| Title | Sagacious Intrusion Detection Strategy in Sensor Network |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Ashraf, S., Ahmed, T. |
| Conference Name | 2020 International Conference on UK-China Emerging Technologies (UCET) |
| Date Published | aug |
| Keywords | ANN, Classification algorithms, composability, computer network security, data corpus, Denial of Service attacks, external attacks, Floods, IDS methodology, Intrusion detection, KNN, logistic regression, malicious attackers, Metrics, naive Bayes, network intrusion detection, neural nets, Prediction algorithms, pubcrawl, regression analysis, resilience, Resiliency, sagacious intrusion detection strategy, Sensors, service attacks, smart appliances, support vector machine, Support vector machines, telecommunication computing, telecommunication security, time division multiple access, Wireless sensor networks, WSN |
| Abstract | Almost all smart appliances are operated through wireless sensor networks. With the passage of time, due to various applications, the WSN becomes prone to various external attacks. Preventing such attacks, Intrusion Detection strategy (IDS) is very crucial to secure the network from the malicious attackers. The proposed IDS methodology discovers the pattern in large data corpus which works for different types of algorithms to detect four types of Denial of service (DoS) attacks, namely, Grayhole, Blackhole, Flooding, and TDMA. The state-of-the-art detection algorithms, such as KNN, Naive Bayes, Logistic Regression, Support Vector Machine (SVM), and ANN are applied to the data corpus and analyze the performance in detecting the attacks. The analysis shows that these algorithms are applicable for the detection and prediction of unavoidable attacks and can be recommended for network experts and analysts. |
| DOI | 10.1109/UCET51115.2020.9205412 |
| Citation Key | ashraf_sagacious_2020 |
Sagacious Intrusion Detection Strategy in Sensor Network