Classifying bank accounts by using transaction data is encouraging in cracking down on illegal financial activities. However, few research simultaneously use heterogenous features, which are embedded in the time series data. In this paper, a two route convolution neural network TRHD-CNN model, fed with two types of heterogeneous feature matrices, is proposed for classifying the bank accounts. TRHD-CNN adopts divide and conquer strategy to extract characteristics from two types of data source independently. The strategy is proved able in mining complementary classification characteristics. We firstly transfer the original log data into a directed and dynamic transaction network. On the basis of that, two feature generation methods are devised for extracting information from local topological structure and time series transaction respectively. A DirectedWalk method is developed in this paper to learning the network vector of vertices used for embedding the neighbor relationship of bank account. The extensive experimental results, conducted on a real bank transaction dataset that contains illegal pyramid selling accounts, show the significant advantage of TRHD-CNN over the existing methods. TRHD-CNN can provide recall scores up to 5.15% higher than competing methods. In addition, the two-route architecture of TRHD-CNN is easy to extend to multi-route scenarios and other fields.

The security of industrial control systems (ICSs) has received a lot of attention in recent years. ICSs were once closed networks. But with the development of IT technologies, ICSs have become connected to the Internet, increasing the potential of cyberattacks. Because ICSs are so tightly linked to human lives, any harm to them could have disastrous implications. As a technique of providing protection, many intrusion detection system (IDS) studies have been conducted. However, because of the complicated network environment and rising means of attack, it is difficult to cover all attack classes, most of the existing classification techniques are hard to deploy in a real environment since they cannot deal with the open set problem. We propose a novel artificial neural network based-methodology to solve this problem. Our suggested method can classify known classes while also detecting unknown classes. We conduct research from two points of view. On the one hand, we use the openmax layer instead of the traditional softmax layer. Openmax overcomes the limitations of softmax, allowing neural networks to detect unknown attack classes. During training, on the other hand, a new loss function termed center loss is implemented to improve detection ability. The neural network model learns better feature representations with the combined supervision of center loss and softmax loss. We evaluate the neural network on NF-BoT-IoT-v2 and Gas Pipeline datasets. The experiments show our proposed method is comparable with the state-of-the-art algorithm in terms of detecting unknown classes. But our method has a better overall classification performance.

Detecting fraudulent accounts by using their transaction networks is helpful for proactively preventing illegal transactions in financial scenarios. In this paper, three convolutional neural network models, i.e., NTD-CNN, TTD-CNN, and HDF-CNN, are created to identify whether a bank account is fraudulent. The three models, same in model structure, are different in types of the input features. Firstly, we embed the bank accounts' historical trading records into a general directed and weighted transaction network. And then, a DirectedWalk algorithm is proposed for learning an account's network vector. DirectedWalk learns social representations of a network's vertices, by modeling a stream of directed and time-related trading paths. The local topological feature, generating by accounts' network vector, is taken as input of NTD-CNN, and TTD-CNN takes time series transaction feature as input. Finally, the two kinds of heterogeneous data, being integrated into a novel feature matrix, are fed into HDF-CNN for classifying bank accounts. The experimental results, conducted on a real bank transaction dataset, show the advantage of HDF-CNN over the existing methods. 2Mathematical Problems in Engineering of CNN structure makes it successful to address many classification problems. In a specific classification scenario, one can tune the structural feature settings of CNN, e.g., the layer numbers, the neuron numbers of each layer, the types of pooling functions, and activation functions, to achieve the best performance.Having abstracted the bank accounts into vertices and their transaction relationships into directed edges, the trading behaviors of accounts can be formed into a directed and weighted network. The transaction relationship information and time series information of bank accounts are embedded into the generated network. As mentioned above, CNN models obtain excellent performance in time series classification and social network. The superiority in convolution kernel and structural design inspires us to employ CNN framework in FAD issue. Therefore, with the labelled data provided by economic investigation experts, three convolutional neural network (CNN) models are proposed to address the FAD issue. The models are listed as follows.(1) A CNN model uses network topological data (NTD) being called NTD-CNN model.(2) A CNN model utilizes time series data (TTD) being referred to as TTD-CNN model. (3) A CNN model employs the two kinds of heterogenous data features (HDF), which are extracted from the former two kinds of data, being short for HDF-CNN model. The experiments on a real dataset, containing illegal pyramid selling accounts, demonstrate the effectiveness of our three CNN models. Except for the TTD-CNN, the other two CNN models achieve better performance than traditional abnormal detection method regarding precision, sensitivity, and F1-score. In summary, the classification performance of HDF-CNN is much better than that of the other three methods. To the best of our knowledge, this is the first time that CNN is ap...

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