The Strength of Weak Learnability
Machine Learning
Original Contribution: Stacked generalization
Neural Networks
Experiments on multistrategy learning by meta-learning
CIKM '93 Proceedings of the second international conference on Information and knowledge management
Hierarchical mixtures of experts and the EM algorithm
Neural Computation
Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Practical network support for IP traceback
Proceedings of the conference on Applications, Technologies, Architectures, and Protocols for Computer Communication
Machine Learning
IEEE/ACM Transactions on Networking (TON)
ISDA '05 Proceedings of the 5th International Conference on Intelligent Systems Design and Applications
Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)
Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)
A practical and robust inter-domain marking scheme for IP traceback
Computer Networks: The International Journal of Computer and Telecommunications Networking
On deterministic packet marking
Computer Networks: The International Journal of Computer and Telecommunications Networking
A router-based technique to mitigate reduction of quality (RoQ) attacks
Computer Networks: The International Journal of Computer and Telecommunications Networking
Detecting DRDoS attacks by a simple response packet confirmation mechanism
Computer Communications
From Ensemble of Fuzzy Classifiers to Single Fuzzy Rule Base Classifier
ICAISC '08 Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing
Flexible Deterministic Packet Marking: An IP Traceback System to Find the Real Source of Attacks
IEEE Transactions on Parallel and Distributed Systems
Reduction of Quality (RoQ) attacks on structured peer-to-peer networks
IPDPS '09 Proceedings of the 2009 IEEE International Symposium on Parallel&Distributed Processing
Computer Networks: The International Journal of Computer and Telecommunications Networking
Is it congestion or a DDoS attack?
IEEE Communications Letters
Discriminating DDoS Flows from Flash Crowds Using Information Distance
NSS '09 Proceedings of the 2009 Third International Conference on Network and System Security
Mental Tasks Classification for a Noninvasive BCI Application
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
Outside the Closed World: On Using Machine Learning for Network Intrusion Detection
SP '10 Proceedings of the 2010 IEEE Symposium on Security and Privacy
Evolutionary learning for neuro-fuzzy ensembles with generalized parametric triangular norms
ICAISC'10 Proceedings of the 10th international conference on Artificial intelligence and soft computing: Part I
Distributed denial of service attack detection using an ensemble of neural classifier
Computer Communications
CISC'05 Proceedings of the First SKLOIS conference on Information Security and Cryptology
Empirical study on fusion methods using ensemble of RBFNN for network intrusion detection
ICMLC'05 Proceedings of the 4th international conference on Advances in Machine Learning and Cybernetics
ISPEC'05 Proceedings of the First international conference on Information Security Practice and Experience
Boosting ensemble of relational neuro-fuzzy systems
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
Data Fusion and Cost Minimization for Intrusion Detection
IEEE Transactions on Information Forensics and Security
IEEE Communications Magazine
Tracing cyber attacks from the practical perspective
IEEE Communications Magazine
Bayesian Neural Networks for Internet Traffic Classification
IEEE Transactions on Neural Networks
A novel intrusion detection system based on feature generation with visualization strategy
Expert Systems with Applications: An International Journal
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A DDoS attack is the most prevalent threat, viz., flooding the computing and communication resources in order to make the service unavailable for legitimate users, since a decade and continues to be threatening till date. Therefore, these critical resources must be protected against the DDoS attacks. The detection of DDoS attacks requires adaptive and incremental learning classifier, less computational complexity, and accurate decision making from uncertain information. Hence, the DDoS attacks could be detected using existing soft computing techniques such as fuzzy logic, neural networks, and genetic algorithms. Fuzzy logic has the advantage of interpreting the rules well but it suffers from the disadvantage of not able to acquire the rules automatically. The neural networks generalize the network well but they cannot interpret the rules. Genetic algorithm provides optimal solutions but the time complexity is high. Hybrid methods, Neuro-fuzzy and genetic fuzzy have been proposed to overcome the drawbacks of interpretability and manual rules acquisition. In this paper, adaptive and hybrid neuro-fuzzy systems were proposed as subsystems of the ensemble. Sugeno type Adaptive Neuro-Fuzzy Inference System (ANFIS) has been chosen as a base classifier for our research as Mamdani type ANFIS is not suitable for real time due to its high computational complexity and non-adaptiveness to extract exact knowledge from the dataset. Single classifier makes error on different training samples. So, by creating an ensemble of classifiers and combining their outputs, the total error can be reduced and the detection accuracy can be increased. Improvement in the performance of ANFIS ensemble is the focus of this paper. Our proposed DDoS classification algorithm, NFBoost, differs from the existing methods in weight update distribution strategy, error cost minimization, and ensemble output combination method, but resembles similar in classifier weight assignment and error computation. Our proposed NFBoost algorithm is achieved by combining ensemble of classifier outputs and Neyman Pearson cost minimization strategy, for final classification decision. Publicly available datasets such as KDD Cup, CAIDA DDOS Attack 2007, CONFICKER worm, UNINA traffic traces, and UCI Datasets were used for the simulation experiments. NFBoost was trained and tested with the publicly available datasets and our own SSE Lab SSENET 2011 datasets. Detection accuracy and Cost per sample were the two metrics used to analyze the performance of the NFBoost classification algorithm and were compared with bagging, boosting, and AdaBoost algorithms. From the simulation results, it is evident that NFBoost algorithm achieves high detection accuracy (99.2%) with fewer false alarms. Cost per instance is also very less for the NFBoost algorithm compared to the existing algorithms. NFBoost algorithm outperforms the existing ensemble algorithms with a maximum gain of 8.4% and a minimum gain of 1.1%.