DLDM: Deep learning-based defense mechanism for denial of service attacks in wireless sensor networks

Premkumar, M.; Sundararajan, T.

doi:10.1016/j.micpro.2020.103278

Cited by 88 publications

(49 citation statements)

References 25 publications

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“…It usually authenticate the sender for protection against the active attacks. The DLDM Framework structure [34] is used to identify the different kinds of DDoS attack effectively, thereby it improves the throughput and it also reduces the energy consumption. EPSM [35] is proposed to detect the wormhole attack and it also used to minimize the energy consumption and the overhead of the network.…”

Section: Related Workmentioning

confidence: 99%

“…It also increases the lifetime of the network and increases the live nodes even after 450 rounds. The proposed method was evaluated and compared with the related DDoS detection strategies like the FLQL method [21], FSDNA [25], SACOP algorithm [29] and DLDMFS [34] rounds. But in the other related strategies, no more alive nodes available in 400 rounds that also affect the lifetime of the network.…”

Section: Performance Evaluationmentioning

confidence: 99%

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FBDR-Fuzzy Based DDoS Attack Detection and Recovery Mechanism for Wireless Sensor Networks

Pajila¹,

Julie

Robinson

2021

Wireless Pers Commun

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Wireless sensor networks (WSN) is considering as one of the exploring technology.WSN has a large number of sensor nodes, which sense the environment and collect the data. The collected data are sending to the sink through the intermediate nodes. Since the sensors node data are exposed to the internet, there is a possibility of vulnerability in the WSN. The common attack that affects most of the sensor nodes is the DDoS attack. In this paper aims to identify the DDoS attack quickly and to recover sensors using the fuzzy logic mechanism. In the Fuzzy based DDoS attack Detection and Recovery mechanism (FBDR) method uses type 1 fuzzy-logic to detect the occurrence of DDoS attack in a node. Similarly fuzzy-type 2 is used for recovery DDoS attack.Both the type 1 fuzzy-based rule and type 2 fuzzy-based rule perform well in terms of identifying the DDoS attack and recover the DDoS attack. It also helps to reduce the energy consumption of each node and improves the lifetime of the network. The proposed FBDR scheme is compared with other related schemes. The experimental results represent that the FBDR method works better than other similar schemes.

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Section: Related Workmentioning

confidence: 99%

Section: Performance Evaluationmentioning

confidence: 99%

FBDR-Fuzzy Based DDoS Attack Detection and Recovery Mechanism for Wireless Sensor Networks

Pajila¹,

Julie

Robinson

2021

Wireless Pers Commun

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“…In this context, Figure 1 depicts the percentage of the security attacks collected from McAfee Labs in 2017, in which most of them are browser attacks, brute force attacks, and Distributed Denial of Service (DDoS) attacks [1], [2]. Also, several security attacks for the new computing environments such as WBANs [3]- [5], e-healthcare systems [6]- [8], fog computing [9], Mobile Edge Computing (MEC), Cloud Computing [10], [11], wireless sensor networks [12], [13], mobile ad hoc networks [14]- [16], and…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Intrusion Detection Systems: A Systematic Review

et al. 2021

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Nowadays, the ever-increasing complication and severity of security attacks on computer networks have inspired security researchers to incorporate different machine learning methods to protect the organizations' data and reputation. Deep learning is one of the exciting techniques which recently are vastly employed by the IDS or intrusion detection systems to increase their performance in securing the computer networks and hosts. This survey article focuses on the deep learning-based intrusion detection schemes and puts forward an in-depth survey and classification of these schemes. It first presents the primary background concepts about IDS architecture and various deep learning techniques. It then classifies these schemes according to the type of deep learning methods utilized in each of them. It describes how deep learning networks are utilized in the intrusion detection process to recognize intrusions accurately. Finally, a complete analysis of the investigated IDS frameworks is provided, and concluding remarks and future directions are highlighted.INDEX TERMS Intrusion detection, auto-encoder, recurrent neural network, Boltzmann machine, CNN.

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“…As a result, a variety of techniques have been used to boost DoS detection efficiency using both active and passive methods. Supervised machine learning algorithms are one such technique used to predict and classify DoS and DDoS attacks [ 12 , 13 , 14 , 15 ]. Decision Tree (DT), Support Vector Machine (SVM), K-Nearest Neighbours (KNN), Deep-Learning (DL) classifier, and Naive Bayes (NB) are common algorithms for this purpose [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

“…By summarizing previous studies and applications, we see that there has been an increase in the use of WSNs recently, that these devices play multiple roles, and the biggest challenges for WSNs remain security and energy. To overcome these challenges, several solutions that discuss data security performance problems have been proposed [ 10 , 11 , 12 , 13 , 15 , 35 , 36 ] that do not consider the impact on wireless power consumption. Moreover, other studies have discussed the concept of energy conservation from a data security-independent perspective [ 30 , 31 , 32 , 33 , 34 ].…”

Section: Introductionmentioning

confidence: 99%

Feature-Selection and Mutual-Clustering Approaches to Improve DoS Detection and Maintain WSNs’ Lifetime

Ahmad

Wazirali

Bsoul

et al. 2021

Sensors

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Wireless Sensor Networks (WSNs) continue to face two major challenges: energy and security. As a consequence, one of the WSN-related security tasks is to protect them from Denial of Service (DoS) and Distributed DoS (DDoS) attacks. Machine learning-based systems are the only viable option for these types of attacks, as traditional packet deep scan systems depend on open field inspection in transport layer security packets and the open field encryption trend. Moreover, network data traffic will become more complex due to increases in the amount of data transmitted between WSN nodes as a result of increasing usage in the future. Therefore, there is a need to use feature selection techniques with machine learning in order to determine which data in the DoS detection process are most important. This paper examined techniques for improving DoS anomalies detection along with power reservation in WSNs to balance them. A new clustering technique was introduced, called the CH_Rotations algorithm, to improve anomaly detection efficiency over a WSN’s lifetime. Furthermore, the use of feature selection techniques with machine learning algorithms in examining WSN node traffic and the effect of these techniques on the lifetime of WSNs was evaluated. The evaluation results showed that the Water Cycle (WC) feature selection displayed the best average performance accuracy of 2%, 5%, 3%, and 3% greater than Particle Swarm Optimization (PSO), Simulated Annealing (SA), Harmony Search (HS), and Genetic Algorithm (GA), respectively. Moreover, the WC with Decision Tree (DT) classifier showed 100% accuracy with only one feature. In addition, the CH_Rotations algorithm improved network lifetime by 30% compared to the standard LEACH protocol. Network lifetime using the WC + DT technique was reduced by 5% compared to other WC + DT-free scenarios.

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DLDM: Deep learning-based defense mechanism for denial of service attacks in wireless sensor networks

Cited by 88 publications

References 25 publications

FBDR-Fuzzy Based DDoS Attack Detection and Recovery Mechanism for Wireless Sensor Networks

FBDR-Fuzzy Based DDoS Attack Detection and Recovery Mechanism for Wireless Sensor Networks

Deep Learning-Based Intrusion Detection Systems: A Systematic Review

Feature-Selection and Mutual-Clustering Approaches to Improve DoS Detection and Maintain WSNs’ Lifetime

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