Comparison of Deep Learning and the Classical Machine Learning Algorithm for the Malware Detection

Recently, Deep Learning has been showing promising results in various Artificial Intelligence applications like image recognition, natural language processing, language modeling, neural machine translation, etc. Although, in general, it is computationally more expensive as compared to classical machine learning techniques, their results are found to be more effective in some cases. Therefore, in this paper, we investigated and compared one of the Deep Learning Architecture called Deep Neural Network (DNN) with the classical Random Forest (RF) machine learning algorithm for the malware classification. We studied the performance of the classical RF and DNN with 2, 4 & 7 layers architectures with the four different feature sets, and found that irrespective of the features inputs, the classical RF accuracy outperforms the DNN.Comment: 11 Pages, 1 figur

DRLDO A Novel DRL based De obfuscation System for Defence Against Metamorphic Malware

In this paper, we propose a novel mechanism to normalise metamorphic and obfuscated malware down at the opcode level and hence create an advanced metamorphic malware de-obfuscation and defence system. We name this system as DRLDO, for deep reinforcement learning based de-obfuscator. With the inclusion of the DRLDO as a sub-component, an existing Intrusion Detection System could be augmented with defensive capabilities against ‘zero-day’ attack from obfuscated and metamorphic variants of existing malware. This gains importance, not only because there exists no system till date that use advance DRL to intelligently and automatically normalise obfuscation down even to the opcode level, but also because the DRLDO system does not mandate any changes to the existing IDS. The DRLDO system does not even mandate the IDS’ classifier to be retrained with any new dataset containing obfuscated samples. Hence DRLDO could be easily retrofitted into any existing IDS deployment. We designed, developed, and conducted experiments on the system to evaluate the same against multiple-simultaneous attacks from obfuscations generated from malware samples from a standardised dataset that contain multiple generations of malware. Experimental results prove that DRLDO was able to successfully make the otherwise undetectable obfuscated variants of the malware detectable by an existing pre-trained malware classifier. The detection probability was raised well above the cut-off mark to 0.6 for the classifier to detect the obfuscated malware unambiguously. Further, the de-obfuscated variants generated by DRLDO achieved a very high correlation (of ≈ 0.99) with the base malware. This observation validates that the DRLDO system is actually learning to de-obfuscate and not exploiting a trivial trick

An Evaluation of the Genotoxic Effects of Seed Decoction of Cassia tora L. (Leguminosae) in Allium cepa Model

Cytological effects of Cassia tora seed decoction were evaluated in Allium cepa root tip cells. Bulbs were grown in pure tap water (controls, Gr. I) and also in six concentrations (0.15 mg/ml, 0.31 mg/ml, 0.62 mg/ml, 1.25 mg/ml, 2.5 mg/ml and 5 mg/ml) of C.tora seed decoction in tap water (experimental, Grs. II). Parameters of study were \u27mean root length\u27 and morphology i.e. colour and shape of root tips at 72 hr of cultivation and \u27mitotic Index\u27, chromosomal aberrations and abnormal mitosis at 48 hr of cultivation. Physico-chemical characterization of decoction was also made. No changes in the morphology of root tips occurred at any concentration of C.tora seed decoction, however, change in color did occur at all concentrations. Mitotic index and mean root length remained unaffected at first two concentrations but all higher four concentrations caused progressive mitodepression hence a decline in root growth occurred. No abnormal mitosis and no chromosomal aberration occurred at all at any concentration. Results suggest that water soluble constituents of C.tora seeds could only lower mitosis but not caused any adverse genotoxic effects in mitotically dividing A.cepa root cells under laboratory condition

Phenotypic Data from Inbred Parents Can Improve Genomic Prediction in Pearl Millet Hybrids

Pearl millet is a non-model grain and fodder crop adapted to extremely hot and dry environments globally. In India, a great deal of public and private sectors’ investment has focused on developing pearl millet single cross hybrids based on the cytoplasmic-genetic male sterility (CMS) system, while in Africa most pearl millet production relies on open pollinated varieties. Pearl millet lines were phenotyped for both the inbred parents and hybrids stage. Many breeding efforts focus on phenotypic selection of inbred parents to generate improved parental lines and hybrids. This study evaluated two genotyping techniques and four genomic selection schemes in pearl millet. Despite the fact that 6· more sequencing data were generated per sample for RAD-seq than for tGBS, tGBS yielded more than 2· as many informative SNPs (defined as those having MAF \u3e 0.05) than RAD-seq. A genomic prediction scheme utilizing only data from hybrids generated prediction accuracies (median) ranging from 0.73-0.74 (1000- grain weight), 0.87-0.89 (days to flowering time), 0.48-0.51 (grain yield) and 0.72-0.73 (plant height). For traits with little to no heterosis, hybrid only and hybrid/inbred prediction schemes performed almost equivalently. For traits with significant mid-parent heterosis, the direct inclusion of phenotypic data from inbred lines significantly (P \u3c 0.05) reduced prediction accuracy when all lines were analyzed together. However, when inbreds and hybrid trait values were both scored relative to the mean trait values for the respective populations, the inclusion of inbred phenotypic datasets moderately improved genomic predictions of the hybrid genomic estimated breeding values. Here we show that modern approaches to genotyping by sequencing can enable genomic selection in pearl millet. While historical pearl millet breeding records include a wealth of phenotypic data from inbred lines, we demonstrate that the naive incorporation of this data into a hybrid breeding program can reduce prediction accuracy, while controlling for the effects of heterosis per se allowed inbred genotype and trait data to improve the accuracy of genomic estimated breeding values for pearl millet hybrids

Modified DSLM technique for PAPR reduction in FBMC system

In the modern age, wireless communication has taken on a wide range of applications in different aspects of a human life. Ever increasing demand for higher data rates led the interest of research in FBMC (Filter Bank Multi-Carrier) as a new transmission system for the next-generation mobile communication system. FBMC is a multicarrier technique which utilized filters at the transmitting and the receiving side of the system. It is considered as one of the most promising waveform for fifth generation mobile communication system (5G). But, high value of Peak Average Power Ratio (PAPR) is considered to be one of the giant problems in FBMC. The PAPR reduction techniques used in OFDM cannot be utilized in FBMC due to its overlapping structure. Hence, new PAPR reduction techniques for FBMC have diverted the attention of researchers. In this work, dispersive selective mapping (DSLM) PAPR reduction technique is proposed. Further, the proposed technique is compared to selected mapping (SLM) and clipping PAPR reduction techniques. The simulation results show that the proposed DSLM technique gives the better performance in terms of bit error rate (BER), PAPR and complexity as compared to the SLM and clipping PAPR reduction techniques