DoS (Denial of Service) attacks are becoming one of the most serious security threats to global networks. We analyze the existing DoS detection methods and defense mechanisms in depth. In recent years, K-Means and improved variants have been widely examined for security intrusion detection, but the detection accuracy to data is not satisfactory. In this paper we propose a multi-dimensional space feature vector expansion K-Means model to detect threats in the network environment. The model uses a genetic algorithm to optimize the weight of K-Means multi-dimensional space feature vector, which greatly improves the detection rate against 6 typical Dos attacks. Furthermore, in order to verify the correctness of the model, this paper conducts a simulation on the NSL-KDD data set. The results show that the algorithm of multi-dimensional space feature vectors expansion K-Means improves the recognition accuracy to 96.88%. Furthermore, 41 kinds of feature vectors in NSL-KDD are analyzed in detail according to a large number of experimental training. The feature vector of the probability positive return of security attack detection is accurately extracted, and a comparison chart is formed to support subsequent research. A theoretical analysis and experimental results show that the multi-dimensional space feature vector expansion K-Means algorithm has a good application in the detection of DDos attacks.

The predictability of network traffic is an important and widely studied topic because it can lead to the solutions to get more efficient dynamic bandwidth allocation, admission control, congestion control and better performance wireless networks. Support vector machine (SVM) is a novel type of learning machine based on statistical learning theory, can solve small-sample learning problems. The work presented in this paper aims to examine the feasibility of applying SVM to predict actual WLAN traffic. We study one-step-ahead prediction and multi-step-ahead prediction without any assumption on the statistical property of actual WLAN traffic. We also evaluate the performance of different prediction models such as ARIMA, FARIMA, artificial neural network, and wavelet-based model using three actual WLAN traffic. The results show that the SVM-based model for predicting WLAN traffic is reasonable and feasible and has the best performance among the above mentioned prediction models.

Energy-saving is crucial in wireless sensor networks. In this letter, we address the issue of lossless packing aggregation with the aim of reducing energy lost in cluster-model wireless sensor networks. We propose a performance model based on the bin packing problem to study the packing efficiency. It is evaluated in terms of control header size, and validated by simulations.

In recent years, with the continuous development of the Internet of Things, radio frequency identification (RFID) technology has also been widely concerned. The computing power of low cost tags is limited because of their high hardware requirements. Symmetric encryption algorithms and asymmetric encryption algorithms, such as RSA, DES, AES, etc., cannot be suitable for low cost RFID protocols. Therefore, research on RFID security authentication protocols with low cost and high security has become a focus. Recently, an ultralightweight RFID authentication protocol LP2UF was proposed to provide security and prevent all possible attacks. However, it is discovered that a type of desynchronization attack can successfully break the proposed scheme. To overcome the vulnerability against desynchronization attacks, we propose here a new ultra-lightweight RFID two-way authentication protocol based on stream cipher technology that uses only XOR. The stream cipher is employed to ensure security between readers and tags. Analysis shows that our protocol can effectively resist position tracking attacks, desynchronization attacks, and replay attacks.