Search Results - (( java implication based algorithm ) OR ( worm detection a algorithm ))

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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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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A traffic signature-based algorithm for detecting scanning internet worms by M. Rasheed, Mohammad, Ghazali, Osman, Md Norwawi, Norita, M. Kadhum, Mohammed

“…In this paper, we propose a method for detecting traffic signature for unknown internet worm. …”
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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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Intelligent signature detection for scanning Internet Worms by Rasheed M.M., Ghazali O., Norwawi N.M.

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

A new generation for intelligent anti-internet worm early system detection by Rasheed, Mohammad M., Md Norwawi, Norita, M. Kadhum, Mohammed, Ghazali, Osman

“…Worm requires host computer with an address on the Internet and any of several vulnerabilities to create a big threat environment.We propose intelligent early system detection mechanism for detecting internet worm.The mechanism is combined of three techniques: Failure Connection Detection (FCD) which concerns with detecting the internet worm and stealthy worm in which computer infected by the worm by using Artificial Immune System; and the Traffic Signature Detection (TSD) which responsible for detecting traffic signature for the worm; and the DNA Filtering Detection (DNAFD) which converts traffic signature to DNA signature and sending it to all computer that connected with the router to create a firewall for new worms.Our proposed algorithm can detect difficult stealthy internet worm in addition to detecting unknown internet worm.…”
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An Innovative Signature Detection System for Polymorphic and Monomorphic Internet Worms Detection and Containment by Rasheed, Mohammad M.

“…Anomaly detection systems can detect unknown worms but usually suffer from a high false alarm rate. …”
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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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Designing a new model for worm response using security metrics by Madihah Mohd Saudi, Taib B.M.

Designing a New Model for Worm Response Using Security Metrics by Madihah Mohd Saudi, Taib, BM

“…Currently, there are many works related with worm detection techniques but not much research is focusing on worm response. …”

Server scanning worm detection by using intelligent failure connection algorithm by Rasheed M.M., Ghazali O., Norwawi N.M.

A systematic analysis on worm detection in cloud-based systems by Kanaker H.M., Madihah Mohd Saudi, Mohd Fadzli Marhusin

Detection algorithm for internet worms scanning that used user datagram protocol by Rasheed M.M., Norwawi N.M., Ghazali O., Faaeq M.K.

An automated signature generation method for zero-day polymorphic worms based on multilayer perceptron model by Mohammed, Mohssen M. Z. E., Chan, H. Anthony, Ventura , Neco, Pathan, Al-Sakib Khan

“…The second step is the signature generation for the collected samples which is done by k-means clustering algorithm and a Multilayer Perceptron Model. The system collects different types of polymorphic worms; we used the k-means clustering algorithm to separate each type into a cluster. …”
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Designing a New Model for Trojan Horse Detection Using Sequential Minimal Optimization by Saudi, MM, Abuzaid, AM, Taib, BM, Abdullah, ZH

“…Nowadays the malwares attack especially by the trojan horse is becoming more sophisticated and intelligent, makes it is harder to be detected than before. Therefore, in this research paper, a new model called Efficient Trojan Detection Model (ETDMo) is built to detect trojan horse attacks more efficiently. …”

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.

Ensemble based categorization and adaptive model for malware detection by Ahmad Zabidi, Muhammad Najmi, Maarof, Mohd Aizaini, Zainal, Anazida

“…Current malware detection method involved string search algorithm which based on the pattern detection. …”
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The implications for ahybrid detection technique against malicious sqlattacks on web applications by Bahjat Arif, Sarajaldeen Akram, Wani, Sharyar

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Mobile botnet classification by using hybrid analysis by Yusof M., Madihah Mohd Saudi, Farida Hazwani Mohd Ridzuan