Experimental Research Articles

Abstract: In the modern era of technology, malicious software, or malware, holds a serious security hazard as computer users, businesses, and governments see an uptick in malware attacks. In attempts to identify unknown malware, current malware detection solutions use dynamic as well as static examination of malware signatures and behavior patterns, which takes time and is unsuccessful. Modern malware employs evasive strategies such as metamorphosis and polymorphism to rapidly alter its actions and produce a multitude of variants. Machine learning algorithms (MLAs) are being used more and more to do an efficient malware analysis because new malware is primarily versions of current malware. Extensive feature engineering, feature learning, and feature representation are needed for this. It is likely to fully eliminate the feature engineering stage by utilizing sophisticated MLAs like deep learning. Even though there have been a few fresh investigations in the field, the algorithms' performance is skewed by the training set. It is a prerequisite to reduce bias and figure out these techniques holistically in order to develop new, improved techniques for successful zero-day malware detection. This paper fills a vacuum in the literature by comparing and contrasting deep learning architectures with standard MLAs for malware detection, classification, and categorization using public and private datasets. The public and private dataset’s train and test splits, which were gathered during distinctly different periods, are not connected to one another in the experimental study. Furthermore, we provide a new method of image processing with ideal parameters for deep learning architectures and MLAs. In response to a thorough scientific assessment of these methodologies, deep learning architectures perform more efficiently than traditional MLAs. All in all, our work suggests a scalable and multimodal deep learning system for real-time malware detection through visual means. An improved technique for successful zero-day malware detection is the visualization and deep learning architectures for static, dynamic, and image processing based blended methods in a big data environment.

Abstract The article introduces an experimental study of glottal stops that are generated by h aspiré (H) in French (il [ʔ] hoche la tête). To date the phenomenon is merely mentioned in passing, and evidence only comes from native speaker intuitions and cursory personal observation. Participants pronounced verbs that either did (hocher) or did not (aimer) begin with an H, whereby the left context was controlled for: the preceding word could end in a vowel (tu hoches/aimes), in a consonant (il hoche/aime) or in a liaison consonant (LC nous hochons/aimons). Results confirm the observation made in the literature regarding the high variability of H: lexical (elision is much more frequent in j’harcèle than in j’hais), inter-speaker (some participants chose unelided je for 10 out of 12 H verbs, while others only for 4 H verbs) and intra-speaker (participants pronounced vous [z] hissez with liaison, while they chose je hisse in a multiple choice-based pretest). Results also confirmed that H is indeed a glottal stop creator: glottal stops occur much more often before H-initial than before V-initial words. The glottal stop rate also depended on the left context: while LC + H (nous hochons) and C + H (il hoche) are statistically indistinguishable, both are significantly distinct from V + H (tu hoches). This suggests that glottal stop insertion is sensitive to all types of preceding consonants, whether they are pronounced (C + H) or not (LC + H). This result is relevant in the debate on French liaison where it was claimed that (some) LCs are epenthetic, that is absent from phonological computation when unpronounced: this view is challenged by the experimental evidence. On the analytic side, the article argues that all glottal stops that occur stand in Strong Position, i.e. word-initially or after a consonant {#,C}__ (Ségéral, Philippe & Tobias Scheer. 2001. La Coda-Miroir. Bulletin de la Société de Linguistique de Paris 96. 107–152). The word-initial position is in fact domain-initial, and it is a long standing observation in the literature that H sets off its word into a separate domain. Thus even glottal stops in V + H (tu hoches) that appear to occur in intervocalic position may in fact be domain-initial V + [H]. The question then is what kind of domain could be responsible for the (rare) presence of glottal stops in V + V (tu aimes): such a domain V + [V] cannot stem from H, nor can it be of morpho-syntactic origin. It is argued that these domains are production planning domains in the sense of Wagner (2012. Locality in phonology and production planning. McGill Working Papers in Linguistics 22. 1–18 and following).

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