Candidate working set strategy based SMO algorithm in support vector machine

Sequential minimal optimization (SMO) is quite an efficient algorithm for training the support vector machine. The most important step of this algorithm is the selection of the working set, which greatly affects the training speed. The feasible direction strategy for the working set selection can decrease the objective function, however, may augment to the total calculation for selecting the working set in each of the iteration. In this paper, a new candidate working set (CWS) Strategy is presented considering the cost on the working set selection and cache performance. This new strategy can select several greatest violating samples from Cache as the iterative working sets for the next several optimizing steps, which can improve the efficiency of the kernel cache usage and reduce the computational cost related to the working set selection. The results of the theory analysis and experiments demonstrate that the proposed method can reduce the training time, especially on the large-scale datasets.

Zero-day malware detection based on supervised learning algorithms of API call signatures

Zero-day or unknown malware are created using code obfuscation techniques that can modify the parent code to produce offspring copies which have the same functionality but with different signatures. Current techniques reported in literature lack the capability of detecting zero-day malware with the required accuracy and efficiency. In this paper, we have proposed and evaluated a novel method of employing several data mining techniques to detect and classify zero-day malware with high levels of accuracy and efficiency based on the frequency of Windows API calls. This paper describes the methodology employed for the collection of large data sets to train the classifiers, and analyses the performance results of the various data mining algorithms adopted for the study using a fully automated tool developed in this research to conduct the various experimental investigations and evaluation. Through the performance results of these algorithms from our experimental analysis, we are able to evaluate and discuss the advantages of one data mining algorithm over the other for accurately detecting zero-day malware successfully. The data mining framework employed in this research learns through analysing the behavior of existing malicious and benign codes in large datasets. We have employed robust classifiers, namely Naïve Bayes (NB) Algorithm, k−Nearest Neighbor (kNN) Algorithm, Sequential Minimal Optimization (SMO) Algorithm with 4 differents kernels (SMO - Normalized PolyKernel, SMO – PolyKernel, SMO – Puk, and SMO- Radial Basis Function (RBF)), Backpropagation Neural Networks Algorithm, and J48 decision tree and have evaluated their performance. Overall, the automated data mining system implemented for this study has achieved high true positive (TP) rate of more than 98.5%, and low false positive (FP) rate of less than 0.025, which has not been achieved in literature so far. This is much higher than the required commercial acceptance level indicating that our novel technique is a major leap forward in detecting zero-day malware. This paper also offers future directions for researchers in exploring different aspects of obfuscations that are affecting the IT world today.

An application of novel clustering technique for information security

This article presents experimental results devoted to a new application of the novel clustering technique introduced by the authors recently. Our aim is to facilitate the application of robust and stable consensus functions in information security, where it is often necessary to process large data sets and monitor outcomes in real time, as it is required, for example, for intrusion detection. Here we concentrate on the particular case of application to profiling of phishing websites. First, we apply several independent clustering algorithms to a randomized sample of data to obtain independent initial clusterings. Silhouette index is used to determine the number of clusters. Second, we use a consensus function to combine these independent clusterings into one consensus clustering . Feature ranking is used to select a subset of features for the consensus function. Third, we train fast supervised classification algorithms on the resulting consensus clustering in order to enable them to process the whole large data set as well as new data. The precision and recall of classifiers at the final stage of this scheme are critical for effectiveness of the whole procedure. We investigated various combinations of three consensus functions, Cluster-Based Graph Formulation (CBGF), Hybrid Bipartite Graph Formulation (HBGF), and Instance-Based Graph Formulation (IBGF) and a variety of supervised classification algorithms. The best precision and recall have been obtained by the combination of the HBGF consensus function and the SMO classifier with the polynomial kernel.

Hedgehog partial agonism drives warburg-lie metabolism in muscle and brown fat

Diabetes, obesity, and cancer affect upward of 15% of the world’s population. Interestingly, all three diseases juxtapose dysregulated intracellular signaling with altered metabolic state. Exactly which genetic factors define stable metabolic set points in vivo remains poorly understood. Here, we show that hedgehog signaling rewires cellular metabolism. We identify a cilium-dependent Smo-Ca2+-Ampk axis that triggers rapid Warburg-like metabolic reprogramming within minutes of activation and is required for proper metabolic selectivity and flexibility. We show that Smo modulators can uncouple the Smo-Ampk axis from canonical signaling and identify cyclopamine as one of a new class of “selective partial agonists,” capable of concomitant inhibition of canonical and activation of noncanonical hedgehog signaling. Intriguingly, activation of the Smo-Ampk axis in vivo drives robust insulin-independent glucose uptake in muscle and brown adipose tissue. These data identify multiple noncanonical endpoints that are pivotal for rational design of hedgehog modulators and provide a new therapeutic avenue for obesity and diabetes.

Automatic generation of meta classifiers with large levels for distributed computing and networking

This paper is devoted to a case study of a new construction of classifiers. These classifiers are called automatically generated multi-level meta classifiers, AGMLMC. The construction combines diverse meta classifiers in a new way to create a unified system. This original construction can be generated automatically producing classifiers with large levels. Different meta classifiers are incorporated as low-level integral parts of another meta classifier at the top level. It is intended for the distributed computing and networking. The AGMLMC classifiers are unified classifiers with many parts that can operate in parallel. This make it easy to adopt them in distributed applications. This paper introduces new construction of classifiers and undertakes an experimental study of their performance. We look at a case study of their effectiveness in the special case of the detection and filtering of phishing emails. This is a possible important application area for such large and distributed classification systems. Our experiments investigate the effectiveness of combining diverse meta classifiers into one AGMLMC classifier in the case study of detection and filtering of phishing emails. The results show that new classifiers with large levels achieved better performance compared to the base classifiers and simple meta classifiers classifiers. This demonstrates that the new technique can be applied to increase the performance if diverse meta classifiers are included in the system.

Smoothened activates breast cancer stem-like cell and promotes tumorigenesis and metastasis of breast cancer

Smoothened (Smo) is a G protein-coupled receptor protein encoded by the Smo gene of the hedgehog signalling pathway, which is thought to play an important role in maintaining organ patterning, cell differentiation and self-renewal. The possible role of Smo in the process of tumorigenesis and metastasis of breast cancer still remains unclear. The present experiments were to investigate the effect of Smo on activating breast cancer stem-like CD44(+)CD24(-) cells and the tumorigenesis and metastasis of breast cancer. By injected CD44(+)CD24(-) cells (1×10(4)) into the cleared fat pad of NOD/SCID mice, it was observed that CD44(+)CD24(-) cells possess higher tumor-initiating capacity and metastasis properties than equal numbers of non-CD44(+)CD24(-) cells. The mRNA and protein expressions of Smo in CD44(+)CD24(-) cells were higher than those in non-CD44(+)CD24(-) cells, indicating that Smo may play a role in maintaining breast cancer stem cell features. qRT-PCR results revealed that expressions of STAT3, Bcl-2 and cyclinD1 mRNA in MCF-7 cells were decreased after transfected by Smo siRNA. In addition, the expressions of MMP-2 and MMP-9 were downregulated in MCF-7 cells after Smo expression was inhibited. Smo inhibition may be a possible therapeutic target that potentially suppresses breast tumor formation and development.