Search Results - (( (worm OR work) detection _ algorithm ) OR ( botnet detection based algorithm ))

A comprehensive review of mobile botnet detection using genetic algorithm: A systematic review by Abd Rahman M.Z.B., Saudi M.B.M., Basir N.B.

Systematic analysis on mobile botnet detection techniques using genetic algorithm by Rahman M.Z.A., Madihah Mohd Saudi

Systematic Analysis on Mobile Botnet Detection Techniques Using Genetic Algorithm by Rahman, MZA, Madihah Mohd Saudi

“…Furthermore, this paper also discusses the challenges and the potential research for future work with relate of the genetic algorithm. This research paper can be used as a reference and guidance for further study on mobile botnet detection techniques.…”

A new mobile botnet classification based on permission and API calls by Yusof M., Madihah Mohd Saudi, Farida Hazwani Mohd Ridzuan

Layered Botnet Detection Framework Based On Signal Processing And Discrete Time Analysis by Melhim, Loai Kayed Hassan Bani

“…LBDF is equipped with a ‘malicious-scanning’ detection algorithm. The LBDF algorithm uses SYN, ACK (SNAK) rules to reduce the volume of network captured traffic and to convert the reduced traffic into discrete time sequences. …”
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A New Mobile Botnet Classification based on Permission and API Calls by Yusof, M, Saudi, MM, Ridzuan, F

“…This new classification can be used as the input for mobile botnet detection for future work, especially for financial matters.…”

Designing a New Model for Trojan Horse Detection Using Sequential Minimal Optimization by Saudi, MM, Abuzaid, AM, Taib, BM, Abdullah, ZH

“…Based on the experiment conducted, the Sequential Minimal Optimization (SMO) algorithm has outperformed other machine learning algorithms with 98.2 % of true positive rate and with 1.7 % of false positive rate.…”

Designing a new model for Trojan horse detection using sequential minimal optimization by Madihah Mohd Saudi, Abuzaid A.M., Taib B.M., Abdullah Z.H.

Back Propagation Algorithm-Based Intelligent Model for Botnet Detection by Nik Quosthoni, Sunaidi, Ahmed, Abdulghani Ali

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ABC: android botnet classification using feature selection and classification algorithms by Abdullah, Zubaile, Mohd Saudi, Madihah, Anuar, Nor Badrul

“…Due to the fast modifications in the technologies used by malicious application (app) developers, there is an urgent need for more advanced techniques for Android botnet detection. In this paper, a new approach for Android botnet classification based on features selection and classification algorithms is proposed. …”
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Machine Learning For HTTP Botnet Detection Using Classifier Algorithms by Mohd Dollah, Rudy Fadhlee, Abd Majid, Mohd Faizal, Arif, Fahmi, Mas'ud, Mohd Zaki, Lee, Kher Xin

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Intelligent DNA signature detection for internet worms by Ghazali, Osman

“…Active worms spread in an automated fashion flooding the Internet in a very short time.Slammer worm infected more than 90% of vulnerable machines within 10 minutes on January 25th, 2003.Hence it is necessary to monitor and detect the worms as soon as they are introduced to minimize the damage caused by them.This project concentrates on developing an anti-scanning worm detection system that can automatically detect and control the spread of internet scanning worms without any manual intervention.The Intelligent Failure Connection Algorithm (IFCA) developed in this project can detect both stealth and normal worms within a short time.Experiments conducted as part of the evaluation shows that IFCA detects a worm within two scanning cycles of the worm.This is faster than any of the currently available algorithms or mechanisms reported in the literature.The IFCA uses Artificial Immune System (AIS) for the purpose of monitoring and detecting the worms.The Traffic Signature Algorithm (TSA) developed in the project captures the traffic signature of the worm from the infector when it sends the traffic to the victim.The Intelligent DNA Signature Detection Algorithm (IDNASDA) algorithm works by breaking an infection session into different infection phases, each phase containing a number of different traffic such as Internet Control Message Protocol (ICMP), Transmission Control Protocol (TCP), or User Datagram Protocol (UDP).Finally it converts the traffic signature to DNA signature.The tests carried out show that the IDNASD could detect DNA signature for MSBlaster worm.…”
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An enhanced android botnet detection approach using feature refinement by Anwar, Shahid

“…In order to detect botnet attacks which causes immense chaos and problems to smartphones, first the Android botnet need to be analysed. …”
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ABC: Android botnet classification using feature selection and classification algorithms by Abdullah Z., Anuar N.B., Madihah Mohd Saudi

Cloud Worm Detection and Response Technique By Integrating The Enhanced Genetic Algorithm An Threat Level by Hasan Mahmoud Sha'ban Kanaker

Improving The Algorithm To Detect Internet Worms by Rasheed, Mohmmad M

“…The aim of this project is to improved algorithm to detect internet worm by two sub algorithm. …”
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A behavior based algorithm to detect spam bots by Zamil, Mohammed Fadhil

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Intelligent failure connection algorithm for detecting internet worms by M. Rasheed, Mohammad, Md Norwawi, Norita, Ghazali, Osman, M. Kadhum, Mohammed

“…In this paper, we show that our algorithm can detect new types of worms. This paper shows that intelligent Failure Connection Algorithm (IFCA) operation is faster than traditional algorithm in detecting worms.…”
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IoT-based botnet attacks systematic mapping study of literature by Habiba Hamid, Rafidah Md Noor, Syaril Nizam Omar, Ismail Ahmedy, Shaik Shabana Anjum, Syed Adeel Ali Shah, Sheena Kaur, Fazidah Othman, Emran Mohd Tamil

Server scanning worm detection by using intelligent failure connection algorithm by M. Rasheed, Mohammad, Ghazali, Osman, Md. Norwawi, Norita

“…Our proposal decreases the false alarm in Intelligent Failure Connection Algorithm (IFCA). Our proposal also works when the computer is infected by the worm and IFCDA detected the worm, many computers that are connected through the internet will receive the warning by using our proposal. …”
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