Deep Nested Clustering Auto-Encoder for Anomaly-Based Network Intrusion Detection

Anomaly-based intrusion detection system(AIDS) plays an increasingly important role in detecting complex,multi-stage network attacks, especially zero-day attacks. Although there have been improvements both in practical applications and the research environment, there are still many unresolved accuracy-related concerns. The two fundamental limitations that contribute to these concerns are: i) the succinct, concise, latent representation learning of the normal network data, and ii) the optimization volume of normal regions in latent space. Recent studies have suggested many ways to learn the latent representation of normal network data in a semi-supervised manner to construct AIDS. However, these approaches are still affected by the above limitations,mainly due to the inability to process high data dimensionality or ineffectively explore the underlying architecture of the data. In this paper, we propose a novel Deep Nested Clustering Auto Encoder (DNCAE ) model to thoroughly overcome the aforementioned difficulties and improve the performance o fnetwork attack detection. The proposed model consists of two nested Deep Auto-Encoders(DAE) to learn the informative and tighter data representation space. In addition, the DNCAE model integrates the clustering technique into the latent layer of the outer DAE to learn the optimal arrangement of datapoints in the latent space. This harmonious combination allows us to effectively deal with the limitations outlined. The performance of the proposed model is evaluated using standard datasets including NSL-KDD,UNSW-NB15, and six scenarios of CIC-IDS2017(Tuesday, Wednesday, Thursday-Morning, Friday-Morning, Friday-Afternoon Port Scan,Friday-Afternoon DDoS).The experimental results strongly confirm that the proposed model clearly out performs th baselines and the existing methods for network anomaly detection. IndexTerms—Latent Representation, DeepAuto-Encoder, Clustering, AnomalyDetection, Intrusion Detection Syste

ICRS-Filter: A randomized direct search algorithm for constrained nonconvex optimization problems

This work presents a novel algorithm and its implementation for the stochastic optimization of generally constrained Nonlinear Programming Problems (NLP). The basic algorithm adopted is the Iterated Control Random Search (ICRS) method of Casares and Banga (1987) with modifications such that random points are generated strictly within a bounding box defined by bounds on all variables. The ICRS algorithm serves as an initial point determination method for launching gradient-based methods that converge to the nearest local minimum. The issue of constraint handling is addressed in our work via the use of a filter based methodology, thus obviating the need for use of the penalty functions as in the basic ICRS method presented in Banga and Seider (1996),which handles only bound constrained problems. The proposed algorithm, termed ICRS-Filter, is shown to be very robust and reliable in producing very good or global solutions for most of the several case studies examined in this contribution.This is the author accepted manuscript. The final version is available from Elsevier via http://dx.doi.org/10.1016/j.cherd.2015.12.00

Salmonella typhimurium Suppresses Tumor Growth via the Pro-Inflammatory Cytokine Interleukin-1 beta

Although strains of attenuated Salmonella typhimurium and wild-type Escherichia coli show similar tumor-targeting capacities, only S. typhimurium significantly suppresses tumor growth in mice. The aim of the present study was to examine bacteria-mediated immune responses by conducting comparative analyses of the cytokine profiles and immune cell populations within tumor tissues colonized by E. coli or attenuated Salmonellae. CT26 tumor-bearing mice were treated with two different bacterial strains: S. typhimurium defective in ppGpp synthesis (Delta ppGpp Salmonellae) or wild-type E. coli MG1655. Cytokine profiles and immune cell populations in tumor tissue colonized by these two bacterial strains were examined at two time points based on the pattern of tumor growth after Delta ppGpp Salmonellae treatment: 1) when tumor growth was suppressed ('suppression stage') and 2) when they began to re-grow ('re-growing stage'). The levels of IL-1 beta and TNF-alpha were markedly increased in tumors colonized by Delta ppGpp Salmonellae. This increase was associated with tumor regression; the levels of both IL-1 beta and TNF-alpha returned to normal level when the tumors started to re-grow. To identify the immune cells primarily responsible for Salmonellae-mediated tumor suppression, we examined the major cell types that produce IL-1 beta and TNF-alpha. We found that macrophages and dendritic cells were the main producers of TNF-alpha and IL-1 beta. Inhibiting IL-1 beta production in Salmonellae-treated mice restored tumor growth, whereas tumor growth was suppressed for longer by local administration of recombinant IL-1 beta or TNF-alpha in conjunction with Salmonella therapy. These findings suggested that IL-1 beta and TNF-alpha play important roles in Salmonella-mediated cancer therapy. A better understanding of host immune responses in Salmonella therapy may increase the success of a given drug, particularly when various strategies are combined with bacteriotherapy.111715Ysciescopu

Optimal neighborhood indexing for protein similarity search

Background: Similarity inference, one of the main bioinformatics tasks, has to face an exponential growth of the biological data. A classical approach used to cope with this data flow involves heuristics with large seed indexes. In order to speed up this technique, the index can be enhanced by storing additional information to limit the number of random memory accesses. However, this improvement leads to a larger index that may become a bottleneck. In the case of protein similarity search, we propose to decrease the index size by reducing the amino acid alphabet.\ud \ud Results: The paper presents two main contributions. First, we show that an optimal neighborhood indexing combining an alphabet reduction and a longer neighborhood leads to a reduction of 35% of memory involved into the process, without sacrificing the quality of results nor the computational time. Second, our approach led us to develop a new kind of substitution score matrices and their associated e-value parameters. In contrast to usual matrices, these matrices are rectangular since they compare amino acid groups from different alphabets. We describe the method used for computing those matrices and we provide some typical examples that can be used in such comparisons. Supplementary data can be found on the website http://bioinfo.lifl.fr/reblosum.\ud \ud Conclusions: We propose a practical index size reduction of the neighborhood data, that does not negatively affect the performance of large-scale search in protein sequences. Such an index can be used in any study involving large protein data. Moreover, rectangular substitution score matrices and their associated statistical parameters can have applications in any study involving an alphabet reduction

Strained graphene structures: from valleytronics to pressure sensing

Due to its strong bonds graphene can stretch up to 25% of its original size without breaking. Furthermore, mechanical deformations lead to the generation of pseudo-magnetic fields (PMF) that can exceed 300 T. The generated PMF has opposite direction for electrons originating from different valleys. We show that valley-polarized currents can be generated by local straining of multi-terminal graphene devices. The pseudo-magnetic field created by a Gaussian-like deformation allows electrons from only one valley to transmit and a current of electrons from a single valley is generated at the opposite side of the locally strained region. Furthermore, applying a pressure difference between the two sides of a graphene membrane causes it to bend/bulge resulting in a resistance change. We find that the resistance changes linearly with pressure for bubbles of small radius while the response becomes non-linear for bubbles that stretch almost to the edges of the sample. This is explained as due to the strong interference of propagating electronic modes inside the bubble. Our calculations show that high gauge factors can be obtained in this way which makes graphene a good candidate for pressure sensing.Comment: to appear in proceedings of the NATO Advanced Research Worksho

Synthesis and biological characterisation of 18F-SIG343 and 18F-SIG353, novel and high selectivity σ2 radiotracers, for tumour imaging properties

Sigma2 (σ2) receptors are highly expressed in cancer cell lines and in tumours. Two novel selective 18F-phthalimido σ2 ligands, 18F-SIG343 and 18F-SIG353, were prepared and characterised for their potential tumour imaging properties. © 2013 Nguyen et al.; licensee Springer.© Nguyen et al.; licensee Springer. 2013 This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited