Cyber security is identified as an emerging concern for information technology management in business and society, owing to swift advances in telecommunication and wireless technologies. Cyberspace security has had a tremendous impact on numerous crucial infrastructures. Along with current security status data, historical data should be acquired by the system to implement the latest cyber security defense and protection. It also makes intelligent decisions that can provide adaptive security management and control. An intelligent cyber security framework using Hyperparameter Tuning based on Regularized Long Short-Term Memory (HT-RLSTM) technique was developed in this work to elevate the security level of core system assets. To detect various attacks, the proposed framework was trained and tested on the collection of data. Owing to missing values, poor scaling, imbalanced and overlapped data, the data was primarily incomplete and inconsistent. To elevate the decision making for detecting attacks, the inconsistent or unstructured data issue was addressed. The missing values were handled by this work along with scaling performance using the developed Kernelized Robust Scaler (KRS). Using the developed Random Over Sample-Based Density-Based Spatial Clustering Associated with Noise (ROS-DBSCAN), the imbalanced and overlapped data were handled, which was followed by the relevant feature selection of data utilizing the Sine Cosine-Based Artificial Jellyfish Search Optimization (SC-AJSO) technique. The data were split under the provision of Stratified K-Fold cross-validation along being trained in the proposed HT-RLSTM. The experimental analysis depicted that better accuracy was attained in detecting attacks by the proposed work for different datasets. When analogized with prevailing state-of-the-art methods, a low false detection rate, as well as computation time, was attained by the proposed scheme.

Intrusion detection is critical to guaranteeing the safety of the data in the network. Even though, since Internet commerce has grown at a breakneck pace, network traffic kinds are rising daily, and network behavior characteristics are becoming increasingly complicated, posing significant hurdles to intrusion detection. The challenges in terms of false positives, false negatives, low detection accuracy, high running time, adversarial attacks, uncertain attacks, etc. lead to insecure Intrusion Detection System (IDS). To offset the existing challenge, the work has developed a secure Data Mining Intrusion detection system (DataMIDS) framework using Functional Perturbation (FP) feature selection and Bengio Nesterov Momentum-based Tuned Generative Adversarial Network (BNM-tGAN) attack detection technique. The data mining-based framework provides shallow learning of features and emphasizes feature engineering as well as selection. Initially, the IDS data are analyzed for missing values based on the Marginal Likelihood Fisher Information Matrix technique (MLFIMT) that identifies the relationship among the missing values and attack classes. Based on the analysis, the missing values are classified as Missing Completely at Random (MCAR), Missing at random (MAR), Missing Not at Random (MNAR), and handled according to the types. Thereafter, categorical features are handled followed by feature scaling using Absolute Median Division based Robust Scalar (AMD-RS) and the Handling of the imbalanced dataset. The selection of relevant features is initiated using FP that uses '3' Feature Selection (FS) techniques i.e., Inverse Chi Square based Flamingo Search (ICS-FSO) wrapper method, Hyperparameter Tuned Threshold based Decision Tree (HpTT-DT) embedded method, and Xavier Normal Distribution based Relief (XavND-Relief) filter method. Finally, the selected features are trained and tested for detecting attacks using BNM-tGAN. The Experimental analysis demonstrates that the introduced DataMIDS framework produces an accurate diagnosis about the attack with low computation time. The work avoids false alarm rate of attacks and remains to be relatively robust against malicious attacks as compared to existing methods.