217 research outputs found

    Hybrid Approach for Botnet Detection Using K-Means and K-Medoids with Hopfield Neural Network

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    In the last few years, a number of attacks and malicious activities have been attributed to common channels between users. A botnet is considered as an important carrier of malicious and undesirable briskness. In this paper, we propose a support vector machine to classify botnet activities according to k-means, k-medoids, and neural network clusters. The proposed approach is based on the features of transfer control protocol packets. System performance and accuracy are evaluated using a predefined data set. Results show the ability of the proposed approach to detect botnet activities with high accuracy and performance in a short execution time. The proposed system provides 95.7% accuracy rate with a false positive rate less than or equal to 3%

    Performance evaluation of botnet detection using machine learning techniques

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    Cybersecurity is seriously threatened by Botnets, which are controlled networks of compromised computers. The evolving techniques used by botnet operators make it difficult for traditional methods of botnet identification to stay up. Machine learning has become increasingly effective in recent years as a means of identifying and reducing these hazards. The CTU-13 dataset, a frequently used dataset in the field of cybersecurity, is used in this study to offer a machine learning-based method for botnet detection. The suggested methodology makes use of the CTU-13, which is made up of actual network traffic data that was recorded in a network environment that had been attacked by a botnet. The dataset is used to train a variety of machine learning algorithms to categorize network traffic as botnet-related/benign, including decision tree, regression model, naïve Bayes, and neural network model. We employ a number of criteria, such as accuracy, precision, and sensitivity, to measure how well each model performs in categorizing both known and unidentified botnet traffic patterns. Results from experiments show how well the machine learning based approach detects botnet with accuracy. It is potential for use in actual world is demonstrated by the suggested system’s high detection rates and low false positive rates

    Robust Botnet Detection Techniques for Mobile and Network Environments

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    Cybercrime costs large amounts of money and resources every year. This is because it is usually carried out using different methods and at different scales. The use of botnets is one of the most common successful cybercrime methods. A botnet is a group of devices that are used together to carry out malicious attacks (they are connected via a network). With the widespread usage of handheld devices such as smartphones and tablets, networked devices are no longer limited to personal computers and laptops. Therefore, the size of networks (and therefore botnets) can be large. This means it is not surprising for malicious users to target different types of devices and platforms as cyber-attack victims or use them to launch cyber-attacks. Thus, robust automatic methods of botnet detection on different platforms are required. This thesis addresses this problem by introducing robust methods for botnet family detection on Android devices as well as by generally analysing network traffic. As for botnet detection on Android, this thesis proposes an approach to identify botnet Android botnet apps by means of source code mining. The approach analyses the source code via reverse engineering and data mining techniques for several examples of malicious and non-malicious apps. Two methods are used to build datasets. In the first, text mining is performed on the source code and several datasets are constructed, and in the second, one dataset is created by extracting source code metrics using an open-source tool. Additionally, this thesis introduces a novel transfer learning approach for the detection of botnet families by means of network traffic analysis. This approach is a key contribution to knowledge because it adds insight into how similar instances can exist in datasets that belong to different botnet families and that these instances can be leveraged to enhance model quality (especially for botnet families with small datasets). This novel approach is denoted Similarity Based Instance Transfer, or SBIT. Furthermore, the thesis presents a proposed extended version designed to overcome a weakness in the original algorithm. The extended version is called CB-SBIT (Class Balanced Similarity Based Instance Transfer)

    Deep fused flow and topology features for botnet detection basing on pretrained GCN

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    Nowadays, botnets have become one of the major threats to cyber security. The characteristics of botnets are mainly reflected in bots network behavior and their intercommunication relationships. Existing botnet detection methods use flow features or topology features individually, which overlook the other type of feature. This affects model performance. In this paper, we propose a botnet detection model which uses graph convolutional network (GCN) to deeply fuse flow features and topology features for the first time. We construct communication graphs from network traffic and represent nodes with flow features. Due to the imbalance of existing public traffic flow datasets, it is impossible to train a GCN model on these datasets. Therefore, we use a balanced public communication graph dataset to pretrain a GCN model, thereby guaranteeing its capacity for identify topology features. We then feed the communication graph with flow features into the pretrained GCN. The output from the last hidden layer is treated as the fusion of flow and topology features. Additionally, by adjusting the number of layers in the GCN network, the model can effectively detect botnets under both C2 and P2P structures. Validated on the public ISCX2014 dataset, our approach achieves a remarkable recall rate 92.90% and F1-score 92.76% for C2 botnets, alongside recall rate 94.66% and F1-score of 92.35% for P2P botnets. These results not only demonstrate the effectiveness of our method, but also outperform the performance of the currently leading detection models
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