Anomaly Detection by Using CFS Subset and Neural Network with WEKA Tools

Jabez, J.; Gowri, S.; Vigneshwari, S.; Mayan, J. Albert; Srinivasulu, Senduru

doi:10.1007/978-981-13-1747-7_66

Cited by 19 publications

(6 citation statements)

References 8 publications

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“…Waikato Environment for Knowledge Analysis (Weka) is an open source ML and data mining software which assembled many ML algorithms capable of mining data [54]- [58]. Weka is a flexible tool in which we can integrate our own algorithms and even borrow his algorithms to implement visualization tools.…”

Section: Classification Methodsmentioning

confidence: 99%

SMOPredT4SE: An Effective Prediction of Bacterial Type IV Secreted Effectors Using SVM Training With SMO

et al. 2020

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Various bacterial pathogens can deliver their secreted effectors to host cells via type IV secretion system (T4SS) and cause host diseases. Since T4SS secreted effectors (T4SEs) play important roles in the interaction between pathogens and host, identifying T4SEs is crucial to understanding of the pathogenic mechanism of T4SS. We established an effective predictor called SMOPredT4SE to identify T4SEs from protein sequences. SMOPredT4SE employed combination features of series correlation pseudo amino acid composition and position-specific scoring matrix to present protein sequences, and employed support vector machines (SVM) training with sequential minimal optimization (SMO) arithmetic to train the prediction model (To distinguish it from the traditional SVM, we will abbreviate it as SMO later). In the 5-fold cross-validation test, SMOPredT4SE's overall accuracy was 95.6%. Experiments on comparison with other feature, classifiers, and existing methods are conducted. Experimental results show the effectiveness of SMOPredT4SE in predicting T4SEs.

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Section: Classification Methodsmentioning

confidence: 99%

SMOPredT4SE: An Effective Prediction of Bacterial Type IV Secreted Effectors Using SVM Training With SMO

et al. 2020

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“…Randomly initialize velocity Vid (4) End For (5) (6) While ((t <= M) and (particle fitness value < 100)) do (7) For each particle i do (8) Calculate particle fitness value (9) If its fitness value > Pbestid (10) Set Pbestid = fitness value (11) End If (12) End For (13) Set Gbest = the best ever fitness value (14) For particle i=0 to N do (15) For feature d=0 to D do…”

Section: Hybrid Methodologiesmentioning

confidence: 99%

An Efficiency Optimization for Network Intrusion Detection System

Sakr¹,

Tawfeeq²,

El-Sisi³

2019

IJCNIS

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With the enormous rise in the usage of computer networks, the necessity for safeguarding these networks is also increased. Network intrusion detection systems (NIDS) are designed to monitor and inspect the activities in a network. NIDS mainly depends on the features of the input network data as these features give information on the behaviour nature of the network traffic. The irrelevant and redundant network features negatively affect the efficacy and quality of NIDS, particularly its classification accuracy, detection time and processing complexity. In this paper, several feature selection techniques are applied to optimize the efficiency of NIDS. The categories of the applied feature selection techniques are the filter, wrapper and hybrid. Support vector machine (SVM) is employed as the detection model to classify the network connections behaviour into normal and abnormal traffic. NIDS is trained and tested on the benchmark NSL-KDD dataset. The performance of the applied feature selection techniques is compared with each other and the results are discussed. Evaluation results demonstrated the superiority of the wrapper techniques in providing the highest classification accuracy with the lowest detection time and false alarms of the NIDS.

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“…IDS for a cloud environment was revised to point out the drawbacks and features of each IDS system based on cloud characteristics [17]. In [23], a multilayer perceptron along with feature selection is used to develop an anomaly-based intrusion detection system. The developed IDS is based on an enhanced correlation-based feature selection (CFS), which identifies anomalies at early stages, and improves detection accuracy.…”

Section: Intrusion Detection (Id)mentioning

confidence: 99%

PDIS: A Service Layer for Privacy and Detecting Intrusions in Cloud Computing

Mumtaz¹,

Samawi²,

Alhroob³

et al. 2022

ijasca

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Cloud computing faces numerous challenges in many areas including security and privacy issues. In this work, a developed approach is suggested to tackle three security and privacy issues: network intrusion detection (NID), privacy, and internal attacks. A decision tree (J48) has been used to generate a set of rules based on the CICIDS2017 dataset to solve the NID problem. The accuracy of the generated rules approaches 99.8%. A set of policies are attached to the data file on the bases of a sticky policy to preserve privacy. A new approach is suggested based on blockchain to detect internal attacks in real-time, in which a set of trustees-chain are identified by the data owner. Any data modification conducted by a trusted member will be reported to all members of the trust group including the owner. The developed approach suggests adding a Privacy and Detecting Intrusions Service (PDIS) layer as part of the cloud computing main service model. PDIS includes the three suggested approaches above (NID, sticky policy, and trustees-chain). Finally, a web-based application is implemented to act as casework to validate PDIS and evaluate its reliability. Keywords: Cloud computing, Privacy, Machine learning, Internal Intrusion Detection, Network Intrusion Detection, Real Time Auditing

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Anomaly Detection by Using CFS Subset and Neural Network with WEKA Tools

Cited by 19 publications

References 8 publications

SMOPredT4SE: An Effective Prediction of Bacterial Type IV Secreted Effectors Using SVM Training With SMO

SMOPredT4SE: An Effective Prediction of Bacterial Type IV Secreted Effectors Using SVM Training With SMO

An Efficiency Optimization for Network Intrusion Detection System

PDIS: A Service Layer for Privacy and Detecting Intrusions in Cloud Computing

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