A Review of Machine Learning Solutions to Denial-of-Services Attacks in Wireless Sensor Networks

Gunduz, Sedef; Arslan, Bilgehan; Demirci, Sedef

doi:10.1109/icmla.2015.202

Cited by 17 publications

(11 citation statements)

References 29 publications

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“…Throughout Gunduz et al [22] looked at Denial of Service (DoS) attacks at each TCP/ IP protocol stack layer. Author attention was drawn to the network layer assaults, which are more diversified than the other layers of attacks.…”

Section: Related Workmentioning

confidence: 99%

Performance evaluation of deep learning techniques for DoS attacks detection in wireless sensor network

Salim

Oughdir

2023

J Big Data

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Wireless sensor networks (WSNs) are increasingly being used for data monitoring and collection purposes. Typically, they consist of a large number of sensor nodes that are used remotely to collect data about the activities and conditions of a particular area, for example, temperature, pressure, motion. Each sensor node is usually small, inexpensive, and relatively easy to deploy compared to other sensing methods. For this reason, WSNs are used in a wide range of applications and industries. However, WSNs are vulnerable to different kinds of security threats and attacks. This is primarily because they are very limited in resources like power, storage, bandwidth, and processing power that could have been used in developing their defense. To ensure their security, an effective Intrusion detection system (IDS) need to be in place to detect these attacks even under these constraints. Today, traditional IDS are less effective as these malicious attacks are becoming more intelligent, frequent, and complex. Denial of service (DOS) attack is one of the main types of attacks that threaten WSNs. For this reason, we review related works that focus on detecting DoS attacks in WSN. In addition, we developed and implemented several Deep learning (DL) based IDS. These systems were trained on a specialized dataset for WSNs called WSN-DS in detecting four types of DoS attacks that affects WSNs. They include the Blackhole, Grayhole, Flooding, and Scheduling attacks. Finally, we evaluated and compared the results and we discuss possible future works.

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Section: Related Workmentioning

confidence: 99%

Performance evaluation of deep learning techniques for DoS attacks detection in wireless sensor network

Salim

Oughdir

2023

J Big Data

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“…Similarly, the authors in [6] have discussed WSNs and their crucial role in different applications and usage; the vulnerabilities of the WSN due to their constrained resources. How DoS attack can be carried out at different layers of the network architecture.…”

Section: Related Workmentioning

confidence: 99%

“…The authors focused specifically on the network layer because of the diversity of the attack at this layer. The authors reviewed many studies that use machine learning techniques pertaining to the network layer DoS attacks in WSN [6].…”

Section: Related Workmentioning

confidence: 99%

Evaluation of Machine Learning Algorithms for Intrusion Detection System in WSN

Alsahli¹,

Almasri²,

Al-Akhras³

et al. 2021

IJACSA

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Technology has revolutionized into connecting "things" together with the rebirth of the global network called Internet of Things (IoT). This is achieved through Wireless Sensor Network (WSN) which introduces new security challenges for Information Technology (IT) scientists and researchers. This paper addresses the security issues in WSN by establishing potential automated solutions for identifying associated risks. It also evaluates the effectiveness of various machine learning algorithms on two types of datasets, mainly, KDD99 and WSN datasets. The aim is to analyze and protect WSN networks in combination with Firewalls, Deep Packet Inspection (DPI), and Intrusion Prevention Systems (IPS) all specialized for the overall protection of WSN networks. Multiple testing options were investigated such as cross validation and percentage split. Based on the finding, the most accurate algorithm and the least time processing were suggested for both datasets.

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“…The best results were achieved with one hidden layer. Gonduz et al [4] presented a survey of ML solutions for detecting DoS attacks. The DoS variations were reviewed at each layer of the TCP/IP protocol stack and focused on network layer attacks.…”

Section: Literature Reviewmentioning

confidence: 99%

Performance Evaluation of Machine Learning Techniques for DOS Detection in Wireless Sensor Network

Alsulaiman¹,

Al-Ahmadi²

2021

IJNSA

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The nature of Wireless Sensor Networks (WSN) and the widespread of using WSN introduce many security threats and attacks. An effective Intrusion Detection System (IDS) should be used to detect attacks. Detecting such an attack is challenging, especially the detection of Denial of Service (DoS) attacks. Machine learning classification techniques have been used as an approach for DoS detection. This paper conducted an experiment using Waikato Environment for Knowledge Analysis (WEKA)to evaluate the efficiency of five machine learning algorithms for detecting flooding, grayhole, blackhole, and scheduling at DoS attacks in WSNs. The evaluation is based on a dataset, called WSN-DS. The results showed that the random forest classifier outperforms the other classifiers with an accuracy of 99.72%.

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A Review of Machine Learning Solutions to Denial-of-Services Attacks in Wireless Sensor Networks

Cited by 17 publications

References 29 publications

Performance evaluation of deep learning techniques for DoS attacks detection in wireless sensor network

Performance evaluation of deep learning techniques for DoS attacks detection in wireless sensor network

Evaluation of Machine Learning Algorithms for Intrusion Detection System in WSN

Performance Evaluation of Machine Learning Techniques for DOS Detection in Wireless Sensor Network

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