generate an extra control overhead aiming to exhausting the network resources and thereby to shorten its lifetime [24]. Various recent researches have focused on proposing solutions against this critical threat; however they are mainly based on artificial intelligence algorithms exploiting cloud platforms [25][26][27][28], since the edge part is composed by constrained devices with limited resources in terms of computation, storage, and energy. This makes complex algorithms heavy to be supported by IoT devices [29].…”

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confidence: 99%

Parent supervision lightweight solution against version number attacks for IoT networks

Rouissat

Belkheir

Mokaddem

2023

Preprint

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With the rapid involvment of IoT networks in almost all aspects of our daily life, the security concern is being more challenging to protect the enormous amount of the data exchanged over theses networks. Statisitcs reveal that threats against IoT networks are more and more growing , while DoS attacks are taking the lead. However, mitigation solutions against attacks targetting IoT networks must be developped while taking into consideration the network scalability, but also they should fit with the limited resources of IoT devices in terms of processing, storage, energy efficiency, and connectivity-loss. For thi aim, the present work proposes a lightweight detection and mitigation solution against the version number attack "VNA" which is considered as one among the haitful DoS attacks against IoT networks. The proposed decentralized algorithm targets the edge part of the netowrk. Upon an illigitimate incremented version number the sink node is informed via an alert DAO message by the malicious node parent, and it disseminates this information through the overall network to advertise the malicois originator. Beside its efficiency, the proposed algorithm does not involve any encryption algorithm, it does not generale any heavyweight computation, nor a specific extra node is needed for monitoring the network. Simulations were carried out to demonstrate the effectiveness of the solution in terms of control overhead, energy efficiency, latency, and memory footrpint.

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OPTIMIST: Lightweight and Transparent IDS With Optimum Placement Strategy to Mitigate Mixed-Rate DDoS Attacks in IoT Networks

Cited by 17 publications

References 38 publications

Effective injection of adversarial botnet attacks in IoT ecosystem using evolutionary computing

Effective injection of adversarial botnet attacks in IoT ecosystem using evolutionary computing

Coarse and fine feature selection for Network Intrusion Detection Systems (IDS) in IoT networks

Parent supervision lightweight solution against version number attacks for IoT networks

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