CNN-LSTM Based Approach for Dos Attacks Detection in Wireless Sensor Networks

Salmi, Salim; Oughdir, Lahcen

doi:10.14569/ijacsa.2022.0130497

Cited by 19 publications

(10 citation statements)

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“…It has mainly concentrated on DoS (Denial of Service) a acks. The classification outcome demonstrates that the existing system a ained an accuracy of 0.944 [27]. In the same way, wormhole and blackhole a ack detection in the wireless sensor networks system has been generated in existing research.…”

Section: Review Of Literaturementioning

confidence: 59%

Reinforcing Network Security: Network Attack Detection Using Random Grove Blend in Weighted MLP Layers

Binbusayyis

2024

Mathematics

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In the modern world, the evolution of the internet supports the automation of several tasks, such as communication, education, sports, etc. Conversely, it is prone to several types of attacks that disturb data transfer in the network. Efficient attack detection is needed to avoid the consequences of an attack. Traditionally, manual attack detection is limited by human error, less efficiency, and a time-consuming mechanism. To address the problem, a large number of existing methods focus on several techniques for better efficacy in attack detection. However, improvement is needed in significant factors such as accuracy, handling larger data, over-fitting versus fitting, etc. To tackle this issue, the proposed system utilized a Random Grove Blend in Weighted MLP (Multi-Layer Perceptron) Layers to classify network attacks. The MLP is used for its advantages in solving complex non-linear problems, larger datasets, and high accuracy. Conversely, it is limited by computation and requirements for a great deal of labeled training data. To resolve the issue, a random info grove blend and weight weave layer are incorporated into the MLP mechanism. To attain this, the UNSW–NB15 dataset, which comprises nine types of network attack, is utilized to detect attacks. Moreover, the Scapy tool (2.4.3) is utilized to generate a real-time dataset for classifying types of attack. The efficiency of the presented mechanism is calculated with performance metrics. Furthermore, internal and external comparisons are processed in the respective research to reveal the system’s better efficiency. The proposed model utilizing the advantages of Random Grove Blend in Weighted MLP attained an accuracy of 98%. Correspondingly, the presented system is intended to contribute to the research associated with enhancing network security.

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Section: Review Of Literaturementioning

confidence: 59%

Reinforcing Network Security: Network Attack Detection Using Random Grove Blend in Weighted MLP Layers

Binbusayyis

2024

Mathematics

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“…Salmi and Oughdir [20] presented a CNN-LSTM approach to detect and classify DoS intrusion attacks as Flooding,Blackhole, Normal, TDMA, or Grayhole.This research study uses a computer-generated wireless sensor network-detection system WSN-DS dataset;The developed model gives a promising outcome in the attack detection process and successfully classifies the given attacks with a accuracy of 97%.…”

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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“…Collect network data and preprocess it to generate 23 functions, classify the condition of each sensor, and simulate five types of DoS using the NS-2 network simulator LEACH-based routing protocol to replicate the wireless sensor network environment. The proposed CNN-LSTM model was tested in 25 epochs, and the accuracy, precision, and sensitivity scores were 0.944, 0.959, and 0.922, respectively, everything within the 0-1 range [18].…”

Section: Literature Surveymentioning

confidence: 99%

Effective detection of DoS attack in wireless sensor network using ASFO algorithm and Improved Deep Convolutional Neural Network

2023

Preprint

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Wireless sensor networks (WSN) are rapidly growing in popularity, and their flexibility and ease of implementation cause an increasing number of security issues, making it critical to research network intrusion defense for wireless sensor networks. Denial of service (DoS) is a cyber-attack that shuts down the targeted network. A DoS attack on a WSN device will be fatal. It is prone to malicious attacks and difficult to prevent because every single node is independent of the others, yet there's no central or monitoring node, which is tough to avoid. Numerous lightweight authentication systems have been utilized in real-time to ensure encrypted communication. However, with the lack of synchronization between nodes during data routing, WSNs are highly prone to Denial of Service (DoS) attacks. This article combines the Adaptive Sunflower Optimization (ASFO) method with an improved Deep Convolutional Neural Network (IDCNN) to enhance the degree of security against DoS attacks on WSNs term ASFO-IDCNN. This paper initially utilizes the ASFO method to improve the initial values of IDCNN to prevent getting into the local optimum. Then, the ASFO-IDCNN technique is used to detect intrusions in WSNs. The results of numerous simulated situations are shown, and the associated data is compared. DoS protection research is precious in analyzing the anti-attack efficiency of WSN nodes. The influence of DoS attacks on the functioning of WSNs is considered in this research.

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CNN-LSTM Based Approach for Dos Attacks Detection in Wireless Sensor Networks

Cited by 19 publications

References 0 publications

Reinforcing Network Security: Network Attack Detection Using Random Grove Blend in Weighted MLP Layers

Reinforcing Network Security: Network Attack Detection Using Random Grove Blend in Weighted MLP Layers

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

Effective detection of DoS attack in wireless sensor network using ASFO algorithm and Improved Deep Convolutional Neural Network

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