Deep Neural Networks based Meta-Learning for Network Intrusion Detection

The digitization of different components of industry and inter-connectivity among indigenous networks have increased the risk of network attacks. Designing an intrusion detection system to ensure security of the industrial ecosystem is difficult as network traffic encompasses various attack types, including new and evolving ones with minor changes. The data used to construct a predictive model for computer networks has a skewed class distribution and limited representation of attack types, which differ from real network traffic. These limitations result in dataset shift, negatively impacting the machine learning models' predictive abilities and reducing the detection rate against novel attacks. To address the challenges, we propose a novel deep neural network based Meta-Learning framework; INformation FUsion and Stacking Ensemble (INFUSE) for network intrusion detection. First, a hybrid feature space is created by integrating decision and feature spaces. Five different classifiers are utilized to generate a pool of decision spaces. The feature space is then enriched through a deep sparse autoencoder that learns the semantic relationships between attacks. Finally, the deep Meta-Learner acts as an ensemble combiner to analyze the hybrid feature space and make a final decision. Our evaluation on stringent benchmark datasets and comparison to existing techniques showed the effectiveness of INFUSE with an F-Score of 0.91, Accuracy of 91.6%, and Recall of 0.94 on the Test+ dataset, and an F-Score of 0.91, Accuracy of 85.6%, and Recall of 0.87 on the stringent Test-21 dataset. These promising results indicate the strong generalization capability and the potential to detect network attacks.Comment: Pages: 15, Figures: 10 and Tables:

De novo assembly and characterisation of chloroplast genomes of broccoli cvs. Marathon and Green sprout using next generation sequencing

The genus Brassica (family Brassicaceae) includes nutritionally and economically important species such as Brassica napus, Brassica rapa, and Brassica oleracea. Many varieties of B. oleracea are available in various morphological forms including nutritive vegetables such as cauliflower (var. botrytis), Brussels sprouts (var. gemmifera), kales and collards (var. acephala), kohlrabi (var. gongylodes), cabbage (var. capitata), and broccoli (var. italica). Objective of the present study was to sequence chloroplast genomes of two cultivars of broccoli: Marathon and Green sprout. The sequencing was done by next generation sequencing. The analysis was performed using Velvet, Geneious, GeSeq, tRNAscan-SE, ARAGORN, OrganellarGenomeDRAW, IRscope and REPuter. The genomes of both cultivars showed highly similar quadripartite structure of 153,365 bp. The LSC (83,136 bp) and SSC (17,835 bp) regions were separated by a pair of IR (26,197 bp) region. In total, 114 unique genes were present in both species, including 80 protein-coding, 30 tRNA and 4 rRNA genes, while 18 genes were duplicated in IRs. The highest amino acid encoding frequency was found for Leucine whereas cysteine was the least encoding amino acid. The codon usage analyses confirmed high encoding efficacy of codons that ended at 3'-end with A/T. Repeat analyses of these genomes revealed 415 microsatellites and 36 oligonucleotide repeats. Microsatellites motifs were mostly comprised of A/T instead of C/G. The comparative analyses confirmed the presence of 17 substitutions between both cultivars. Overall, this study will increase knowledge about the chloroplast genomes of broccoli and will provide a resource for chloroplast genetic engineering of this important edible plant.Higher Education Commission of Pakistan 7407/Federal/NRPU/RD/HEC/201