DDoS attack detection
As the world increasingly becomes more interconnected, the demand for safety and security is ever-increasing, particularly for industrial networks. This has prompted numerous researchers to investigate different methodologies and techniques suitable for intrusion detection systems (IDS) requirements. Over the years, many studies have proposed various solutions in this regard, including signature-based and machine learning (ML)-based systems. More recently, researchers are considering deep learning (DL)-based anomaly detection approaches.
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Maximum capture length for interface 0: 65000
First timestamp: 1186262976.484933000
Last timestamp: 1186263276.484931000
Unknown encapsulation: 0
IPv4 bytes: 2768247216
IPv4 pkts: 45954067
IPv4 flows: 2860519
Unique IPv4 addresses: 7075
Unique IPv4 source addresses: 7065
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The network attacks are increasing both in frequency and intensity with the rapid growth of internet of things (IoT) devices. Recently, denial of service (DoS) and distributed denial of service (DDoS) attacks are reported as the most frequent attacks in IoT networks. The traditional security solutions like firewalls, intrusion detection systems, etc., are unable to detect the complex DoS and DDoS attacks since most of them filter the normal and attack traffic based upon the static predefined rules.
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