As an artificial neural network method, self-organizing mapping facilities efficient complete and visualize high-dimensional data topology representation, valid in a number of applications such as network intrusion detection. However, there remains a challenge to accurately depict the topology of network traffic data with unbalanced distribution, which deteriorates the performance of e.g. DoS attack detection. Hence, we propose a new model of the ''statistic-enhanced directed batch growth self-organizing mapping'', renew the definition of the growth threshold used to evaluate/control neuron expansion, and first introduce the inner distribution factor for fine-grained data distinguishing. The numerical experiments based on two datasets, KDD99, and CICIDS2017, demonstrate that the key performance in DoS attack detection including the detection rate, the false positive rate, and the training time are greatly enhanced thanks to the statistic concepts consulted in the proposed model. INDEX TERMS DoS attack detection, statistic-enhanced directed batch growing self-organizing mapping, growth threshold, inner distribution factor.

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A new evaluation mechanism was proposed to enhance the representation of data topology in the directed batch growth hierarchical self-organizing mapping. In the proposed mechanism, the growth threshold and the correlation worked in a case-sensitive manner through the statistic calculation of the input data. Since the proposed model enabled a more thorough representation of data topology from both the horizontal and the vertical directions, it naturally held great potential in detecting various traffic attacks. Numerical experiments of network intrusion detection were carried out on the datasets of KDD99, Moore and CICIDS2017, where the good performance validated the superiority of the proposed method.INDEX TERMS Network intrusion detection, growth hierarchical self-organizing mapping, statistical enhancement, growth threshold, correlation.

A prominent security threat to unmanned aerial vehicle (UAV) is to capture it by GPS spoofing, in which the attacker manipulates the GPS signal of the UAV to capture it. This paper introduces an anti-spoofing model to mitigate the impact of GPS spoofing attack on UAV mission security. In this model, linear regression (LR) is used to predict and model the optimal route of UAV to its destination. On this basis, a countermeasure mechanism is proposed to reduce the impact of GPS spoofing attack. Confrontation is based on the progressive detection mechanism of the model. In order to better ensure the flight security of UAV, the model provides more than one detection scheme for spoofing signal to improve the sensitivity of UAV to deception signal detection. For better proving the proposed LR anti-spoofing model, a dynamic Stackelberg game is formulated to simulate the interaction between GPS spoofer and UAV. In particular, for GPS spoofer, it is worth mentioning that for the scenario that the UAV is cheated by GPS spoofing signal in the mission environment of the designated route is simulated in the experiment. In particular, UAV with the LR anti-spoofing model, as the leader in this game, dynamically adjusts its response strategy according to the deception’s attack strategy when upon detection of GPS spoofer’s attack. The simulation results show that the method can effectively enhance the ability of UAV to resist GPS spoofing without increasing the hardware cost of the UAV and is easy to implement. Furthermore, we also try to use long short-term memory (LSTM) network in the trajectory prediction module of the model. The experimental results show that the LR anti-spoofing model proposed is far better than that of LSTM in terms of prediction accuracy.

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