Journal of Algorithms & Computational Technology

Firewalls, especially at large organizations, process high velocity internet traffic and flag suspicious events and activities. Flagged events can be benign, such as misconfigured routers, or malignant, such as a hacker trying to gain access to a specific computer. Confounding this is that flagged events are not always obvious in their danger and the high velocity nature of the problem. Current work in firewall log analysis is manual intensive and involves manpower hours to find events to investigate. This is predominantly achieved by manually sorting firewall and intrusion detection/prevention system log data. This work aims to improve the ability of analysts to find events for cyber forensics analysis. A tabulated vector approach is proposed to create meaningful state vectors from time-oriented blocks. Multivariate and graphical analysis is then used to analyze state vectors in human–machine collaborative interface. Statistical tools, such as the Mahalanobis distance, factor analysis, and histogram matrices, are employed for outlier detection. This research also introduces the breakdown distance heuristic as a decomposition of the Mahalanobis distance, by indicating which variables contributed most to its value. This work further explores the application of the tabulated vector approach methodology on collected firewall logs. Lastly, the analytic methodologies employed are integrated into embedded analytic tools so that cyber analysts on the front-line can efficiently deploy the anomaly detection capabilities.

A joint manifold learning fusion (JMLF) approach is proposed for nonlinear or mixed sensor modalities with large streams of data. The multimodal sensor data are stacked to form joint manifolds, from which the embedded low intrinsic dimensionalities are discovered for moving targets. The intrinsic low dimensionalities are mapped to resolve the target locations. The JMLF framework is tested on digital imaging and remote sensing image generation scenes with mid-wave infrared (WMIR) data augmented with distributed radio-frequency (RF) Doppler data. Eight manifold learning methods are explored to train the system with the neighborhood preserving embedding showing promise for robust target tracking using video–radio-frequency fusion. The JMLF method shows a 93% improved accuracy as compared to a standard target tracking (e.g., Kalman-filter based) approach.

Parts supplier selection (PSS) is an important part of supply chain management of manufacturing enterprise. In the PSS process, the values of evaluation indicators are often uncertain and incomplete and the importance degrees of evaluation indicators are often instable. To solve this problem, a PSS framework of manufacturing enterprise based on D-S evidence theory is proposed. The indicator system for PSS is established, and the indicators are divided into three categories: quantitative, comprehensive qualitative and direct qualitative. The initial indicator values are processed by membership grade method to obtain the tendency degree. A two-order weighted D-S evidence theory model is constructed to evaluate the screened candidate suppliers. A manufacturing enterprise application case is given finally to illustrate the correctness and feasibility of the proposed framework.

Noise data in text are one of the main factors affecting the quality of text categorization. A parallel noise data elimination algorithm based on principal component analysis method and term frequency-inverse document frequency method for the noise data issue of massive text categorization is proposed. Five types of noise data which may occur during text categorization process are analyzed and summarized in this paper. Before text categorization, a redundant noise elimination algorithm based on key feature selection is presented for redundant noise features. During the process of text categorization, the error noise detection algorithm is given for inaccurate noise features. The proposed method is compared with other four typical noise processing methods in different noise ratios on two common corpora. The results show that the proposed method is feasible and can maintain more stable and excellent classification performance and lower running time.

How to get maximal benefit within a range of risk in securities market is a very interesting and widely concerned issue. Meanwhile, as there are many complex factors that affect securities’ activity, such as the risk and uncertainty of the benefit, it is very difficult to establish an appropriate model for investment. Aiming at solving the curse of dimension and model disaster caused by the problem, we use the approximate dynamic programming to set up a Markov decision model for the multi-time segment portfolio with transaction cost. A model-based actor-critic algorithm under uncertain environment is proposed, where the optimal value function is obtained by iteration on the basis of the constrained risk range and a limited number of funds, and the optimal investment of each period is solved by using the dynamic planning of limited number of fund ratio. The experiment indicated that the algorithm could get a stable investment, and the income could grow steadily.

In this paper, a new six-dimensional hyperchaotic system is proposed and some basic dynamical properties including bifurcation diagrams, Lyapunov exponents and phase portraits are investigated. Furthermore, the electronic circuit of this novel hyperchaotic system is simulated on the Multisim platform, and the simulation results are agreed well with the numerical simulation of the same hyperchaotic system on the Matlab platform. Finally, a control method based on Deep Belief Network is proposed to track and control the proposed hyperchaotic system. In this method, the function of the hyperchaotic system is studied by Deep Belief Network and a high precision fitting function is obtained. Then a controller which is composed of the fitting function and the tracking reference signal is designed to achieve the tracking control of hyperchaotic systems. Simulation results verify the effectiveness and feasibility of this method.

Intuitionistic fuzzy preference relations can take membership degrees, non-membership degrees, and hesitancy degrees into account during decision making. It has good practicability and flexibility in dealing with fuzzy and uncertain information. As for analytic network process, it is performed by thinking over the interaction and feedback relationships between criteria and indices, so that an effective method is provided for multi-criteria decision making. An index system with network structure for evaluating the bonds is presented, and a comprehensive method by combining the advantages of intuitionistic fuzzy preference relations and analytic network process is proposed to select and rank the bonds. A case study is given by the proposed method as well.

Green supply chain management is a strategy which strengthens and integrates environmental consideration into whole supply chain. The green strategic supplier plays an important role in the implementation of green supply chain strategy. In the selection methodology of a strategic green supplier, some special requirements are needed which are different from the traditional supplier selection practices. In this paper, we combine the generalized weighted BM operator with Pythagorean 2-tuple linguistic numbers to propose the generalized Pythagorean 2-tuple linguistic-weighted Bonferroni mean operator, and then the multiple attribute decision-making methods are developed based on this operator. Finally, we use an example for green supplier selection to illustrate the multiple attribute decision-making process of the proposed methods.