Lightweight adaptive Random-Forest for IoT rule generation and execution

Domb, Menachem; Bonchek-Dokow, Elisheva; Leshem, Guy

doi:10.1016/j.jisa.2017.03.001

Cited by 18 publications

(12 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As an example of classification using RF in IoT, the research in [88] generate rules using random forests, which are then applied to features extracted from network traffic data with an objective to identify IoT device type from a whitelist of trustworthy devices. To reduce the probability of generating biased trees, the work in [89] [97]. In this work, the authors divide the pre-processed IoT data as low-risk and high-risk, which is further detected using different frequencies to achieve better accuracy.…”

Section: ) On the Basis Of Detection Techniquementioning

confidence: 99%

“…Theoretical analysis by creating a system model, lemma and proofs are done in [100], [101], [105]. [46], [48], [70]- [73], [77]- [79], [86], [89], [95]- [97], [107], [108], [112]- [115], [128], [129], [131], [134], [136], [143], [144], [148], [150]- [152] [80]- [82], [84], [85], [87], [88], [98], [101], [103], [109], [110], [118], [119], [122], [123], [130], [135], [138], [146], [147] [74], [83] Hybrid [102] [125], [132],…”

Section: ) On the Basis Of Validation Strategymentioning

confidence: 99%

“…• Detection Related -In the literature, the metric of accuracy has been used to evaluate the performance of classification [88], [101], [144], pattern matching [72], [85], random forest [89], neural network based deep learning [109], [111], [113]- [115], clustering [120], [151] and game theory [146]. Although accuracy is a great start metric to measure performance of an IDS, it is not considered enough to measure performance.…”

Section: ) On the Basis Of Evaluation Metrics Of Idsmentioning

confidence: 99%

“…With this time, IDS are classified as being real time or non-real time. This time is the difference between the attack launch time and the attack detection time, and was discussed in [89], [113], [138]. In other works [72], [73], [83], [111], this time was indirectly termed as the execution time or the time taken by the IDS to generate appropriate output.…”

Section: ) On the Basis Of Evaluation Metrics Of Idsmentioning

confidence: 99%

“…Finally, in [139], [141], [146] the difference of initial energy set in sensor nodes and energy consumed was used to calculate average energy consumed at all nodes. The works [71], [81]- [85], [87]- [89], [98], [102]- [110], [113]- [115], [119], [120], [124], [126], [128]- [130], [135]- [138], [140], [142], [143], [145], [151], [152], [164], [168], [169] do not provide any performance metric for their proposed IDS solutions. The classification of various proposals with evaluation metrics for routing attack detection is shown in Table 7.…”

Section: ) On the Basis Of Evaluation Metrics Of Idsmentioning

confidence: 99%

See 4 more Smart Citations

Towards a Secure Internet of Things: A Comprehensive Study of Second Line Defense Mechanisms

2020

View full text Add to dashboard Cite

The Internet of Things (IoT) exemplifies a large network of sensing and actuating devices that have penetrated into the physical world enabling new applications like smart homes, intelligent transportation, smart healthcare and smart cities. Through IoT, these applications have consolidated in the modern world to generate, share, aggregate and analyze large amount of security-critical and privacy sensitive data. As this consolidation gets stronger, the need for security in IoT increases. With first line of defense strategies like cryptography being unsuited due to the resource constrained nature, second line of defense mechanisms are crucial to ensure security in IoT networks. This paper presents a comprehensive study of existing second line of defense mechanisms for standardized protocols in IoT networks. The paper analyzes existing mechanisms in three aspects: Intrusion Detection Systems (IDS), Intrusion Prevention Systems (IPS) and Intrusion Response Systems (IRS). We begin by providing an overview of standardized protocol stack, its layers and defensive security systems in IoT. From there, we build our narrative by presenting an extended taxonomy of IDS, IPS and IRS classifying them on their techniques, deployment, attacks, datasets, evaluation metrics and data pre-processing methods. We then thoroughly review, compare and analyze the research proposals in this context, considering the unique characteristics involved in these systems. Based on the extensive analysis of the existing defensive security systems, the paper also identifies open research challenges and directions for effective design of such systems for IoT networks, which could guide future research in the area.

show abstract

Section: ) On the Basis Of Detection Techniquementioning

confidence: 99%

Section: ) On the Basis Of Validation Strategymentioning

confidence: 99%

Section: ) On the Basis Of Evaluation Metrics Of Idsmentioning

confidence: 99%

Section: ) On the Basis Of Evaluation Metrics Of Idsmentioning

confidence: 99%

Section: ) On the Basis Of Evaluation Metrics Of Idsmentioning

confidence: 99%

See 3 more Smart Citations

Towards a Secure Internet of Things: A Comprehensive Study of Second Line Defense Mechanisms

2020

View full text Add to dashboard Cite

show abstract

On Reducing Underutilization of Security Standards by Deriving Actionable Rules: An Application to IoT

Shuvo¹,

Hoq²,

Majumdar³

et al. 2023

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Even though there exist a number of security guidelines and recommendations from various worldwide standardization authorities (e.g., NIST, ISO, ENISA), it is evident from many of the recent attacks that these standards are not strictly followed in the implementation of real-world products. Furthermore, most security applications (e.g., monitoring and auditing) do not consider those standards as the basis of their security check. Therefore, regardless of continuous efforts in publishing security standards, they are still under-utilized in practice. Such under-utilization might be caused by the fact that existing security standards are intended more for high-level recommendations than for being readily adopted to automated security applications on the system-level data.Bridging this gap between high-level recommendations and low-level system implementations becomes extremely difficult, as a fully automated solution might suffer from high inaccuracy, whereas a fully manual approach might require tedious efforts. Therefore, in this thesis, we aim for a more practical solution by proposing a partially automated approach, where it automates the tedious tasks (e.g., summarizing long standard documents, and extracting device specifications) and relies on manual efforts from security experts to avoid mistakes in finalizing security rules. We apply our solution to IoT by implementing it with IoT-specific standards (NISTIR 8228) and smart home networks. We further demonstrate the actionability of our derived rules in three major applications: security auditing, Intrusion Detection systems (IDS), and secure application development.

show abstract

Artificial intelligence, cyber-threats and Industry 4.0: challenges and opportunities

Praça²,

2021

View full text Add to dashboard Cite

Lightweight adaptive Random-Forest for IoT rule generation and execution

Cited by 18 publications

References 6 publications

Towards a Secure Internet of Things: A Comprehensive Study of Second Line Defense Mechanisms

Towards a Secure Internet of Things: A Comprehensive Study of Second Line Defense Mechanisms

On Reducing Underutilization of Security Standards by Deriving Actionable Rules: An Application to IoT

Artificial intelligence, cyber-threats and Industry 4.0: challenges and opportunities

Contact Info

Product

Resources

About