An Adaptive and Collaborative Server-Side SMS Spam Filtering Scheme Using Artificial Immune System

Onashoga, Adebukola; Abayomi‐Alli, Olusola; Sodiya, Adesina S.; Ojo, D. A.

doi:10.1080/19393555.2015.1078017

Cited by 7 publications

(8 citation statements)

References 17 publications

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“…I.J. Information Technology and Computer Science, 2017, 7, 42-50 [1] 2012 IEEE RQ1, RQ2, RQ3 S2 [2] 2014 IJRAT RQ1 S3 [3] 2013 ACM RQ1,RQ2, RQ3 S4 [5] 2010 Computers and Security RQ1,RQ2, RQ3 S5 [10] 2012 IJCSI RQ1 RQ2, RQ3 S6 [11] 2014 IJMLC RQ1 RQ2, RQ3 S7 [12] 2006 ACM RQ1 RQ2, RQ3 S8 [7] 2012 IEEE RQ1, RQ3 S9 [13] 2015 CAE RQ1 RQ2, RQ3 S10 [14] 2011 ACM RQ1, RQ3 S11 [15] 2011 ACM RQ1, RQ3 S12 [16] 2007 ACM RQ1, RQ3 S13 [17] 2008 ITJ RQ1, RQ3 S14 [18] 2014 ASTL RQ1 S15 [4] 2015 Information Security Journal RQ1 RQ2, RQ3 S16 [21] 2014 JBASR RQ1 RQ2, RQ3 S17 [22] 2013 RQ1, RQ3 [2] Mutual Relation Based on the previous relation of sender and receiver.…”

Section: Results Analysismentioning

confidence: 99%

“…There is scope of research in this field and some research works have been conducted on it. There are different categories of SMS spam filtering such as white listing and black listing, content-based, non-content based, collaborative approaches and challenge-response technique [4], [5], [12], [29]. The techniques are used in client side, server side or in both client and server side [4].…”

Section: Background and Related Workmentioning

confidence: 99%

“…There are different categories of SMS spam filtering such as white listing and black listing, content-based, non-content based, collaborative approaches and challenge-response technique [4], [5], [12], [29]. The techniques are used in client side, server side or in both client and server side [4]. Several Machine Learning Algorithms such as Naï ve Bayes, Support Vector Machine (SVM), Logistic Regression, Decision Trees, K-Nearest Neighbor are used to classify between Spam and legitimate SMSes named as Ham.…”

Section: Background and Related Workmentioning

confidence: 99%

“…Classification algorithm is necessary for gaining better accuracy. [11] Incorporating Apriori Algorithm S7 [12] Used a weighting Mechanism to reduce false negatives S8 [7] Suffers from implementation complexity S9 [13] Combination of two formulas gives better result in terms of false positives S10 [14] Concludes SVM outperforms other algorithms and created a baseline for further comparison S11 [15] Although SVM gives better results in Spam identification Bayesian is more feasible for mobile applications Extensive feature engineering is needed for better accuracy S12 [16] Demonstrates the need of spam filtering in spite of having established email spam filtering S14 [18] Lightweight and focuses on runtime S15 [4] Server side, complex and suffers from updating issues S16 [21] Implementation complexity…”

Section: S10 [14] Tokenization With Various Classifiermentioning

confidence: 99%

See 3 more Smart Citations

A Systematic Literature Review on SMS Spam Detection Techniques

Lota¹,

Hossain²

2017

IJITCS

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Spam SMSes are unsolicited messages to users, which are disturbing and sometimes harmful. There are a lot of survey papers available on email spam detection techniques. But, SMS spam detection is comparatively a new area and systematic literature review on this area is insufficient. In this paper, we perform a systematic literature review on SMS spam detection techniques. For that purpose, we consider the available published research works from 2006 to 2016. We choose 17 papers for our study and reviewed their used techniques, approaches and algorithms, their advantages and disadvantages, evaluation measures, discussion on datasets and finally result comparison of the studies. Although, the SMS spam detection techniques are more challenging than email spam detection techniques because of the regional contents, use of abbreviated words, unfortunately none of the existing research addresses these challenges. There is a huge scope of future research in this area and this survey can act as a reference point for the future direction of research.

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Section: Results Analysismentioning

confidence: 99%

Section: Background and Related Workmentioning

confidence: 99%

Section: Background and Related Workmentioning

confidence: 99%

Section: S10 [14] Tokenization With Various Classifiermentioning

confidence: 99%

See 2 more Smart Citations

A Systematic Literature Review on SMS Spam Detection Techniques

Lota¹,

Hossain²

2017

IJITCS

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“…The However, The IDP serves two purposes in this framework, first to reduce redundancy in the local and global knowledge, and also to increase the search and decision-making time when a new threat arrives at any node. As adopted by Onashoga et al [14], the Detector for Ageing Technology for Adaptive and Collaborative SMS Spam Filtering was employed to remove aged intrusion signature from the knowledge base and also to reduce computation time when accessing knowledge at the global scope. Algorithm 4 shows the pseudo-code description of the intrusion detection pruning process.…”

Section: Ck-raid External Modular Componentsmentioning

confidence: 99%

CK-RAID: Collaborative Knowledge Repository for Intrusion Detection System

Onashoga

Akinwale

Amusa

et al. 2019

CIT. J. comput. inf. technol

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Intrusion Detection Systems (IDSs) are an integral part of an organization's infrastructure. Without an IDS facility in place to monitor network and host activities, attempted and successful intrusion attempts may go unnoticed. This study proposed a Collaborative Knowledge Repository Architecture for Intrusion Detection (CK-RAID). It is based on a distributed network of computer nodes, each with their individual IDS with a centralized knowledge repository system, and firewall acting as a defence. When an unfamiliar attack hits any node, the first step the intrusion monitor takes is to request from Knowledge Repository Server the most effective intrusion response. To improve performance, Intrusion Update module collaborates with IDSs sensor and log by updating their expert rule and intrusion information respectively and removing the old intrusion signature from the knowledge base with the aid of Intrusion Detector Pruning. To ensure security of information exchange, RSA encryption and Digital Signature were used to encode information during transit. The result showed that CK-RAID had a detection rate of 97.2%, compared with Medoid Clustering, Y-means, FCM and K-means that have an accuracy of 96.38%, 87.15%, 82.13% and 77.25% respectively. Therefore, CK-RAID can be deployed for efficient detection of all categories of intrusion detection and response.

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A deep learning method for automatic SMS spam classification: Performance of learning algorithms on indigenous dataset

Abayomi‐Alli

Misra

Abayomi‐Alli

2022

Concurrency and Computation

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SMS, one of the most popular and fast‐growing GSM value‐added services worldwide, has attracted unwanted SMS, also known as SMS spam. The effects of SMS spam are significant as it affects both the users and the service providers, causing a massive gap in trust among both parties. This article presents a deep learning model based on BiLSTM. Further, it compares our results with some of the states of the art machine learning (ML) algorithm on two datasets: our newly collected dataset and the popular UCI SMS dataset. This study aims to evaluate the performance of diverse learning models and compare the result of the new dataset expanded (ExAIS_SMS) using the following metrics the true positive (TP), false positive (FP), F‐measure, recall, precision, and overall accuracy. The average accuracy for the BiLSTSM model achieved moderately improved results compared to some of the ML classifiers. The experimental results achieved significant improvement from the ground truth results after effective fine‐tuning of some of the parameters. The BiLSTM model using the ExAIS_SMS dataset attained an accuracy of 93.4% and 98.6% for UCI datasets. Further comparison of the two datasets on the state‐of‐the‐art ML classifiers gave an accuracy of Naive Bayes, BayesNet, SOM, decision tree, C4.5, J48 is 89.64%, 91.11%, 88.24%, 75.76%, 80.24%, and 79.2% respectively for ExAIS_SMS datasets. In conclusion, our proposed BiLSTM model showed significant improvement over traditional ML classifiers. To further validate the robustness of our model, we applied the UCI datasets, and our results showed optimal performance while classifying SMS spam messages based on some metrics: accuracy, precision, recall, and F‐measure.

show abstract

An Adaptive and Collaborative Server-Side SMS Spam Filtering Scheme Using Artificial Immune System

Cited by 7 publications

References 17 publications

A Systematic Literature Review on SMS Spam Detection Techniques

A Systematic Literature Review on SMS Spam Detection Techniques

CK-RAID: Collaborative Knowledge Repository for Intrusion Detection System

A deep learning method for automatic SMS spam classification: Performance of learning algorithms on indigenous dataset

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