Jammer Detection based on Artificial Neural Networks: A Measurement Study
Jammer Detection based on Artificial Neural Networks: A Measurement Study
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基于人工神经网络的干扰检测：测量研究
DOI：
10.1145/3324921.3328788
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Günes Karabulut
中科院分区：
文献类型：
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作者：
Selen Gecgel;Caner Goztepe;Günes Karabulut
由于无线传输环境的广播性质，无线网络容易受到干扰攻击。干扰攻击的效果可以进一步增加，因为干扰者可以将他们的信号集中在发射机的参考信号上，从而进一步恶化传输性能。在本文中，我们的目标是利用神经网络来联合确定干扰机的存在及其攻击特征。实现了两种神经网络结构：深层卷积神经网络和深层递归神经网络。干扰器和发射机的存在以及干扰器的类型是通过使用基于正交频分多路传输的信令在软件定义的无线电上实现的不同场景集来确定的。为了提高检测性能，采用了先验技术。测试结果表明，该方法能够有效地对干扰攻击进行检测和分类，准确率在85%左右。
Wireless networks are prone to jamming attacks due to the broadcast nature of the wireless transmission environment. The effect of jamming attacks can be further increased as the jammers can focus their signals on reference signals of the transmitters, to further deteriorate the transmission performance. In this paper, we aim to jointly determine the presence of the jammer, along with its attack characteristics by using neural networks. Two neural network architectures are implemented; deep convolutional neural networks and deep recurrent neural networks. The presence of jammer and the transmitter and the type of the jammer is determined through a diverse set of scenarios that are implemented on software defined radios using orthogonal frequency division multiplexing based signaling. To improve the detection performance, prepossessing techniques are applied. Test results show that the proposed approach can effectively detect and classify the jamming attacks with around 85% accuracy.