scholarly journals Improving Information Integrity using Artificial Bee Colony based Intrusion Detection System

2015 ◽  
Vol 130 (6) ◽  
pp. 12-23
Author(s):  
Qamar Rayees ◽  
Mohammad Asger ◽  
Muheet Ahmed
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Artificial Bee Colony ◽  
Detection System ◽  
Bee Colony ◽  
Information Integrity

scholarly journals Design of intrusion detection system based on improved ABC_elite and BP neural networks

2019 ◽  
Vol 16 (3) ◽  
pp. 773-795
Author(s):  
Letian Duan ◽  
Dezhi Han ◽  
Qiuting Tian
Keyword(s):  
Neural Networks ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Detection System ◽  
Local Optimum ◽  
Bp Neural Networks ◽  
Guided Search ◽  
Bee Colony

Intrusion detection is a hot topic in network security. This paper proposes an intrusion detection method based on improved artificial bee colony algorithm with elite-guided search equations (IABC elite) and Backprogation (BP) neural net works. The IABC elite algorithm is based on the depth first search framework and the elite-guided search equations, which enhance the exploitation ability of artificial bee colony algorithm and accelerate the convergence. The IABC elite algorithm is used to optimize the initial weight and threshold value of the BP neural networks, avoiding the BP neural networks falling into a local optimum during the training process and improving the training speed. In this paper, the BP neural networks optimized by IABC elite algorithm is applied to intrusion detection. The simulation on the NSL-KDD dataset shows that the intrusion detection system based on the IABC elite algorithm and the BP neural networks has good classification and high intrusion detection ability.

scholarly journals Hybrid Classification Technique for Accurate Detection of DDoS Attacks

2020 ◽  
Vol 9 (4) ◽  
pp. 1338-1342
Keyword(s):  
Intrusion Detection ◽  
Classification Accuracy ◽  
Intrusion Detection System ◽  
Detection System ◽  
Original Data ◽  
High Dimensional ◽  
Data Set ◽  
Real Time Traffic ◽  
Bee Colony ◽  
Optimization Schemes

Intellectual intrusion detection system can merely be build if there is accessibility to an effectual data set. A high dimensional quality dataset that imitates the real time traffic facilitates training and testing an intrusion detection system. Since it is complex to scrutinize and extort knowledge from high-dimensional data, it is identified that feature selection is a preprocessing phase during attack defense. It increases the classification accuracy and reduces computational complexity by extracting important features from original data. Optimization schemes can be utilized on the dataset for selecting the features to find the appropriate subspace of features while preserving ample accuracy rate characterized by the inventive feature set. This paper aims at implementing the hybrid algorithm, ABC-LVQ. The bio-inspired algorithm, Artificial Bee Colony (ABC) is adapted to lessen the amount of features to build a dataset on which a supervised classification algorithm, Linear Vector Quantization (LVQ) is applied, thus achieving highest classification accuracy compared to k-NN and LVQ. The NSL-KDD dataset is scrutinized to learn the efficiency of the proposed algorithm in identifying the abnormalities in traffic samples within a specific network.

scholarly journals OPTIMIZATION ALGORITHMS FOR INTRUSION DETECTION SYSTEM: A REVIEW

2020 ◽  
Vol 8 (8) ◽  
pp. 217-225
Author(s):  
Sheren Sadiq ◽  
Adel Sabry Eesa
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Optimization Algorithms ◽  
Intrusion Detection Systems ◽  
The Internet ◽  
Good Source ◽  
Detection Systems ◽  
Bee Colony ◽  
System A

With the growth and development of the Internet, the devices and the hosts connected to the Internet have become the target for attackers and intruders. Consequently, the integrity of systems and data has become more sophisticated. Meanwhile, many institutions suffer from money-losing or other losses due to attacks on computer systems. Accordingly, the detection of intrusion and attacks has become a challenge and a vital necessity at the same time. Many different methods were used to build intrusion detection systems (IDSs), and all these methods seek to a plus the efficiency of intrusion detection systems. This paper is a survey which tries to covers some of the optimization algorithms used in the field of intrusion detection in past ten years such as Artificial Bee Colony (ABC), Genetic Algorithm (GA), Cuttlefish Algorithms (CFA), and Particle Swarm Optimization (PSO). It is hoped that this review will provide useful insights about the intrusion detection literature and is a good source for anyone interested in applying one of the used optimization algorithms in the field of intrusion detection.

Sign in / Sign up

Export Citation Format

Share Document