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Advanced Attack Detection and Prevention Systems by Using Botnet
Published in Monika Mangla, Ashok Kumar, Vaishali Mehta, Megha Bhushan, Sachi Nandan Mohanty, Real-Life Applications of the Internet of Things, 2022
Anjanna Matta, Altaf Ahmad, Shantanu Bhattacharya, Shubham Kumar
Under the radar of botnet detection and neutralization systems, the botnet operation is tried to flying is one of the primary goals of the botnet. Resilience technique have a goal of secrecy and communication integrity. The integrity of the communication and security are preserved by using these techniques. However, usage of encrypted communication channel and obfuscated communication protocol can be considered suspicious, and it can be used as a trigger for additional traffic analysis. Fast flux and domain generation algorithm (DGA) are other commonly used techniques that provide resilience of botnet operation.
An efficient botnet detection approach based on feature learning and classification
Published in Journal of Control and Decision, 2023
Fok et al. (2018) initiate a botnet detection system that predicts stealthy botnets. The anticipated model concentrates on predicting the botnet that relies on traffic monitoring. This model extracts four different traffic flow analysis features: the various amounts of transmitted and received bytes and the total amount of packets received and sent. Here, the birch and hierarchical clustering model is utilised to form a clustered network flow. Moreover, this model is independent of various payload signatures and attains a better prediction rate for legitimate and malicious hosts with 100% APR and 0.3% FPR. Even though this approach predicts botnet by analysing various malicious activities’ performance, it concentrates on botnet prediction, and it cannot identify other kinds of HTTP or IRC bots (Ali et al., 2017). Moreover, the anticipated model is exceptionally vulnerable with specific evasion techniques like flow disturbance packets and the adoption of fast-flux and the DGA model as a communication facility to offer higher-level privacy (Moodi & Ghazvini, 2019).
A meta data mining framework for botnet analysis
Published in International Journal of Computers and Applications, 2019
Afzalul Haque, Amrit Venkat Ayyar, Sanjay Singh
Botnets are a significant threat in the world of cybersecurity. Caglyan et al [2] have analyzed and discussed the overall economics of botnets. They have also done a case study on the famous botnet fast flux [3] and inferred that it generates a revenue of $800 million by phishing alone. It signifies the magnitude that botnets has grown into and it requires more attention. The most common type of botnet attack is Distributed Denial-of-Service (DDoS) attack [4], where multiple computers simultaneously try to flood a server’s resources and overwhelm it, thus making it inaccessible to the server’s normal and regular users.