A novel approach for early malware detection

Sharma, Anshul; Singh, Sanjay Kumar

doi:10.1002/ett.3968

Cited by 6 publications

(4 citation statements)

References 43 publications

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“…In the specific case of Early Classification of Time Series (ECTS), an important limitation is that the training time series: (i ) have the same length T ; (ii ) correspond to different i.i.d individuals; (iii ) have a label which characterizes the whole time period of length T . There are obviously applications where this formulation of the problem is relevant [2,47,48,49,50,51,52,53], especially in cases where the start and end of the time series are naturally defined (e.g. a day of trading takes place from 9:30am to 4pm, during the opening hours of the stock exchange).…”

Section: Online Early Decision Makingmentioning

confidence: 99%

Open challenges for Machine Learning based Early Decision-Making research

Bondu¹,

Achenchabe²,

Bifet³

et al. 2022

Preprint

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More and more applications require early decisions, i.e. taken as soon as possible from partially observed data. However, the later a decision is made, the more its accuracy tends to improve, since the description of the problem to hand is enriched over time. Such a compromise between the earliness and the accuracy of decisions has been particularly studied in the field of Early Time Series Classification. This paper introduces a more general problem, called Machine Learning based Early Decision Making (ML-EDM), which consists in optimizing the decision times of models in a wide range of settings where data is collected over time. After defining the ML-EDM problem, ten challenges are identified and proposed to the scientific community to further research in this area. These challenges open important application perspectives, discussed in this paper.

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Section: Online Early Decision Makingmentioning

confidence: 99%

Open challenges for Machine Learning based Early Decision-Making research

Bondu¹,

Achenchabe²,

Bifet³

et al. 2022

Preprint

View full text Add to dashboard Cite

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“…For this template, you just consider particular terms and apply a particular moral relativism score for each term. In a vocabulary of feelings [12], this moral relativism value can be tested. If the overall score is negative, the text will be categorized as pessimistic and the message as beneficial.…”

Section: Delineation Of Supervised Deep Learning Vector Quantization To Detect Iot Malwarementioning

confidence: 99%

“…The Malicious software is classified into many groups, depending on the manner the program is implemented as well as the direction it travels [12]. A virus or computer virus that is self replicated by exporting itself to another application.…”

Section: Introductionmentioning

confidence: 99%

Malicious Attack Identification Using Deep Non Linear Bag-of-Words (FAI-DLB)

Arul¹,

Punidha²,

Gunasekaran³

et al. 2021

Advances in Parallel Computing

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Online media have flourished in modern years to connect with the world. Most of those stuff users share on blogs like facebook, twitter and many other are pessimistic or just middle spirited. Further, an increasingly professional anti - spyware technologies are dependent on Machine Learning(ML) technology to secure malicious consumers. Over the past few years, revolutionary learning approaches have yielded remarkable outcomes and have immediately generated photos, characters and text interpretations of dynamic weak points. The Purple consumer frequency makes the troll and attacker aim an enticing one. The users will learn the controversial topics and techniques used by malware from articles with ties to harmful material and bogus applications. It is essential to build and customize a lot of potential functionality in vulnerability and application developers around the world. To represent a public web firmware assault with deep logistic inference using Extreme Spontaneous Tree (FAI-DLB). A corresponding output device is named harmful or benign by training an FAI-DLB with different modulation clustered with such a normal or anomalous API. It was therefore equipped to locate a suspicious sequence in unidentified firmware of FAI Deep LB. The outcome demonstrates a good actual meaning of 96.25% and a low spyware assault of 0.03%.

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“…Mori et al [26], introduced a framework for early classification on TS by defining the stopping rules as decision criteria and learned the rules by optimizing the accuracy as well as earliness simultaneously. In this line, Recently, Sharma and Singh [34] presented an optimizationbased approach for early malware detection by learning early decision rule through particle swarm optimization.…”

Section: Related Workmentioning

confidence: 99%

Early classification of multivariate data by learning optimal decision rules

Sharma

Singh

2020

Multimed Tools Appl

Self Cite

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Early classification on time series has emerged as an active research area in the field of machine learning. It covers a wide range of applications in agriculture, medical and multimedia systems, including drought prediction, health monitoring, event detection, and many more. The early classification aims to predict the class label of a time series as soon as possible without waiting for the complete series. A critical issue in early classification is the learning of decision policy that determines the adequacy of the collected data required for reliable class prediction. It is more challenging for Multivariate Time Series (MTS) data, where the decision depends on multiple variables to achieve a trade-off between earliness and accuracy. Therefore, this work proposes an optimization-based early classification model for MTS data based on optimal decision rule learning. The proposed model adopts a two-layered approach. The first layer employs the Gaussian process probabilistic classifiers for each variable in MTS that provides the class probabilities at the successive time steps in the series. The second layer defines Early Stopping Rule (ESR) that performs the class prediction task. The ESR learns its parameters through the particle swarm optimization by simultaneously minimizing the misclassification cost and delaying the decision cost. This work has utilized publicly available MTS datasets to validate the proposed early classification model. The experimental results show that the proposed model achieves promising results in terms of accuracy and earliness compared to existing methods.

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A novel approach for early malware detection

Cited by 6 publications

References 43 publications

Open challenges for Machine Learning based Early Decision-Making research

Open challenges for Machine Learning based Early Decision-Making research

Malicious Attack Identification Using Deep Non Linear Bag-of-Words (FAI-DLB)

Early classification of multivariate data by learning optimal decision rules

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