Anomaly detection in network traffic using dynamic graph mining with a sparse autoencoder.

Network based attacks on ecommerce websites can have serious economic consequences. Hence, anomaly detection in dynamic network traffic has become an increasingly important research topic in recent years. This paper proposes a novel dynamic Graph and sparse Autoencoder based Anomaly Detection algorithm named GAAD. In GAAD, the network traffic over contiguous time intervals is first modelled as a series of dynamic bipartite graph increments. One mode projection is performed on each bipartite graph increment and the adjacency matrix derived. Columns of the resultant adjacency matrix are then used to train a sparse autoencoder to reconstruct it. The sum of squared errors between the reconstructed approximation and original adjacency matrix is then calculated. An online learning algorithm is then used to estimate a Gaussian distribution that models the error distribution. Outlier error values are deemed to represent anomalous traffic flows corresponding to possible attacks. In the experiment, a network emulator was used to generate representative ecommerce traffic flows over a time period of 225 minutes with five attacks injected, including SYN scans, host emulation and DDoS attacks. ROC curves were generated to investigate the influence of the autoencoder hyper-parameters. It was found that increasing the number of hidden nodes and their activation level, and increasing sparseness resulted in improved performance. Analysis showed that the sparse autoencoder was unable to encode the highly structured adjacency matrix structures associated with attacks, hence they were detected as anomalies. In contrast, SVD and variants, such as the compact matrix decomposition, were found to accurately encode the attack matrices, hence they went undetected

Cooperative co-evolutionary module identification with application to cancer disease module discovery

none10siModule identification or community detection in complex networks has become increasingly important in many scientific fields because it provides insight into the relationship and interaction between network function and topology. In recent years, module identification algorithms based on stochastic optimization algorithms such as evolutionary algorithms have been demonstrated to be superior to other algorithms on small- to medium-scale networks. However, the scalability and resolution limit (RL) problems of these module identification algorithms have not been fully addressed, which impeded their application to real-world networks. This paper proposes a novel module identification algorithm called cooperative co-evolutionary module identification to address these two problems. The proposed algorithm employs a cooperative co-evolutionary framework to handle large-scale networks. We also incorporate a recursive partitioning scheme into the algorithm to effectively address the RL problem. The performance of our algorithm is evaluated on 12 benchmark complex networks. As a medical application, we apply our algorithm to identify disease modules that differentiate low- and high-grade glioma tumors to gain insights into the molecular mechanisms that underpin the progression of glioma. Experimental results show that the proposed algorithm has a very competitive performance compared with other state-of-the-art module identification algorithms.noneHe, S and Jia, G and Zhu, Z and Tennant, DA and Huang, Q and Tang, K and Liu, J and Musolesi, M and Heath, JK and Yao, XHe, S and Jia, G and Zhu, Z and Tennant, DA and Huang, Q and Tang, K and Liu, J and Musolesi, M and Heath, JK and Yao,

Community detection in complex networks using evolutionary computation

In real world many complex systems can be naturally represented as complex networks of which one distinctive feature is the community structure. The community detection, i.e., identifying the community structure, provides insight into the relationship and interaction between network function and topology and has become increasingly important in many scientific fields. In this thesis, we firstly propose a cooperative coevolutionary module identification algorithm named CoCoMi to address the scalability problem when detecting community structures in especially medium and large-scale complex networks. Secondly, we propose a consensus community detection algorithm based on the multimodal optimization and fast Surprise named CoCoMOS to detect community structures in complex networks. Thirdly, we propose an adaptive ensemble selection and multimodal optimization based consensus community detection algorithm named MASCOD to find high quality and stable consensus partitions of community structures in complex networks. The performance of these three proposed algorithms is evaluated on some well-known social, artificial and biological complex networks and experimental results demonstrate that all these three proposed algorithms have very competitive performance compared with other state-of-the-art community detection algorithms

An improved (μ+λ)-constrained differential evolution for constrained optimization

Jia G, Wang Y, Cai Z, Jin Y. An improved (μ+λ)-constrained differential evolution for constrained optimization. Information Sciences. 2013;222:302-322.To overcome the main drawbacks of (μ + λ)-constrained differential evolution ((μ + λ)-CDE) [45], this paper proposes an improved version of (μ + λ)-CDE, named ICDE, to solve constrained optimization problems (COPs). ICDE mainly consists of an improved (μ + λ)-differential evolution (IDE) and a novel archiving-based adaptive tradeoff model (ArATM). Therein, IDE employs several mutation strategies and the binomial crossover of differential evolution (DE) to generate the offspring population. Moreover, a new mutation strategy named “current-to-rand/best/1” is proposed by making use of the current generation number in IDE. Since the population may undergo three situations during the evolution (i.e., the infeasible situation, the semi-feasible situation, and the feasible situation), like (μ + λ)-CDE, ArATM designs one constraint-handling mechanism for each situation. However, unlike (μ + λ)-CDE, in the constraint-handling mechanism of the infeasible situation, the hierarchical nondominated individual selection scheme is utilized, and an individual archiving technique is proposed to maintain the diversity of the population. Furthermore, in the constraint-handling mechanism of the semi-infeasible situation, the feasibility proportion of the combined population consisting of the parent population and the offspring population is used to convert the objective function of each individual. It is noteworthy that ICDE adopts a fixed tolerance value for the equality constraints. In addition, in this paper two criteria are used to compute the degree of constraint violation of each individual in the population, according to the difference among the violations of different constraints. By combining IDE with ArATM, ICDE has the capability to maintain a good balance between the diversity and the convergence of the population during the evolution. The performance of ICDE has been tested on 24 well-known benchmark test functions collected for the special session on constrained real-parameter optimization of the 2006 IEEE Congress on Evolutionary Computation (IEEE CEC2006). The experimental results demonstrate that ICDE not only overcomes the main drawbacks of (μ + λ)-CDE but also obtains very competitive performance compared with other state-of-the-art methods for constrained optimization in the community of constrained evolutionary optimization

An External Archive-Based Constrained State Transition Algorithm for Optimal Power Dispatch

This paper proposes an external archive-based constrained state transition algorithm (EA-CSTA) with a preference trade-off strategy for solving the power dispatch optimization problem in the electrochemical process of zinc (EPZ). The optimal power dispatch problem aims to obtain the optimal current density schedule to minimize the cost of power consumption with some rigorous technology and production constraints. The current density of each production equipment in different power stages is restricted by technology and production requirements. In addition, electricity price and current density are considered comprehensively to influence the cost of power consumption. In the process of optimization, technology and production restrictions are difficult to be satisfied, which are modeled as nonconvex equality constraints in the power dispatch optimization problem. Moreover, multiple production equipment and different power supply stages increase the amount of decision variables. In order to solve this problem, an external archive-based constrained state transition algorithm (EA-CSTA) is proposed. The external archive strategy is adopted for maintaining the diversity of solutions to increase the probability of finding the optima of power dispatch optimization problem. Moreover, a preference trade-off strategy is designed to improve the global search performance of EA-CSTA, and the translation transformation in state transition algorithm is modified to improve the local search ability of EA-CSTA. Finally, the experimental results indicate that the proposed method is more efficient compared with other approaches in previous papers for the optimal power dispatch. Furthermore, the proposed method significantly reduces the cost of power consumption, which not only guides the production process of zinc electrolysis but also alleviates the pressure of the power grid load

Oxidative Stress Induced by Selenium Deficiency Contributes to Inflammation, Apoptosis and Necroptosis in the Lungs of Calves

Selenium is an essential trace element for health that can only be obtained through food. However, the pathological processes of selenium deficiency in cattle have received little attention. This study investigated the effects of selenium deficiency on oxidative stress, apoptosis, inflammation, and necroptosis in the lungs of weaning calves compared with healthy calves as controls. The lung selenium content and the expression of 11 selenoproteins mRNA in selenium-deficient calves were substantially reduced compared with the controls. Pathological results showed engorged alveolar capillaries, thickened alveolar septa, and diffuse interstitial inflammation throughout the alveolar septa. The levels of GSH and T-AOC, as well as the CAT, SOD, and TrxR activities, were significantly decreased compared with healthy calves. MDA and H2O2 were significantly elevated. Meanwhile, the apoptosis activation in the Se-D group was validated. Next, in the Se-D group, several pro-inflammatory cytokines showed higher expression. Further research revealed that the lungs in the Se-D group experienced inflammation via hyperactive NF-κB and MAPK pathways. The high level of expression of c-FLIP, MLKL, RIPK1, and RIPK3 indicated that necroptosis also causes lung damage during selenium deficiency

Community Detection Using Cooperative Co-evolutionary Differential Evolution

Abstract. In many scientific fields, from biology to sociology, community detection in complex networks has become increasingly important. This paper, for the first time, introduces Cooperative Co-evolution framework for detecting communities in complex networks. A Bias Grouping scheme is proposed to dynamically decompose a complex network into smaller subnetworks to handle large-scale networks. We adopted Differential Evolution (DE) to optimize network modularity to search for an optimal partition of a network. We also designed a novel mutation operator specifically for community detection. The resulting algorithm, Cooperative Co-evolutionary DE based Community Detection (CCDECD) is evaluated on 5 small to large scale real-world social and biological networks. Experimental results show that CCDECD has very competitive performance compared with other state-of-the-art community detection algorithms.