A Review on Mobile SMS Spam Filtering Techniques

Abdulhamid, Shafií Muhammad; Latiff, Muhammad Shafie Abd; Chiroma, Haruna; Osho, Oluwafemi; Abdul-Salaam, Gaddafi; Abubakar, Adamu; Herawan, Tutut

doi:10.1109/access.2017.2666785

Cited by 84 publications

(31 citation statements)

References 74 publications

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“…Kalkun and gammu is two different things. Kalkun is only manages the database provided by Gammu [6,14]. Kalkun's architecture can be seen in Figure 4.…”

Section: Kalkun Technologymentioning

confidence: 99%

Gammu and kalkun for information services and sales based on information technology

Santoso

Wibowo

Raharjo

et al. 2020

IJECE

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The rapid development of information technology forces everything to be done sophisticated, easy, and practical. During the information technology revolution 4.0 which is commonly called the disruptive era, the people need communication to exchange information and data. One of the communication systems that has been found today is handphone technology that can be used to communicate remotely through short message services. It can be ascertained that almost every adult having a handphone can be even more than one number, so it is a top priority for market sharing in business activities by utilizing gammu and kalkun technology. Gammu which is an open source program application was developed to build SMS gateways and or while the kalkun program application is used for web based SMS management system. This paper describes the use of gammu and kalkun in information technology to develop and manufacture and manage SMS SERVER effectively and certainly can save costs. This method is more efficient because it allows companies to easily manage their own information dissemination and sales activities and according transactions to business needs, so that the budget spent by the company can be reduced and more economical.

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“…Kalkun and gammu is two different things. Kalkun is only manages the database provided by Gammu [6,14]. Kalkun's architecture can be seen in Figure 4.…”

Section: Kalkun Technologymentioning

confidence: 99%

Gammu and kalkun for information services and sales based on information technology

Santoso

Wibowo

Raharjo

et al. 2020

IJECE

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“…Without dataset, the developed algorithm is impossible to be tested and implemented, and as a result, could not be verified for its significance. It is insisted that accessibility to a requisite dataset constitute one of the challenges researchers often face in successfully researching filtering or classifying SMS spam messages [38]. Authors also explored and refined a list of credible research dataset used by researchers for the study in the field that require SMS or short text messages.…”

Section: B Datasetmentioning

confidence: 99%

An Immunological-Based Simulation: A Case Study of Risk Concentration for Mobile Spam Context Assessment

Zainal

Jali

2018

International Journal on Advanced Science, Engineering and Information Technology

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Over the past two decades, there has been a substantial increase in spam messages that caused critical impact loss. Besides the factor of integration of Internet and mobile technology, this issue is also due to the human's reaction towards spam. This paper presents RiCCA or Risk Concentration for Context Assessment model that performs a risk classification of text spam messages in Short Message Service (SMS) format. The identified risk levels will assist users in anticipating the potential impact of the spam message that they have been receiving. Danger Theory, a prominent theory from Artificial Immune Systems (AIS), inspires the developed model. During the simulation phase, an immunological-based testing lifecycle is applied, with the deployment of the dataset that is shared at UCI Machine Learning Repository and self-collected messages. The performance of the testing revealed a distinctive result, which more than 80% of true positive rate is achieved, employed with two variants algorithm from the Danger Theory; Dendritic Cell Algorithm (DCA) and Deterministic Dendritic Cell Algorithm (dDCA). This simulation demonstrated that the Danger Theory as a feasible model to be applied in measuring the risk of spam. The further articulation on how this immunological-based testing lifecycle is applied in computer simulation and adopting mobile spam as the case study is clarified thoroughly.

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“…It takes inputs from dataset that have been preprocessed and normalized, passed it to the hidden layers for processing and gives output [10,19]. ANNs is used by most researchers to develop systems that will provide solution to problems, it has the structure of human brain and is applied in many research areas like science, medicine computing among others.…”

Section: Artificial Neural Network Conceptsmentioning

confidence: 99%

Distributed Denial of Service Detection using Multi Layered Feed Forward Artificial Neural Network

Idris¹,

Fabian²,

Abdulhamid³

et al. 2017

IJCNIS

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Abstract-One of the dangers faced by various organizations and institutions operating in the cyberspace is Distributed Denial of Service (DDoS) attacks; it is carried out through the internet. It resultant consequences are that it slow down internet services, makes it unavailable, and sometime destroy the systems. Most of the services it affects are online applications and procedures, system and network performance, emails and other system resources. The aim of this work is to detect and classify DDoS attack traffics and normal traffics using multi layered feed forward (FFANN) technique as a tool to develop model. The input parameters used for training the model are: service count, duration, protocol bit, destination byte, and source byte, while the output parameters are DDoS attack traffic or normal traffic. KDD99 dataset was used for the experiment. After the experiment the following results were gotten, 100% precision, 100% specificity rate, 100% classified rate, 99.97% sensitivity. The detection rate is 99.98%, error rate is 0.0179%, and inconclusive rate is 0%. The results above showed that the accuracy rate of the model in detecting DDoS attack is high when compared with that of the related works which recorded detection accuracy as 98%, sensitivity 96%, specificity 100% and precision 100%.

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A Review on Mobile SMS Spam Filtering Techniques

Cited by 84 publications

References 74 publications

Gammu and kalkun for information services and sales based on information technology

Gammu and kalkun for information services and sales based on information technology

An Immunological-Based Simulation: A Case Study of Risk Concentration for Mobile Spam Context Assessment

Distributed Denial of Service Detection using Multi Layered Feed Forward Artificial Neural Network

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