A Novel Distributed Machine Learning Framework for Semi-Supervised Detection of Botnet Attacks

Kaur, Gagandeep

doi:10.1109/ic3.2018.8530562

Cited by 14 publications

(5 citation statements)

References 6 publications

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“…• Unsupervised or semisupervised clustering requires only a training dataset of normal system behavior to form a corresponding cluster. Then, any event sufficiently different from the previously learned cluster is considered anomalous system behavior [28,29].…”

Section: Basic Requirements Of Ddos Detection In Modern Information Systemsmentioning

confidence: 99%

Deep Semisupervised Learning-Based Network Anomaly Detection in Heterogeneous Information Systems

Lutsiv¹,

Maksymyuk²,

Beshley³

et al. 2022

Computers, Materials &Amp; Continua

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The extensive proliferation of modern information services and ubiquitous digitization of society have raised cybersecurity challenges to new levels. With the massive number of connected devices, opportunities for potential network attacks are nearly unlimited. An additional problem is that many low-cost devices are not equipped with effective security protection so that they are easily hacked and applied within a network of bots (botnet) to perform distributed denial of service (DDoS) attacks. In this paper, we propose a novel intrusion detection system (IDS) based on deep learning that aims to identify suspicious behavior in modern heterogeneous information systems. The proposed approach is based on a deep recurrent autoencoder that learns time series of normal network behavior and detects notable network anomalies. An additional feature of the proposed IDS is that it is trained with an optimized dataset, where the number of features is reduced by 94% without classification accuracy loss. Thus, the proposed IDS remains stable in response to slight system perturbations, which do not represent network anomalies. The proposed approach is evaluated under different simulation scenarios and provides a 99% detection accuracy over known datasets while reducing the training time by an order of magnitude.

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Section: Basic Requirements Of Ddos Detection In Modern Information Systemsmentioning

confidence: 99%

Deep Semisupervised Learning-Based Network Anomaly Detection in Heterogeneous Information Systems

Lutsiv¹,

Maksymyuk²,

Beshley³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…Botnet attack is a champion among the most lethal attacks. A botnet may be a compromised note in the frameworks that are linked over the web and are controlled distantly by a criminal to perform malignant action without consent and approval [42]. The essential purpose of criminals to perform Botnet is a cash-related advantage by performing Denial of service assault, Phishing, spamming, etc.…”

Section: Security Issues In Fog Computingmentioning

confidence: 99%

A Hybrid Deep Learning Approach for Bottleneck Detection in IoT

et al. 2022

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Cloud computing is perhaps the most enticing innovation in the present figuring situation. It gives an expense-effective arrangement by diminishing the enormous forthright expense for purchasing equipment foundations and processing power. Fog computing is an additional help to cloud infrastructure by utilizing a portion of the less register concentrated undertaking at the edge devices, reducing the end client's reaction time, such as the Internet of Things (IoT). However, most IoT devices are resource-constrained, and there are many devices that cyber-attacks could target. Cyber-attacks such as Denial of Service (DoS), Distributed Denial of Service (DDoS), and botnets are still significant threats in the IoT environment. Botnets are currently the most significant threat on the internet. A botnet is a group of compromised systems connected through the internet and controlled by a criminal to perform a malicious activity without authentication and permission. A botnet can compromise the system and steal the data. It can also be used to perform attacks, like Phishing, spamming, and more. To overcome the critical issue, the authors exhibit a novel botnet attack detection approach that could be utilized in fog computing situations to dispense with the attack using the programmable nature of the SDN environment. In order to demonstrate the effectiveness of the proposed technique, extensive experimentation has been performed on the N_BaIoT dataset for botnet attack signature in various edge devices, i.e., Internet of Things (IoT), to evaluate the standard parameters. We have also compared the proposed technique with other deep learning algorithms. The proposed technique shows promising detection accuracy and time utilization results.

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“…We have categorized malware detection existing methods for IoT devices [ 12 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 ] into two methods based on machine learning and deep learning. Researchers [ 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 ] have proposed a variety of machine learning intrusion detection methods. Table 1 critically evaluates the existing machine learning methods.…”

Section: Related Workmentioning

confidence: 99%

“…Costa et al [ 20 ] introduced an intrusion detection system using Support Vector Domain. To determine if network traffic is an attack or not, a novelty detection model based on SSPV-SVDD as a classifier and SMO as a solution is used.…”

Section: Related Workmentioning

confidence: 99%

Malware Detection in Internet of Things (IoT) Devices Using Deep Learning

Riaz

Latif

Usman

et al. 2022

Sensors

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Internet of Things (IoT) devices usage is increasing exponentially with the spread of the internet. With the increasing capacity of data on IoT devices, these devices are becoming venerable to malware attacks; therefore, malware detection becomes an important issue in IoT devices. An effective, reliable, and time-efficient mechanism is required for the identification of sophisticated malware. Researchers have proposed multiple methods for malware detection in recent years, however, accurate detection remains a challenge. We propose a deep learning-based ensemble classification method for the detection of malware in IoT devices. It uses a three steps approach; in the first step, data is preprocessed using scaling, normalization, and de-noising, whereas in the second step, features are selected and one hot encoding is applied followed by the ensemble classifier based on CNN and LSTM outputs for detection of malware. We have compared results with the state-of-the-art methods and our proposed method outperforms the existing methods on standard datasets with an average accuracy of 99.5%.

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A Novel Distributed Machine Learning Framework for Semi-Supervised Detection of Botnet Attacks

Cited by 14 publications

References 6 publications

Deep Semisupervised Learning-Based Network Anomaly Detection in Heterogeneous Information Systems

Deep Semisupervised Learning-Based Network Anomaly Detection in Heterogeneous Information Systems

A Hybrid Deep Learning Approach for Bottleneck Detection in IoT

Malware Detection in Internet of Things (IoT) Devices Using Deep Learning

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