Data Mining Techniques for Distributed Denial of Service Attacks Detection in the Internet of Things

Machaka, Pheeha; Nelwamondo, Fulufhelo V.

doi:10.4018/978-1-5225-9866-4.ch030

Cited by 2 publications

(5 citation statements)

References 72 publications

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“…The charging scheduling phase is then implemented by utilizing the hybrid algorithm known as Exponential HHO, a combination of HHO 51 and exponential weighted moving average (EWMA) algorithm. 52 The HHO algorithm is computationally more efficient compared with the other algorithms. HHO utilizes a series of search strategies and selects the best movement step.…”

Section: Proposed Scheduling Approach For Ev Chargingmentioning

confidence: 99%

“…The HHO algorithm is effective in handling search space complexities with local optimum solutions. On the other hand, EWMA 52 was utilized to determine small shifts in processing the target values. Considering the merits of both methods, the issues found in one method can be easily solved by other methods, which is the fundamental utilization of the proposed algorithm.…”

Section: Proposed Exponential Hho For Charging Schedulingmentioning

confidence: 99%

“…HHO is capable of managing search space challenges after considering local optimal solutions, deceptive optima, and multimodality issues. EWMA 52 was devised by averaging a count of consecutive observations, wherein the values are weighed so that the recent observations pose a high impact on averaging. Secondly, there is no need to maintain the database of all numbers.…”

Section: Algorithmic Steps Of Exponential Hho Strategymentioning

confidence: 99%

“…rab rab (11) According to EWMA, 52 the updated equation is given by the current and past observations and is expressed as…”

Section: Algorithmic Steps Of Exponential Hho Strategymentioning

confidence: 99%

See 3 more Smart Citations

Intelligent optimization for charging scheduling of electric vehicle using exponential Harris Hawks technique

Devendiran¹,

Kasinathan²,

Ramachandaramurthy

et al. 2021

Int J Intell Syst

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The coordination of modern transportation system depends heavily on intelligent techniques, information assortment, and its analysis. Sensors play a crucial role in information assortment in charging scheduling of electric vehicles (EVs). EVs are destined to become inevitable due to their innate economic contribution, climate improvement, and social attributes as per United Nation's sustainable development goals. Innovation in EV has gained the interest of many researchers since it is one of the novel green transportation sectors. Moreover, EVs are essential to preserve conventional fuels and to maximize the utilization of renewable sources. Nevertheless, EVs have short driving ranges due to their battery limitation, which hinders the reliability. The charging stations (CS) for EVs are also unevenly distributed. This paper presents a novel strategy to schedule the charging points in EV CSs. The goal is to determine the convenient CS for EVs through Vehicular Ad-hoc Network (VANET) model. In this model, the CSs are determined and prioritized using four phases, such as driving, charge planning, charging scheduling, and

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Section: Proposed Scheduling Approach For Ev Chargingmentioning

confidence: 99%

Section: Proposed Exponential Hho For Charging Schedulingmentioning

confidence: 99%

Section: Algorithmic Steps Of Exponential Hho Strategymentioning

confidence: 99%

“…rab rab (11) According to EWMA, 52 the updated equation is given by the current and past observations and is expressed as…”

Section: Algorithmic Steps Of Exponential Hho Strategymentioning

confidence: 99%

See 2 more Smart Citations

Intelligent optimization for charging scheduling of electric vehicle using exponential Harris Hawks technique

Devendiran¹,

Kasinathan²,

Ramachandaramurthy

et al. 2021

Int J Intell Syst

View full text Add to dashboard Cite

show abstract

“…Machine learning has proven to be very important and effective in identifying and protecting against cyber-attacks [12][13][14][15][16], also specifically for DDOS attacks [17]. For example a 97.4% of identification success for real traffic data has been obtained by D'Angelo et al [18] using U-BRAIN [19].…”

Section: Introductionmentioning

confidence: 99%

Towards Near-Real-Time Intrusion Detection for IoT Devices using Supervised Learning and Apache Spark

Morfino

Rampone

2020

Electronics

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In the fields of Internet of Things (IoT) infrastructures, attack and anomaly detection are rising concerns. With the increased use of IoT infrastructure in every domain, threats and attacks in these infrastructures are also growing proportionally. In this paper the performances of several machine learning algorithms in identifying cyber-attacks (namely SYN-DOS attacks) to IoT systems are compared both in terms of application performances, and in training/application times. We use supervised machine learning algorithms included in the MLlib library of Apache Spark, a fast and general engine for big data processing. We show the implementation details and the performance of those algorithms on public datasets using a training set of up to 2 million instances. We adopt a Cloud environment, emphasizing the importance of the scalability and of the elasticity of use. Results show that all the Spark algorithms used result in a very good identification accuracy (>99%). Overall, one of them, Random Forest, achieves an accuracy of 1. We also report a very short training time (23.22 sec for Decision Tree with 2 million rows). The experiments also show a very low application time (0.13 sec for over than 600,000 instances for Random Forest) using Apache Spark in the Cloud. Furthermore, the explicit model generated by Random Forest is very easy-to-implement using high- or low-level programming languages. In light of the results obtained, both in terms of computation times and identification performance, a hybrid approach for the detection of SYN-DOS cyber-attacks on IoT devices is proposed: the application of an explicit Random Forest model, implemented directly on the IoT device, along with a second level analysis (training) performed in the Cloud.

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Data Mining Techniques for Distributed Denial of Service Attacks Detection in the Internet of Things

Cited by 2 publications

References 72 publications

Intelligent optimization for charging scheduling of electric vehicle using exponential Harris Hawks technique

Intelligent optimization for charging scheduling of electric vehicle using exponential Harris Hawks technique

Towards Near-Real-Time Intrusion Detection for IoT Devices using Supervised Learning and Apache Spark

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