Privacy-Preserving Distributed IDS Using Incremental Learning for IoT Health Systems

Tabassum, Aliya; Erbad, Aiman; Mohamed, Amr; Guizani, Mohsen

doi:10.1109/access.2021.3051530

Cited by 38 publications

(10 citation statements)

References 26 publications

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“…Tabassum et al use the datasets KDDcup-99 and NSL-KDD and merge their selfgenerated IoT traffic to cope with the missing IoT traffic in named datasets. However, they do not explain which MCPS they employed for the generation [115].…”

Section: ) Publicly Available Datasetsmentioning

confidence: 95%

“…], Radoglou-Grammatikis et al[97] Radoglou-Grammatikis et al[98], Rahmadika et al[99], Ram and Kumar[100], Rao et al[101], Rao et al[102], Ravi et al[103], Rehman et al[104], Saba[105], Saheed and Arowolo[106], Said et al[107], Salem and Mehaoua[108], Schneble and Thamilarasu[109], Sehatbakhsh et al[110], Sharma et al[111], Singh et al[112], Siniosoglou et al[113], Spegni et al[114], Tabassum et al[115], Tahir et al[116], Thamilarasu et al[117], Thamilarasu et al[24], Thapa et al[118], Toor et al[119], Wa Umba et al[120], Wagan et al [121], Wahab et al [122], Wang et al [123], Yan et al [124], Zaabar et al [125], Zachos et al [126], Zubair et al [127] 99 Specification-based Abdulhammed et al [128], Choudhary et al [129], Fang et al [130], Mitchell and Chen [29], Mitchell and Chen [131], Li et al [132], Raiyat Aliabadi et al [133], You et al [134], Zhang et al [135] 9 Signature-based Boujrad et al [136], Meng et al [25], Mpungu et al [137], Zhang et al [138] 4 Hybrid Begli et al [139], Chen et al [140], Dupont et al [141], Meng et al [142], Kolokotronis et al [143], Lakka et al [144], Tariq et al [145] 7 researchers substantiate why their approach works best (e.g., Saheed et al with their swarm-based approach [106]), others focus on optimizing parts of their approach. E.g., Priya et al measure the benefits of different dimensionality reduction approaches [96].…”

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confidence: 99%

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Attack Detection for Medical Cyber-Physical Systems–A Systematic Literature Review

et al. 2023

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The threat situation due to cyber attacks in hospitals is emerging and patient life is at risk. One significant source of potential vulnerabilities is medical cyber-physical systems (MCPS). Detecting intrusions in this environment faces challenges different from other domains, mainly due to the heterogeneity of devices, the diversity of connectivity types, and the variety of terminology. To summarize existing results, we conducted a structured literature review (SLR) following the guidelines of Kitchenham et al. for SLRs in software engineering. We developed six research questions regarding detection approach, detection location, included features, adversarial focus, utilized datasets, and intrusion prevention. We identified that most researchers focused on an anomaly-based detection approach at the network layer. The primary focus was on the detection of malicious insiders. While several researchers used publicly available datasets for training and testing their algorithms, the lack of suitable datasets resulted in the development of testbeds consisting of various medical devices. Based on the results, we formulated five future research topics. First, the special conditions of hospital networks, the MCPS deployed within them, and the contrasts to other IT and OT environments should be examined. Thereupon, MCPS-specific datasets should be created that allow researchers to address the health domain's unique requirements and possibilities. At the same time, endeavors aimed at standardization in this area should be supported and expanded. Moreover, the use of medical context for attack detection should be further explored. Last but not least, efforts for MCPS-tailored intrusion prevention should be intensified. This way, the emerging threat landscape can be addressed, IT security in hospitals can be improved, and patient health can be protected.

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Section: ) Publicly Available Datasetsmentioning

confidence: 95%

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confidence: 99%

Attack Detection for Medical Cyber-Physical Systems–A Systematic Literature Review

et al. 2023

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“…To reduce the computation overhead [30], introduced a privacy-preserving pipelinebased intrusion detection for distributed incremental learning that selects unique features using an innovative extraction technique. Current incremental learning techniques are computationally expensive.…”

Section: Related Workmentioning

confidence: 99%

Using Incremental Ensemble Learning Techniques to Design Portable Intrusion Detection for Computationally Constraint Systems

Agbedanu¹,

Musabe²,

Rwigema³

et al. 2022

Preprint

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Computers have evolved over the years and as the evolution continues, we have been ushered into an era where high-speed internet has made it possible for devices in our homes, hospital, energy and industry to communicate with each other. This era is what is known as the Internet of Things (IoT). IoT has several benefits in the health, energy, transportation and agriculture sectors of a country’s economy. These enormous benefits coupled with the computational constraint of IoT devices which makes it difficult to deploy enhanced security protocols on them make IoT devices a target of cyber-attacks. One approach that has been used in traditional computing over the years to fight cyber-attacks is Intrusion Detection System (IDS). However, it is practically impossible to deploy IDS meant for traditional computers in IoT environments because of the computational constraint of these devices. In this regard, this study proposes a lightweight IDS for IoT devices using an incremental ensemble learning technique. We used Gaussian Naive Bayes and Hoeffding tree to build our incremental ensemble model. The model was then evaluated on the TON IoT dataset. Our proposed model was compared with other state-of-the-art methods proposed and evaluated using the same dataset. The experimental results show that the proposed model achieved an average accuracy of 99.98\%. We also evaluated the memory consumption of our model which showed that our model achieved a lightweight model status of 650.11KB as the highest memory consumption and 122.38KB as the lowest memory consumption.

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“…To reduce the computation overhead, [30], introduced a privacy-preserving pipeline-based intrusion detection for distributed incremental learning that selects unique features using an innovative extraction technique. Current incremental learning techniques are computationally expensive, and the distributed intrusion detection method is used to distribute the load across IoT and edge devices.…”

Section: Related Workmentioning

confidence: 99%

Using Incremental Ensemble Learning Techniques to Design Portable Intrusion Detection for Computationally Constraint Systems

Agbedanu¹,

Musabe²,

Rwigema³

et al. 2022

IJACSA

View full text Add to dashboard Cite

Computers have evolved over the years, and as the evolution continues, we have been ushered into an era where high-speed internet has made it possible for devices in our homes, hospital, energy, and industry to communicate with each other. This era is known as the Internet of Things (IoT). IoT has several benefits in a country's economy's health, energy, transportation, and agriculture sectors. These enormous benefits, coupled with the computational constraint of IoT devices, make it challenging to deploy enhanced security protocols on them, making IoT devices a target of cyber-attacks. One approach that has been used in traditional computing over the years to fight cyber-attacks is Intrusion Detection System (IDS). However, it is practically impossible to deploy IDS meant for traditional computers in IoT environments because of the computational constraint of these devices. This study proposes a lightweight IDS for IoT devices using an incremental ensemble learning technique. We used Gaussian Naive Bayes and Hoeffding trees to build our incremental ensemble model. The model was then evaluated on the TON IoT dataset. Our proposed model was compared with other proposed state-of-the-art methods and evaluated using the same dataset. The experimental results show that the proposed model achieved an average accuracy of 99.98%. We also evaluated the memory consumption of our model, which showed that our model achieved a lightweight model status of 650.11KB as the highest memory consumption and 122.38KB as the lowest memory consumption.

show abstract

Privacy-Preserving Distributed IDS Using Incremental Learning for IoT Health Systems

Cited by 38 publications

References 26 publications

Attack Detection for Medical Cyber-Physical Systems–A Systematic Literature Review

Attack Detection for Medical Cyber-Physical Systems–A Systematic Literature Review

Using Incremental Ensemble Learning Techniques to Design Portable Intrusion Detection for Computationally Constraint Systems

Using Incremental Ensemble Learning Techniques to Design Portable Intrusion Detection for Computationally Constraint Systems

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