As advances in networking technology help to connect industrial control networks with the Internet, the threat from spammers, attackers and criminal enterprises has also grown accordingly. However, traditional Network Intrusion Detection System makes significant use of pattern matching to identify malicious behaviors and have bad performance on detecting zero-day exploits in which a new attack is employed. In this paper, a novel method of anomaly detection in industrial control network is proposed based on RNN-GBRBM feature decoder. The method employ network packets and extract high-quality features from raw features which is selected manually. A modified RNN-RBM is trained using the normal traffic in order to learn feature patterns of the normal network behaviors. Then the test traffic is analyzed against the learned normal feature pattern by using osPCA to measure the extent to which the test traffic resembles the learned feature pattern. Moreover, we design a semi-supervised incremental updating algorithm in order to improve the performance of the model continuously. Experiments show that our method is more efficient in anomaly detection than other traditional approaches for industrial control network.

In this paper, we consider the crosscorrelation of two interleaved sequences of period 4N constructed by Gong and Tang which has been proved to possess optimal autocorrelation. Results show that the interleaved sequences achieve the largest crosscorrelation value 4.

The surface potential built across vacuum deposited phthalocyanine (Pc) films on aluminum electrode was investigated by means of electro-modulation spectroscopy. The sandwich-type cells with thin air gap, which becomes a good insulator were used in order to avoid the influence of charge injection. The existence of the surface potential at the metal/organic-material interface induced 1f referenced electro-reflectance (ER) signals. As a result, the surface potential built across vacuum deposited Pc films on aluminum electrode was estimated to be 1.25 V.

A simplified algorithm for check node processing of extended min-sum (EMS) q-ary LDPC decoders is presented in this letter. Compared with the bubble check algorithm, the so-called dynamic bubble-check (DBC) algorithm aims to further reduce the computational complexity for the elementary check node (ECN) processing. By introducing two flag vectors in ECN processing, The DBC algorithm can use the minimum number of comparisons at each step. Simulation results show that, DBC algorithm uses significantly fewer comparison operations than the bubble check algorithm, and presents no performance loss compared with standard EMS algorithm on AWGN channels.

In this paper, we compare two generalized cyclotomic binary sequences with length 2p2 in terms of the linear complexity. One classical sequence is defined using the method introduced by Ding and Helleseth, while the other modified sequence is defined in a slightly different manner. We show that the modified sequence has linear complexity of 2p2, which is higher than that of the classical one.