.png){kind=icon}
返回
Network Attack Classification in IoT Using Support Vector Machines
.png){kind=icon}
OA
AI
DOI：10.3390/jsan10030058.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Machine learning (ML) techniques learn a system by observing it. Events and occurrences in the network define what is expected of the network's operation. It is for this reason that ML techniques are used in the computer network security field to detect unauthorized intervention. In the event of suspicious activity, the result of the ML analysis deviates from the definition of expected normal network activity and the suspicious activity becomes apparent. Support vector machines (SVM) are ML techniques that have been used to profile normal network activity and classify it as normal or abnormal. They are trained to configure an optimal hyperplane that classifies unknown input vectors' values based on their positioning on the plane. We propose to use SVM models to detect malicious behavior within low-power, low-rate and short range networks, such as those used in the Internet of Things (IoT). We evaluated two SVM approaches, the C-SVM and the OC-SVM, where the former requires two classes of vector values (one for the normal and one for the abnormal activity) and the latter observes only normal behavior activity. Both approaches were used as part of an intrusion detection system (IDS) that monitors and detects abnormal activity within the smart node device. Actual network traffic with specific network-layer attacks implemented by us was used to create and evaluate the SVM detection models. It is shown that the C-SVM achieves up to 100% classification accuracy when evaluated with unknown data taken from the same network topology it was trained with and 81% accuracy when operating in an unknown topology. The OC-SVM that is created using benign activity achieves at most 58% accuracy.
Keyword:
intrusion detection systems
anomaly detection
Internet of Things
support vector machines
期刊
IF:
4.2
论文数:
615
被引数:
1.6K
机构
引用论文
Prospective assessment of NGS-detectable mutations in CML patients with nonoptimal response: the NEXT-in-CML study.png)
BloodOAAI
BloodIF0
OAAI分享收藏A Machine-Learning-Based Technique for False Data Injection Attacks Detection in Industrial IoT
PREAI
分享收藏
查看更多内容