2020 IEEE 23rd International Multitopic Conference (INMIC) 2020
DOI: 10.1109/inmic50486.2020.9318216
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Abstract: 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. However, th… Show more

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Cited by 74 publications
(5 citation statements)
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References 13 publications
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“…Based on deep learning, Hussain et al [19] used CICDDoS2019 to simulate an IoT system. They developed an IDS with a CNN classifier to detect Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks.…”
Section: Anomaly-based Intrusion Detection Systems (Ads) Related Workmentioning
confidence: 99%
“…Based on deep learning, Hussain et al [19] used CICDDoS2019 to simulate an IoT system. They developed an IDS with a CNN classifier to detect Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks.…”
Section: Anomaly-based Intrusion Detection Systems (Ads) Related Workmentioning
confidence: 99%
“…Feature selection plays a significant role in the performance of a machine learning model. After preprocessing the data, we applied a Logistic Regression (LR) algorithm due to its efficient implementation in the existing literature [3,18]. Using the LR algorithm, we selected the ten most significant features to train and test the machine learning models: ['frame.time_delta', 'tcp.time_delta', 'tcp.flags.ack', 'tcp.flags.push', 'tcp.flags.reset', 'mqtt.hdrflags', 'mqtt.msgtype', 'mqtt.qos', 'mqtt.retain', 'mqtt.ver'].…”
Section: Features Selectionmentioning
confidence: 99%
“…Therefore, it is very challenging to design a security mechanism for IoT devices. The manufacturing industries are producing a massive amount of IoT products to create low-cost IoT devices in a short period [3,18]. Due to the race to the market for capturing the market as earlier as possible, the manufacturers are giving less importance to the device security [3].…”
Section: Introductionmentioning
confidence: 99%
“…The work utilizes an offline IDS to collect and analyze information from a variety of IoT networks, as well as to identify DoS attacks on them. Hussain et al [36] have carried out experiments on network attack detection through recognition of DDoS attack patterns using Naïve Bayes method. De Lima Filho et al [37] have performed analysis on DoS/DDoS attack pattern recognition in IoT environment using ResNet.…”
Section: Dos/ddos Attack and Its Counter Measures In Iot Networkmentioning
confidence: 99%