Proceedings ArticleDOI
Botnet Attack Detection using Machine Learning
Mustafa Alshamkhany,Wisam Alshamkhany,Mohamed Mansour,Mueez Khan,Salam Dhou,Fadi Aloul +5 more
- pp 203-208
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TLDR
In this paper, four machine learning models based on four classifiers are built: Naive Bayes, K-Nearest Neighbor, Support Vector Machine, and Decision Trees, using 82,000 records from UNSW-NB15 dataset, the decision trees model has yielded the best overall results with 99.89% testing accuracy, 100% precision,100% recall, and 100% $\Gamma-$score in detecting botnet attacks.Abstract:
With the advancement of computers and technology, security threats are also evolving at a fast pace. Botnets are one such security threat which requires a high level of research and focus in order to be eliminated. In this paper, we use machine learning to detect Botnet attacks. Using the Bot-IoT and University of New South Wales (UNSW) datasets, four machine learning models based on four classifiers are built: Naive Bayes, K-Nearest Neighbor, Support Vector Machine, and Decision Trees. Using 82,000 records from UNSW-NB15 dataset, the decision trees model has yielded the best overall results with 99.89% testing accuracy, 100% precision, 100% recall, and 100% $\Gamma-$score in detecting botnet attacks.read more
Citations
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Journal ArticleDOI
Efficient Detection of Botnet Traffic by Features Selection and Decision Trees
TL;DR: In this article, the authors focus on increasing the performance of botnet traffic classification by selecting those features that further increase the detection rate, and they use two feature selection techniques, i.e., Information Gain and Gini Importance, which led to three pre-selected subsets of five, six and seven features.
Journal ArticleDOI
Memory-Efficient Deep Learning for Botnet Attack Detection in IoT Networks
TL;DR: This paper exploits the joint advantages of Long Short-Term Memory Autoencoder, Synthetic Minority Oversampling Technique, and DRNN to develop a memory-efficient DL method, named LS-DRNN, which outperformed state-of-the-art models in botnet attack detection.
Journal ArticleDOI
Imbalanced tabular data modelization using CTGAN and machine learning to improve IoT Botnet attacks detection
TL;DR: In this paper , the CTGAN model is used for tabular data modeling and generation in order to overcome the limitation of traditional data oversampling methods and the limits of understanding complex datasets by the existing GAN models.
Proceedings ArticleDOI
Security Orchestration, Automation, and Response Engine for Deployment of Behavioural Honeypots
TL;DR: A Security Orchestration, Automation, and Response (SOAR) Engine that dynamically deploys custom honeypots inside the internal network infrastructure based on the attacker's behavior and engages attackers on average 3148 seconds.
Journal ArticleDOI
Exposure of Botnets in Cloud Environment by Expending Trust Model with CANFES Classification Approach
Nagendra Prabhu Selvaraj,Sivakumar Paulraj,P. Ramadass,Rajesh Kaluri,Mohammad Shorfuzzaman,Abdulmajeed Alsufyani,Mueen Uddin +6 more
TL;DR: The goal is to be able to detect a larger range of bots and botnets by relying on several techniques called trust model, and the port access verification in trust model is achieved by a Heuristic factorizing algorithm which verifies the port accessibility between client-end-user and client server.
References
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Proceedings ArticleDOI
UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set)
Nour Moustafa,Jill Slay +1 more
TL;DR: Countering the unavailability of network benchmark data set challenges, this paper examines a UNSW-NB15 data set creation which has a hybrid of the real modern normal and the contemporary synthesized attack activities of the network traffic.
Journal ArticleDOI
Towards the development of realistic botnet dataset in the Internet of Things for network forensic analytics: Bot-IoT dataset
TL;DR: In this paper, the authors proposed a new dataset, called Bot-IoT, which incorporates legitimate and simulated IoT network traffic, along with various types of attacks, and evaluated the reliability of the dataset using different statistical and machine learning methods for forensics purposes.
Proceedings ArticleDOI
Detecting P2P botnets through network behavior analysis and machine learning
Sherif Saad,Issa Traore,Ali A. Ghorbani,Bassam Sayed,David Zhao,Wei Lu,John Felix,Payman Hakimian +7 more
TL;DR: This paper proposes a new approach for characterizing and detecting botnets using network traffic behaviors, and focuses on detecting P2P bots, which represent the newest and most challenging types of botnets currently available.
Journal ArticleDOI
Unsupervised intelligent system based on one class support vector machine and Grey Wolf optimization for IoT botnet detection
TL;DR: The main contribution of the proposed method is to detect IoT botnet attacks launched form compromised IoT devices by exploiting the efficiency of a recent swarm intelligence algorithm called Grey Wolf Optimization algorithm (GWO) to optimize the hyperparameters of the OCSVM and at the same time to find the features that best describe the IoT botnets problem.
Proceedings ArticleDOI
An efficient flow-based botnet detection using supervised machine learning
TL;DR: A novel flow-based detection system that relies on supervised machine learning for identifying botnet network traffic and shows that in order to achieve accurate detection traffic flows need to be monitored for only a limited time period and number of packets per flow.
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