N-grams Based Supervised Machine Learning Model for Mobile Agent Platform Protection against Unknown Malicious Mobile Agents

Bagga, Pallavi; Hans, Rahul; Sharma, Vipul

doi:10.9781/ijimai.2017.03.013

Cited by 4 publications

(4 citation statements)

References 19 publications

(21 reference statements)

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“…In this study, the simplest algorithm known as k-nearest neighbor's (k-NN) serves as an assessor of the candidate solutions in the population [70]. This algorithm is a supervised machine learning algorithm and also a non-parametric method used for classification as well as regression [52], which is based on finding k-nearest neighbors by means of smallest distance between training examples and the query instance [36].…”

Section: A K-nearest Neighborsmentioning

confidence: 99%

“…In literature, there is an enormous amount of machine learning applications where feature selection has been applied. Some of these applications involve medical diagnosis [4], facial expression recognition [5], diagnose of bronchitis [6], gene selection and cancer classification [7], image steganalysis [8], big data classification [9], obstructive sleep apnea diagnosis [10], sentiment classification [11], Mobile Agent Platform Protection [70], Irony Detection [71], categorize text documents [72], classification of Plant Diseases [73], Breast Masses Detection [74].…”

Section: Introduction and Rationalementioning

confidence: 99%

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Binary Multi-Verse Optimization (BMVO) Approaches for Feature Selection

Hans¹,

Kaur²

2020

IJIMAI

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Multi-Verse Optimization (MVO) is one of the newest meta-heuristic optimization algorithms which imitates the theory of Multi-Verse in Physics and resembles the interaction among the various universes. In problem domains like feature selection, the solutions are often constrained to the binary values viz. 0 and 1. With regard to this, in this paper, binary versions of MVO algorithm have been proposed with two prime aims: firstly, to remove redundant and irrelevant features from the dataset and secondly, to achieve better classification accuracy. The proposed binary versions use the concept of transformation functions for the mapping of a continuous version of the MVO algorithm to its binary versions. For carrying out the experiments, 21 diverse datasets have been used to compare the Binary MVO (BMVO) with some binary versions of existing metaheuristic algorithms. It has been observed that the proposed BMVO approaches have outperformed in terms of a number of features selected and the accuracy of the classification process.

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Section: A K-nearest Neighborsmentioning

confidence: 99%

Section: Introduction and Rationalementioning

confidence: 99%

Binary Multi-Verse Optimization (BMVO) Approaches for Feature Selection

Hans¹,

Kaur²

2020

IJIMAI

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“…This technique employs data mining concepts to protect the visited DM, depending on a classification data mining task. Recently, a supervised machine learning classifier was proposed in [54] by Pallavi et al The authors used a data set that contains 80 mobile agents (half of them are malicious, and the remaining are non-malicious). Then, the features of all agents are extracted to determine the behaviors of the agents.…”

Section: ) Machine Learningmentioning

confidence: 99%

“…Depending on the data mining classification task, another approach is introduced in [55]. The same strategy used in [54] is used in [55], but the difference is that the authors used the K-nearest neighbor algorithm to build the classifier instead of the decision tree-based algorithm.…”

Section: ) Machine Learningmentioning

confidence: 99%

A Survey: Agent-based Software Technology Under the Eyes of Cyber Security, Security Controls, Attacks and Challenges

Alluhaybi¹,

Alrahhal²,

Alzhrani³

et al. 2019

IJACSA

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Recently, agent-based software technology has received wide attention by the research community due to its valuable benefits, such as reducing the load on networks and providing an efficient solution for the transmission challenge problem. However, the major concern in building agent-based systems is related to the security of agents. In this paper, we explore the techniques used to build controls that guarantee both the protection of agents against malicious destination machines and the protection of destination machines against malicious agents. In addition, statistical-based analyses are employed to evaluate the level of maturity of the protection techniques to preserve the protection goals (the code and data, state, and itinerary of the agent), with and without the threat of attacks. Challenges regarding the security of agents are presented and highlighted by seven research questions related to satisfying cyber security requirements, protecting the visiting agent and the visited host machine from each other, providing robustness against advanced attacks that target protection goals, quantifying the security in agent-based systems, and providing features of self-protection and self-communication to the agent itself.

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