Intelligent intrusion detection system based on crowd search optimization for attack classification in network security

A NSGA2-LR wrapper approach for feature selection in network intrusion detection

Predicting mineral structures at a given chemical composition before conducting any experimental practices is crucial. This paper proposes an advanced ensemble learning algorithm for accurate prediction of mineral structure using a Convolutional Neural Network (ConvNet) and material data. This approach follows a data processing approach in which raw material data undergoes a data cleaning and data wrangling process, and noise or impurities present in there are removed. The data is graphically represented by using a heat map. The feature transformation and feature scaling techniques are employed to normalize the features of the material data. This enhances the convergence of the model during training. The ConvNet is integrated to automatically predict the structure of the material from the raw data. The hyperparameters of the ConvNet are finely tuned by the Crow Search Optimization (CSO) algorithm. The CSO enhances both the accuracy and generalization of the proposed model. The integration of ConvNet with CSO technique exhibits exceptional performance in predicting the mineral structure accurately compared with existing techniques. The proposed approach is implemented in Python software and validates the accuracy of $82 \%$ thus, ensuring a promising tool for material design and discovery.

Wireless sensor nodes are low cost tiny devices deployed in unsupervised environment. These devices require some kind of security mechanism to tackle different kinds of attacks. Intrusion detection is one such methodology used where the malicious activity is observed, and the target is isolated. Many authors have suggested different methodologies which are detailed in the work. The article also uses intrusion detection scheme to provide reliable network. A mobile agent and an assistance node aids in increasing security and efficiency of the work. The illegitimacy of the node is inferred using the data provided by the assistance and mobile agent. The work minimizes 12.9% of energy consumption, increases security by 3.025%, and increases reliability by 7.07% compared to the previous work.

In network security, intrusion detection plays an important role. Feature subsets obtained by different feature selection methods will lead to different accuracy of intrusion detection. Using individual feature selection method can be unstable in different intrusion detection scenarios. In this paper, the idea of ensemble is applied to feature selection to adjust feature subsets. Feature selection is converted into a two-category problem, and odd number of feature selection methods is used for voting method to decide whether a feature is required or discarded. In actual operation, mean decrease impurity, random forest classifier, stability selection, recursive feature elimination and chi-square test are used. Feature subsets obtained from them will be adjusted by our proposed method to get ensemble feature subsets. To test the performance, support vector machine, decision tree, knn and multi-layer perception are used to observe and compare the classification accuracy with ensemble feature subsets. Three intrusion detection data sets, including kddcup99, cidds-001 and unsw_nb15 are used in our experiments. The best result is reflected on cidds-001 with a 99.40% classification accuracy. The investigation shows that our method has a certain improvement on classification accuracy of intrusion detection.

Uses of Artificial Intelligent Techniques to Build Accurate Models for Intrusion Detection System

Genetic-fuzzy rule mining approach and evaluation of feature selection techniques for anomaly intrusion detection

Orthogonal frequency-division multiplexing [OFDM] is an information transfer technique in which a single data flow is divided between several closely spaced narrowband subchannel frequency range rather than a single Wideband channel frequency. The information is sent to the relay node there is a delay and some data is lost in the relay node is the major issue in the existing system. To overcome these challenges, The objective of this study is to minimize the overall energy consumption and to maximize the network lifetime. In this paper, a novel Five Input Hybrid Optimization Relay Node Selection and Energy Efficient Routing (FIHORNSEER) technique has been proposed for choosing the best relay based on noises. Ant Lion Optimization (ALO) is initially utilized to select the relay node's elite position. Secondly, the Crow Search Optimization (CSO) Algorithm is used for the phenomenon of position and memory of each relay. Finally, the Memetic Algorithm (MA) was generated by integrating the Ant Lion and Crow search optimization algorithm for the best relay node selection. The proposed framework is compared with previous techniques like FRNSEER, LMMSE, and HABO-OFDM Methods in terms of performance analysis, such as average utility, Energy Consumption, and Network Life Time. The result shows that the proposed FIHORNSEER improves the energy consumption better than 22.01%, 16.4%, and 12.2% FRNSEER, LMMSE, and HABO-OFDM, respectively.

Intrusion detection is one of the major research problems in network security. It is the process of monitoring and analyzing network traffic data to detect security violations. Mining approach can play very important role in developing an intrusion detection system. The network traffic can be classified into normal and anomalous in order to detect intrusions. In our paper, top-ten classification algorithms namely J48, BayesNet, Logistic, SGD, IBK, JRip, PART, Random Forest, Random Tree and REPTree were selected after experimenting with more than twenty most widely used classification algorithms. The comparison of these top-ten classification algorithms is presented in this paper based upon their performance metrics to find out the best suitable algorithm available. Performance of the classification models is measured using 10-fold cross validation. Experiments and assessments of these methods are performed in WEKA environment using NSL-KDD dataset.

With the advent of the Internet of Things (IoT), the security of the network layer in the IoT is getting more and more attention. The traditional intrusion detection technologies cannot be well adapted in the complex Internet environment of IoT. For the deep learning algorithm of intrusion detection, a neural network structure may have fine detection accuracy for one kind of attack, but it may not have a good detection effect when facing other attacks. Therefore, it is urgent to design a self-adaptive model to change the network structure for different attack types. This paper presents an intrusion detection model based on improved genetic algorithm (GA) and deep belief network (DBN). Facing different types of attacks, through multiple iterations of the GA, the optimal number of hidden layers and number of neurons in each layer are generated adaptively, so that the intrusion detection model based on the DBN achieves a high detection rate with a compact structure. Finally, the NSL-KDD dataset was used to simulate and evaluate the model and algorithms. The experimental results show that the improved intrusion detection model combined with DBN can effectively improve the recognition rate of intrusion attacks and reduce the complexity of the neural network structure.

One of the deadliest diseases which affects the large intestine is colon cancer. Older adults are typically affected by colon cancer though it can happen at any age. It generally starts as small benign growth of cells that forms on the inside of the colon, and later, it develops into cancer. Due to the propagation of somatic alterations that affects the gene expression, colon cancer is caused. A standardized format for assessing the expression levels of thousands of genes is provided by the DNA microarray technology. The tumors of various anatomical regions can be distinguished by the patterns of gene expression in microarray technology. As the microarray data is too huge to process due to the curse of dimensionality problem, an amalgamated approach of utilizing bilevel feature selection techniques is proposed in this paper. In the first level, the genes or the features are dimensionally reduced with the help of Multivariate Minimum Redundancy–Maximum Relevance (MRMR) technique. Then, in the second level, six optimization techniques are utilized in this work for selecting the best genes or features before proceeding to classification process. The optimization techniques considered in this work are Invasive Weed Optimization (IWO), Teaching Learning-Based Optimization (TLBO), League Championship Optimization (LCO), Beetle Antennae Search Optimization (BASO), Crow Search Optimization (CSO), and Fruit Fly Optimization (FFO). Finally, it is classified with five suitable classifiers, and the best results show when IWO is utilized with MRMR, and then classified with Quadratic Discriminant Analysis (QDA), a classification accuracy of 99.16% is obtained.

Computing systems and networks become increasingly large and complex with a variety of compromises and vulnerabilities. The network security and privacy are of great concern today, where self-defense against different kinds of attacks in an autonomous and holistic manner is a challenging topic. To address this problem, we developed an innovative technology called Bionic Autonomic Nervous System (BANS). The BANS is analogous to biological nervous system, which consists of basic modules like cyber axon, cyber neuron, peripheral nerve and central nerve. We also presented an innovative self-defense mechanism which utilizes the Fuzzy Logic, Neural Networks, and Entropy Awareness, etc. Equipped with the BANS, computer and network systems can intelligently self-defend against both known and unknown compromises/attacks including denial of services (DoS), spyware, malware, and virus. BANS also enabled multiple computers to collaboratively fight against some distributed intelligent attacks like DDoS. We have implemented the BANS in practice. Some case studies and experimental results exhibited the effectiveness and efficiency of the BANS and the self-defense mechanism.

Due to the recent advances in the Internet and communication technologies, network systems and data have evolved rapidly. The emergence of new attacks jeopardizes network security and make it really challenging to detect intrusions. Multiple network attacks by an intruder are unavoidable. Our research targets the critical issue of class imbalance in intrusion detection, a reflection of the real-world scenario where legitimate network activities significantly out number malicious ones. This imbalance can adversely affect the learning process of predictive models, often resulting in high false-negative rates, a major concern in Intrusion Detection Systems (IDS). By focusing on datasets with this imbalance, we aim to develop and refine advanced algorithms and techniques, such as anomaly detection, cost-sensitive learning, and oversampling methods, to effectively handle such disparities. The primary goal is to create models that are highly sensitive to intrusions while minimizing false alarms, an essential aspect of effective IDS. This approach is not only practical for real-world applications but also enhances the theoretical understanding of managing class imbalance in machine learning. Our research, by addressing these significant challenges, is positioned to make substantial contributions to cybersecurity, providing valuable insights and applicable solutions in the fight against digital threats and ensuring robustness and relevance in IDS development. An intrusion detection system (IDS) checks network traffic for security, availability, and being non-shared. Despite the efforts of many researchers, contemporary IDSs still need to further improve detection accuracy, reduce false alarms, and detect new intrusions. The mean convolutional layer (MCL), feature-weighted attention (FWA) learning, a bidirectional long short-term memory (BILSTM) network, and the random forest algorithm are all parts of our unique hybrid model called MCL-FWA-BILSTM. The CNN-MCL layer for feature extraction receives data after preprocessing. After convolution, pooling, and flattening phases, feature vectors are obtained. The BI-LSTM and self-attention feature weights are used in the suggested method to mitigate the effects of class imbalance. The attention layer and the BI-LSTM features are concatenated to create mapped features before feeding them to the random forest algorithm for classification. Our methodology and model performance were validated using NSL-KDD and UNSW-NB-15, two widely available IDS datasets. The suggested model's accuracies on binary and multi-class classification tasks using the NSL-KDD dataset are 99.67% and 99.88%, respectively. The model's binary and multi-class classification accuracies on the UNSW-NB15 dataset are 99.56% and 99.45%, respectively. Further, we compared the suggested approach with other previous machine learning and deep learning models and found it to outperform them in detection rate, FPR, and F-score. For both binary and multiclass classifications, the proposed method reduces false positives while increasing the number of true positives. The model proficiently identifies diverse network intrusions on computer networks and accomplishes its intended purpose. The suggested model will be helpful in a variety of network security research fields and applications.

As the typical anomaly detection methods using statistics, entropy and χ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> based method has been researched and reported in terms of their properties for anomaly attacks. In this research, we compare the properties of both methods and discuss the accuracy of detection and the efficiency for different kinds of attacks. Our previous researches have clarified that the source IP address and destination port number are efficient statistical variables to view the anomaly packet property, which lead to detect correctly. In this paper, we propose EMMM method for entropy value and CSDM method of χ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> value using multi statistical variables. The experiments to verify our proposed methods were conducted using source IP address, destination port number and arriving interval of packets. We could extract the following results. Firstly, EMMM method could decrease the value of False-Positive and False-Negative. Secondly, CSDM method could increase the F-metric, which is the evaluation standard for accurate detection. In the experiments using the same condition of parameters such as probability valuables and window width, CSDM method enlarges the F-metric compared to EMMM method.

Aiming at the problem of low detection accuracy of network traffic data types by traditional intrusion detection methods, we propose an improved Harris Hawk hybrid intrusion detection method to enhance the detection capability. The improved Harris Hawk optimization algorithm is used as a feature selection scheme to reduce the impact of redundant and noisy features on the performance of the classification model. The algorithm introduces the singer map to initialise the population, uses multi-information fusion to obtain the best prey position, and applies the sine function-based escape energy to execute a prey search strategy to obtain the optimal subset of features. In addition, the original data is preprocessed by the k-nearest neighbour and deep denoising autoencoder (KNN-DDAE) to relieve the imbalance problem of the network traffic data. Finally, a deep neural network (DNN) is used to complete the classification. Simulation experiments are conducted on the dataset NSL-KDD, KDD CUP99, and UNSW-NB15. The results show that our feature selection and data balancing scheme greatly improves the detection accuracy. In addition, the detection performance of this method is better than the current popular intrusion detection schemes.

Natural ecosystems have been facing a major threat due to deforestation and forest fires for the past decade. These environmental challenges have led to significant biodiversity loss, disruption of natural habitats, and adverse effects on climate change. The integration of Artificial Intelligence (AI) and Optimization techniques has made a revolutionary impact in disaster management, offering new avenues for early detection and prevention strategies. Therefore, to prevent the outbreak of a forest fire, an efficient forest fire diagnosis and aversion system is needed. To address this problem, an IoT-based Artificial Intelligence (AI) technique for forest fire detection has been proposed. This system leverages the Internet of Things (IoT) to collect real-time data from various sensors deployed in forest areas, providing continuous monitoring and early warning capabilities. Several researchers have contributed different techniques to predict forest fires at various remote locations, highlighting the importance of innovative approaches in this field. The proposed work involves object detection, which is facilitated by EfficientDet, a state-of-the-art object detection model known for its accuracy and efficiency. EfficientDet enables the system to accurately identify potential fire outbreaks by analyzing visual data from the sensors. To facilitate efficient detection at the outbreak of forest fires, a bi-directional gated recurrent neural network (Bi-GRU-NN) is needed. This neural network architecture is capable of processing sequential data from multiple directions, enhancing the system’s ability to predict the spread and intensity of fires. Crow Search Optimization (CSO) and fractional calculus are used to create an optimal solution in the proposed crow search fractional calculus optimization (CSFCO) algorithm for deep learning. CSO is inspired by the intelligent foraging behavior of crows, and when combined with fractional calculus, it provides a robust optimization framework that improves the accuracy and efficiency of the AI model. Experimental analysis shows that the proposed technique outperformed the other existing traditional approaches with an accuracy of 99.32% and an error rate of 0.12%. These results demonstrate the effectiveness of the integrated AI and optimization techniques in enhancing forest fire detection and prevention. The high accuracy and low error rate underscore the potential of this system to be a valuable tool in mitigating the risks associated with forest fires, ultimately contributing to the preservation of natural ecosystems.