Comparing and Analyzing Applications of Intelligent Techniques in Cyberattack Detection

Table 1

Detailed analysis of comprehensive survey and research articles.

Reference	Technique used	Dataset used	Description	Outcomes
Hao et al. [36]	The hybrid form of k-means + PSO	KDDCUP99	The proposed model can be used to detect the crowd (undetermined session) is normal or an attack.	The proposed model can detect attacks with better performance.
Momanyi Nyabuga et al. [37]	Particle swarm optimization	KDDCUP99	The proposed model provides a review and discussions of the denial of service attack detection and prevention mechanisms; moreover, it intended to propose the particle swarm algorithm optimally helps to detect DOS attack.	The simulated outcomes have shown that the proposed PSO-based model was efficiently used for attack detection as compared with other methods.
Shinde and Parvat [26]	The hybrid form of PSO + SVM	NSL-KDD	The attack detection model was designed using a hybrid form of SVM machine with the PSO technique for the selection of optimal features to achieve high accuracy and performance also lower the FAR alarm than normal IDS.	The hybrid approach of machine learning and optimization technique (ABC-SVM) provides better results than the other single approach. The results showed a detection rate with 98.53% and a false alarm rate with 0.0374.
Siva Sankari1 et al. [39]	Genetic algorithms	KDDCUP99	The proposed model is designed by using the genetic algorithm (GA) for the detection of DoS.	This detection approach was better-performed attack detection but not proved to be very efficient as comparing its performance with the hybrid technique approached model. However, it provides better results than the traditional one.
Mizukoshi and Munetomo [40]	Genetic algorithms	KDDCUP99	This proposed model is based on real-time traffic pattern analysis using a genetic algorithm (GA) approach for optimal pattern extraction.	The experimental result has shown that the proposed method performed well as compared with other traditional methods.
Lee et al. [41]	Genetic algorithms	DARPA 2000, LBL-PKT-4	This proposed model is designed for the detection of distributed denial of service attack using a traffic matrix and optimizes some features of the traffic matrix by using GA.	The detection rate and accuracy by using this method were better compared with other traditional techniques.
Dimitris et al. [43]	Genetic algorithms	KDDCUP99	This proposed work is designed for the detection of DDoS attacks using a genetic algorithm for efficient feature selection and optimizing some parameters. Genetic algorithm (GA) evaluation used designed error-free neural network detector.	The evaluated results have shown that the features that best qualify for DDoS attack detection were optimally selected by the proposed approach and provide better results.
Chen et.al. [51]	Ant colony optimization	DARPA/LLDOS KDDCUP99	This proposed work investigated different complexity of the DDIACS framework and also presents its comparison with the swarm technique and other probability-based techniques.	The results have shown that the proposed framework successfully resolved the problems related to processing attributes, and DDIACS framework provides higher performance than existing methods.
Kumar and Walia [52]	Ant colony optimization	KDDCUP99	The objective of this work was to design and implement OSLR and DSR protocols for the blackhole attack also prevent the system from the threat.	After evaluation, results showed that the proposed approach performed well on various network performance metrics such as bit error rate, throughput, delay, and packet delivery ratio.
Rais and Mehmood [53]	Ant colony optimization	KDDCUP99	The proposed model used the ACO optimization technique for better feature selection by various stages of pheromones that help ants to find the optimal features.	Evaluation of the result shows that the proposed approach outperformed in optimal feature selection as compared with the traditional techniques.
Bhuyan et al. [42]	Artificial bee colony	KDDCUP99	This proposed method is applied to ABC algorithm. Anomaly-based attack detection is used by using different feature selection techniques to minimize the number of unwanted features and pick the best one.	Experimental results have shown that the performance of ABC algorithm was better than traditional approaches and also achieved a high accuracy rate.