A Multi-Layer Classification Approach for Intrusion Detection in IoT Networks Based on Deep Learning

Qaddoura, Raneem; Al-Zoubi, Ala’ M.; Faris, Hossam; Almomani, Iman

doi:10.3390/s21092987

Cited by 67 publications

(41 citation statements)

References 70 publications

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“…RLSTMs are highly rated RNN variants as they have the capability to tackle the problem of long-term dependencies of RNN [44]. In addition, they provide long-term memory, and they have the capacity of addressing the problem of vanishing gradients that might occur when training traditional RNNs [45,46]. They can process an entire sequence of data and not just the single data points.…”

Section: Refined Long Short-term Memory (Rlstm)mentioning

confidence: 99%

Refined LSTM Based Intrusion Detection for Denial-of-Service Attack in Internet of Things

Alimi

Ouahada

Abu‐Mahfouz

et al. 2022

JSAN

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The Internet of Things (IoT) is a promising technology that allows numerous devices to be connected for ease of communication. The heterogeneity and ubiquity of the various connected devices, openness to devices in the network, and, importantly, the increasing number of connected smart objects (or devices) have exposed the IoT network to various security challenges and vulnerabilities which include manipulative data injection and cyberattacks such as a denial of service (DoS) attack. Any form of intrusive data injection or attacks on the IoT networks can create devastating consequences on the individual connected device or the entire network. Hence, there is a crucial need to employ modern security measures that can protect the network from various forms of attacks and other security challenges. Intrusion detection systems (IDS) and intrusion prevention systems have been identified globally as viable security solutions. Several traditional machine learning methods have been deployed as IoT IDS. However, the methods have been heavily criticized for poor performances in handling voluminous datasets, as they rely on domain expertise for feature extraction among other reasons. Thus, there is a need to devise better IDS models that can handle the IoT voluminous datasets efficiently, cater to feature extraction, and perform reasonably well in terms of overall performance. In this paper, an IDS based on redefined long short-term memory deep learning approach is proposed for detecting DoS attacks in IoT networks. The model was tested on benchmark datasets; CICIDS-2017 and NSL-KDS datasets. Three pre-processing procedures, which include encoding, dimensionality reduction, and normalization were deployed for the datasets. Using key classification metrics, experimental results obtained show that the proposed model can effectively detect DoS attacks in IoT networks as it performs better compared to other methods including models from related works.

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Section: Refined Long Short-term Memory (Rlstm)mentioning

confidence: 99%

Refined LSTM Based Intrusion Detection for Denial-of-Service Attack in Internet of Things

Alimi

Ouahada

Abu‐Mahfouz

et al. 2022

JSAN

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“…With a ratio of 0.9 and a k value of 3 for the k-means++ clustering technique, the results showed that using the SLFN classification technique and using the SVM and synthetic minority oversampling technique (SVM-SMOTE) yielded more accurate results than using other values and classification techniques. Similarly, a deep multi-layer classification strategy was suggested by Quddoura et al [110], which consisted of two phases of detection. The first phase entails detecting the presence of an intrusion and the second phase identifies the kind of intrusion.…”

Section: Malicious Traffic In a Cloud Environmentmentioning

confidence: 99%

Intelligent Techniques for Detecting Network Attacks: Review and Research Directions

Aljabri

Aljameel

Mohammad

et al. 2021

Sensors

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The significant growth in the use of the Internet and the rapid development of network technologies are associated with an increased risk of network attacks. Network attacks refer to all types of unauthorized access to a network including any attempts to damage and disrupt the network, often leading to serious consequences. Network attack detection is an active area of research in the community of cybersecurity. In the literature, there are various descriptions of network attack detection systems involving various intelligent-based techniques including machine learning (ML) and deep learning (DL) models. However, although such techniques have proved useful within specific domains, no technique has proved useful in mitigating all kinds of network attacks. This is because some intelligent-based approaches lack essential capabilities that render them reliable systems that are able to confront different types of network attacks. This was the main motivation behind this research, which evaluates contemporary intelligent-based research directions to address the gap that still exists in the field. The main components of any intelligent-based system are the training datasets, the algorithms, and the evaluation metrics; these were the main benchmark criteria used to assess the intelligent-based systems included in this research article. This research provides a rich source of references for scholars seeking to determine their scope of research in this field. Furthermore, although the paper does present a set of suggestions about future inductive directions, it leaves the reader free to derive additional insights about how to develop intelligent-based systems to counter current and future network attacks.

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“…where the Precision is the percentage of correct predictions for a class relative to all the predictions of the same class [61], and the Recall is the percentage of correct predictions for a class relative to all instances that actually belong to the class [62].…”

Section: Evaluation Measuresmentioning

confidence: 99%

An Evolutionary-Based Sentiment Analysis Approach for Enhancing Government Decisions during COVID-19 Pandemic: The Case of Jordan

et al. 2021

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The world has witnessed recently a global outbreak of coronavirus disease (COVID-19). This pandemic has affected many countries and has resulted in worldwide health concerns, thus governments are attempting to reduce its spread and impact on different aspects of life such as health, economics, education, and politics by making emergent decisions and policies (e.g., lockdown and social distancing). These new regulations influenced people’s daily life and cast significant burdens, concerns, and disparities on various population groups. Taking the wrong actions and enforcing bad decisions by some countries result in increasing the contagion rate and more catastrophic results. People start to post their opinions and feelings about their government’s decisions on different social media networks, and the data received through these platforms present a very useful source of information that affects how governments perceive and cope with the current the pandemic. Jordan was one of the top affected countries. In this paper, we proposed a decision support system based on the sentiment analysis mechanism by combining support vector machines with a whale optimization algorithm for automatically tuning the hyperparameters and performing feature weighting. The work is based on a hybrid evolutionary approach that aims to perform sentiment analysis combined with a decision support system to study people’s posts on Facebook to investigate their attitudes and feelings toward the government’s decisions during the pandemic. The government regulations were divided into two periods: the first and latter regulations. Studying public sentiments during these periods allows decision-makers in the government to sense people’s feelings, alert them in case of possible threats, and help in making proactive actions if needed to better handle the current pandemic situation. Five different versions were generated for each of the two collected datasets. The results demonstrate the superiority of the proposed Whale Optimization Algorithm & Support Vector Machines (WOA-SVM) against other metaheuristic algorithms and standard classification models as WOA-SVM has achieved 78.78% in terms of accuracy and 84.64% in term of f-measure, while other standard classification models such as NB, k-NN, J84, and SVM achieved an accuracy of 69.25%, 69.78%, 70.17%, and 69.29%, respectively, with 64.15%, 62.90%, 60.51%, and 59.09% F-measure. Moreover, when comparing our proposed WOA-SVM approach with other metaheuristic algorithms, which are GA-SVM, PSO-SVM, and MVO-SVM, WOA-SVM proved to outperform the other approaches with results of 78.78% in terms of accuracy and 84.64% in terms of F-measure. Further, we investigate and analyze the most relevant features and their effect to improve the decision support system of government decisions.

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A Multi-Layer Classification Approach for Intrusion Detection in IoT Networks Based on Deep Learning

Cited by 67 publications

References 70 publications

Refined LSTM Based Intrusion Detection for Denial-of-Service Attack in Internet of Things

Refined LSTM Based Intrusion Detection for Denial-of-Service Attack in Internet of Things

Intelligent Techniques for Detecting Network Attacks: Review and Research Directions

An Evolutionary-Based Sentiment Analysis Approach for Enhancing Government Decisions during COVID-19 Pandemic: The Case of Jordan

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