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An enhancement of classification technique based on rough set theory for intrusion detection system application by Noor Suhana, Sulaiman

“…Experimental results show the proposed technique increases accuracy classification percentage up to 99.95%; and the minimum number of bins determine good discretization algorithm. Consequently, attack detection rate increases and false positive alarm rate minimizes. …”
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Improvement anomaly intrusion detection using Fuzzy-ART based on K-means based on SNC Labeling by Zulaiha Ali Othman, Afaf Muftah Adabashi, Suhaila Zainudin, Saadat M. Al Hashmi

“…The result has shown that this algorithm has increased the detection rate and reduced the false alarm rate compared with Fuzzy-ART.…”
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Effective mining on large databases for intrusion detection by Adinehnia, Reza, Udzir, Nur Izura, Affendey, Lilly Suriani, Ishak, Iskandar, Mohd Hanapi, Zurina

“…Results show that higher detection rate is achieved when using apriori algorithm on the proposed dataset. …”
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Designing a new model to detect Trojan Horse based on knowledge discovery and data mining by Areej Mustafa Khlaif Abuzaid

Experimenting the dendrite cell algorithm for disease outbreak detection model by Mohamad Mohsin, Mohamad Farhan, Hamdan, Abdul Razak, Abu Bakar, Azuraliza

“…The characteristics of early outbreak signal which are weak and behaved under uncertainties has brought to the development of outbreak detection model based on dendrite cell algorithm.Although the algorithm is proven can improve detection performance, it relies on several parameters which need to be defined before mining.In this study, the most appropriate parameter setting for outbreak detection using dendrite cell algorithm is examined.The experiment includes four parameters; the number of cell cycle update, the number of dendrite cell allowed to be in population, weight, and migration threshold value. …”
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Improving intrusion detection using genetic algorithm by Hashemi, V. Moraveji, Muda, Zaiton, Yassin, Warusia

“…The validity of this approach is verified using Knowledge Discovery and Data Mining Cup 1999 (KDD Cup ’99) dataset. The experimental results demonstrate that the proposed approach outperforms the existing techniques, with the detection rate of attack and false alarm rates of 95.7265 and 4.2735, respectively. …”
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Image clustering comparison of two color segmentation techniques by Subramaniam, Kavitha Pichaiyan

“…Finally, the algorithm found, which would solve the image segmentation problem.…”
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Enhanced AI-based anomaly detection method in the intrusion detection system (IDS) / Kayvan Atefi by Atefi, Kayvan

“…An efficient IDS uses computational methods as techniques of machine learning (ML) to enhance the rates of detection to obtain the lowest false positive rate, although such rates tend to be reduced by the big amount of irrelevant features as an optimization issue. …”
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Features selection for intrusion detection system using hybridize PSO-SVM by Tabaan, Alaa Abdulrahman

“…The results reveal that the proposed hybrid algorithm is capable of achieving classification accuracy values of (95.82 % and 97.68 %), detection rates values of (95.8 % and 99.3 %) and false alarm rates values of (0.083 % and 0.045 %) on both KDD CUP 99 and NSL KDD. …”
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A simultaneous spam and phishing attack detection framework for short message service based on text mining approach by Mohd Foozy, Cik Feresa

“…Additionally, the proposed framework also can detect the attack simultaneously using text mining approaches.…”
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Improved Malware detection model with Apriori Association rule and particle swarm optimization by Adebayo, Olawale Surajudeen, Abdul Aziz, Normaziah

“…In order to improve the detection rate of malicious application on the Android platform, a novel knowledge-based database discovery model that improves apriori association rule mining of a priori algorithm with Particle Swarm Optimization (PSO) is proposed. …”
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Unsupervised Anomaly Detection with Unlabeled Data Using Clustering by Chimphlee, Witcha, Abdullah, Abdul Hanan, Md. Sap, Mohd. Noor

“…We present a clustering-based intrusion detection algorithm, unsupervised anomaly detection, which trains on unlabeled data in order to detect new intrusions. …”
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Automatic Number Plate Recognition on android platform: With some Java code excerpts by ., Abdul Mutholib, Gunawan, Teddy Surya, Kartiwi, Mira

“…On the other hand, the traditional algorithm using template matching only obtained 83.65% recognition rate with 0.97 second processing time. …”
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An Improved Artificial Dendrite Cell Algorithm for Abnormal Signal Detection by Mohamad Mohsin, Mohamad Farhan, Abu Bakar, Azuraliza, Hamdan, Abdul Razak, Abdul Wahab, Mohd Helmy

“…From the experiments towards 12 benchmark and two outbreak datasets, the improved DCA is proven to have a better detection result than its previous version in terms of sensitivity, specificity, false detection rate and accuracy.…”
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Development of seven segment display recognition using TensorFlow on Raspberry Pi by Gunawan, Teddy Surya, Mohammad Reduwan, Haque, Yaacob, Mashkuri, Mohamed Azmin, Nor Fadhillah, Ismail, Nanang, Ismail, Md Yusof, Kartiwi, Mira, A. Rahman, Azhar

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An improved hybrid learning approach for better anomaly detection by Mohamed Yassin, Warusia

“…Therefore, anomaly detection is often associated with high false alarm with only moderate accuracy of detection rates. …”
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Development and implementation of a technique for norms-adaptable agents in open multi-agent communities by Mahmoud M., Ahmad M.S., Mohd Yusoff M.Z.

An adaptive anomaly threshold in artificial dendrite cell algorithm by Mohamad Mohsin, Mohamad Farhan, Abu Bakar, Azuraliza, Hamdan, Abdul Razak

“…The dendrite cell algorithm (DCA) relies on the multi-context antigen value (MCAV) to determine the abnormality of a record by comparing it with anomaly threshold.In practice, the threshold is pre-determined before mining based on previous information and the existing MCAV is inefficient when expose to extreme values.This causes the DCA fails to detect unlabeled data if the new pattern distinct from previous information and reduces the detection accuracy.This paper proposed an adaptive anomaly threshold for DCA using the statistical cumulative sum (CUSUM) with the aim to improve its detection capability.In the proposed approach, the MCAV were normalized with upper CUSUM and the new anomaly threshold was calculated during run time by considering the acceptance value and min MCAV.From the experiments towards 12 datasets, the new version of DCA generated a better detection result than its previous version in term of sensitivity, specificity, false detection rate, and accuracy.…”
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An improved artificial dendrite cell algorithm for abnormal signal detection by Mohamad Mohsin, Mohamad Farhan, Abu Bakar, Azuraliza, Hamdan, Abdul Razak, Abdul Wahab, Mohd Helmy

“…From the experiments towards 12 benchmark and two outbreak datasets, the improved DCA is proven to have a better detection result than its previous version in terms of sensitivity, specificity, false detection rate and accuracy.…”
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Feature selection in intrusion detection, state of the art: A review by Rais, H.M., Mehmood, T.

“…With irrelevant and redundant features learning algorithm builds detection model with less accuracy rate. …”
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