Cross layer based miss detection ratio under variable rate for intrusion detection in WLAN

Among the key components of a smart grid, advanced metering infrastructure (AMI) has become the preferred target for network intrusion due to its bidirectional communication and Internet connection. Intrusion detection systems (IDSs) can monitor abnormal information in the AMI network, so they are an important means by which to solve network intrusion. However, the existing methods exhibit a poor ability to detect intrusions in AMI, because they cannot comprehensively consider the temporal and global characteristics of intrusion information. To solve these problems, an AMI intrusion detection model based on the cross-layer feature fusion of a convolutional neural networks (CNN) and long short-term memory (LSTM) networks is proposed in the present work. The model is composed of CNN and LSTM components connected in the form of a cross-layer; the CNN component recognizes regional features to obtain global features, while the LSTM component obtain periodic features by memory function. The two types of features are aggregated to obtain comprehensive features with multi-domain characteristics, which can more accurately identify intrusion information in AMI. Experiments based on the KDD Cup 99 and NSL-KDD datasets demonstrate that the proposed cross-layer feature-fusion CNN-LSTM model is superior to other existing methods.

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Intrusion Detection Techniques based on Cross Layer For Wireless Local Area Networks

INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY ◽

10.24297/ijct.v5i2.3528 ◽

2013 ◽

Vol 5 (2) ◽

pp. 94-97

Author(s):

Dr. Vinod Kumar ◽

Mr Sandeep Agarwal ◽

Mr Avtar Singh

Keyword(s):

Intrusion Detection ◽

False Positive ◽

False Positive Rate ◽

Threshold Value ◽

Base Station ◽

Cross Layer ◽

Delivery Ratio ◽

Detection Techniques ◽

Weight Value ◽

Positive Rate

In this paper, we propose to design a cross-layer based intrusion detection technique for wireless networks. In this technique a combined weight value is computed from the Received Signal Strength (RSS) and Time Taken for RTS-CTS handshake between sender and receiver (TT). Since it is not possible for an attacker to assume the RSS exactly for a sender by a receiver, it is an useful measure for intrusion detection. We propose that we can develop a dynamic profile for the communicating nodes based on their RSS values through monitoring the RSS values periodically for a specific Mobile Station (MS) or a Base Station (BS) from a server. Monitoring observed TT values at the server provides a reliable passive detection mechanism for session hijacking attacks since it is an unspoofable parameter related to its measuring entity. If the weight value is greater than a threshold value, then the corresponding node is considered as an attacker. By suitably adjusting the threshold value and the weight constants, we can reduce the false positive rate, significantly. By simulation results, we show that our proposed technique attains low misdetection ratio and false positive rate while increasing the packet delivery ratio.

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