Optimization Of An Anomaly Based Intrusion Detection System On Smartphone

Ugwuanyi Peace Nkiruka, Okafor Loveth Ijeoma, Nelson Ogechukwu Madu, Agbo Jonathan Chukwunwike, Barnnabas Bundepuun Orndiir, Doowuese Kate Faafaa, Ugwu Nnaemeka Virginus, Ani Chinonso Darlington, Anigbogu Kenechukwu Sylvanus


Android OS is one of the widely used mobile operating systems. There is a huge increment in malware applications in android phones.  This is an effort gear towards detecting malicious activities. This paper proposes a technique that can detect any illegal activities in smart phone using anomaly based. It analyzes system calls’ logs and also the conduct of an app and afterward produces signatures for malware conduct. Intrusion detection system (IDS) is meant to be a software application which monitors system activities and detect any intrusion actions or operations. We proposed a system that will detect any illegal/malicious intrusions in Smart phone using anomaly based approach. Tshis approach is based with respect to viewing the conduct of the gadget by monitoring various parameters and the status of the segments of the gadget. This paper adopts the object oriented analysis and design method (OOADM). This models real world processes, operations and the data is also represented in a more flexibly, efficiently and realistically behaviour. Object-Oriented examination gives a simple progress to mainstream Object-Oriented programming dialects, for example, Java and C++. The proposed system will help to  monitor users Android phone by detecting, authenticate intrusion and also log and mail alert of an attempt to the user’s phone through the phone number and email.


Anomaly based, Intrusion detection system, Smart phones, and malicious activities.

Full Text:



Adebayo Olawale, Surajudeen, M.A.Mabayoje, Amit Mishra, Osho Oluwafemi, “Malware Detection, Supportive Software Agents and Its Classification Schemes”. International Journal of Network Security & Its Applications (IJNSA), Vol.4 no.6, pg 33, 2012

Franklin Tchakounte, “A Malware Detection System for Android,” Universitat Bremen, 2015.

Belal Amro, College of Information Technology, Hebron University, " malware detection techniques for mobile devices". International Journal of Mobile Network Communications & Telematics ( IJMNCT) Vol.7, No.4/5/6, December 2017

I. Burguera and U. Zurutuza, “Crowdroid : Behavior-Based Malware Detection System for Android,” Proc. 1st ACM Work. Secur. Priv. Smartphones Mob. devices (SPSM ’11). ACM, New York, NY, pp. 15–26, 2011.

Mohammed A, Ambusaidi, Xiangjian He, ,Priyadarsi Nanda, Zhiyuan Tan,” Building an intrusion detection system using a filter-based feature selection algorithm”. IEEE transactions on computers, vol 1, no 1 pg 1-3, 2014.

K.Mani, P.Kalpama, “A review on filter based feature selection”. International Journey of Innovation Research in Computer and Communication Engineering, Vol 4, issue 5, pg 9147, 2016.

A.A. Waskita, H. Suhartantoy, P.D. Persadhazy, L.T. Handoko, “A simple statistical analysis approach for Intrusion Detection System”. Center for Development of Nuclear Informatics-National Nuclear Energy Agency, pg 1, 2014.

Yousef Farhaoui, Ahmed Asimi, “Creating a Complete Model of an Intrusion Detection System effective on the LAN”. International Journal of Advanced Computer Science and Applications (IJACSA), Vol. 3, No. 5, pg 1-2, 2012.

Heba Fathy, Ahmed Mohamed Eid, Prof. Afaf Abo, El Ftouh Saleh, Prof. Aboul-Ella Hassanien, “Computational Intelligence in Intrusion Detection System”. Faculty of Science, Al-Azhar University for Obtaining the Degree of Doctor of Philosophy in Computer. pg 147-150, 2013.

Akhilesh Kumar Shrivas, Prabhat Kumar Mishra, “Intrusion Detection System for Classification of Attacks with Cross Validation”. International Journal of Engineering Science Invention, Vol 5, Issue 9, pg 5, 2016

Mr. Akash J Wadate, Prof. N. R Chopde, Prof. D. R. Datar, “Malware Detection System for Android Mobile Applications“. International Journal of Engineering Research and General Science, Vol 4, Issue 1, pg 21-22, 2016.

Abdulla Amin Aburomman, Mamun Bin Ibne Reaz, “Evolution of Intrusion Detection Systems Based on Machine Learning Methods”. Australian Journal of Basic and Applied Sciences, Vol 7, no 7, pg 46, 2013.

Mehrnaz Mazinia, BabakShirazib, IrajMahdavib. “Anomaly network-based sintrusion detection system using a reliable hybrid artificial bee colony and Ada Boost algorithms”. Journal of King Saud University, pg 799-806, 2018.

R. Sato, D. Chiba, and S. Goto, “Detecting Android Malware by Analyzing Manifest Files,” Proc. Asia-Pacific Adv. Netw., vol. 36, pp. 23–31, 2013.

G. Suarez-tangil, S. K. Dash, M. Ahmadi, J. Kinder, G. Giacinto, and L. Cavallaro, “DroidSieve : Fast and Accurate Classification of Obfuscated Android Malware,” ACM Conf. Comput. Commun. Secur., 2017.

K. Abdullah, D. Ibrahim, and C. Aydin, “APK Auditor : Permission-based Android malware detection system,” vol. 13, pp. 13–15, 2015.

X. Su, M. Chuah, and G. Tan, “Smartphone Dual Defense Protection Framework : Detecting Malicious Applications in Android Markets,” Mob. Ad-hoc Sens. Networks (MSN), 2012 Eighth Int. Conf., pp. 153–160, 2012.

DOI: http://dx.doi.org/10.52155/ijpsat.v25.2.2865


  • There are currently no refbacks.

Copyright (c) 2021 Ugwuanyi Peace Nkiruka, Okafor Loveth Ijeoma, Nelson Ogechukwu Madu, Agbo Jonathan Chukwunwike, Barnnabas Bundepuun Orndiir, Doowuese Kate Faafaa, Ugwu Nnaemeka Virginus, Ani Chinonso Darlington

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.