Research on filter-based adversarial feature selection against evasion attacks by Qimeng HUANG, Miaomiao WU, Yun LI

“…With the rapid development and widespread application of machine learning technology, its security has attracted increasing attention, leading to a growing interest in adversarial machine learning.In adversarial scenarios, machine learning techniques are threatened by attacks that manipulate a small number of samples to induce misclassification, resulting in serious consequences in various domains such as spam detection, traffic signal recognition, and network intrusion detection.An evaluation criterion for filter-based adversarial feature selection was proposed, based on the minimum redundancy and maximum relevance (mRMR) method, while considering security metrics against evasion attacks.Additionally, a robust adversarial feature selection algorithm was introduced, named SDPOSS, which was based on the decomposition-based Pareto optimization for subset selection (DPOSS) algorithm.SDPOSS didn’t depend on subsequent models and effectively handles large-scale high-dimensional feature spaces.Experimental results demonstrate that as the number of decompositions increases, the runtime of SDPOSS decreases linearly, while achieving excellent classification performance.Moreover, SDPOSS exhibits strong robustness against evasion attacks, providing new insights for adversarial machine learning.…”
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Ensemble of feature augmented convolutional neural network and deep autoencoder for efficient detection of network attacks by Selvakumar B, Sivaanandh M, Muneeswaran K, Lakshmanan B

“…A novel ensemble of deep learning technique is proposed to enhance the efficiency of Packet Flow Classification in Network Intrusion Detection System (NIDS). The proposed work consists of three phases: (i) Feature Augmented Convolutional Neural Network (FA-CNN) (ii) Deep Autoencoder (iii) Ensemble of FA-CNN and Deep Autoencoder. …”
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Combinatorial intrusion detection model based on deep recurrent neural network and improved SMOTE algorithm by Binghao YAN, Guodong HAN

“…Existing intrusion detection models generally only analyze the static characteristics of network intrusion actions,resulting in low detection rate and high false positive rate,and cannot effectively detect low-frequency attacks.Therefore,a novel combinatorial intrusion detection model (DRRS) based on deep recurrent neural network (DRNN) and region adaptive synthetic minority oversampling technique algorithm (RA-SMOTE) was proposed.Firstly,RA-SMOTE divided the low frequency attack samples into different regions adaptively and improved the number of low-frequency attack samples with different methods from the data level.Secondly,the multi-stage classification features were learned by using the level feedback units in DRNN,at the same time,the multi-layer network structure achieved the optimal non-linear fitting of the original data distribution.Finally,the intrusion detection was completed by trained DRRS.The empirical results show that compared with the traditional intrusion detection models,DRRS significantly improves the detection rate of low-frequency attacks and overall detection efficiency.Besides,DRRS has a certain detection rate for unknown new attacks.So DRRS model is effective and suitable for the actual network environment.…”
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A Comparative Analysis of Support Vector Machine and K-Nearest Neighbors Models for Network Attack Traffic Detection by Han Zhuoxi

“…Moreover, the research highlights future directions to strengthen the resilience and precision of network intrusion detection systems, ensuring the development of more effective defenses against the ever-evolving landscape of cybersecurity risks.…”
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Effectiveness Evaluation of Random Forest, Naive Bayes, and Support Vector Machine Models for KDDCUP99 Anomaly Detection Based on K-means Clustering by Zhang Majun

“…Naïve Bayes (NB). and Support Vector Machine (SVM) with the goal to boost the accuracy of predicting network intrusions. In tins paper. K-means clustering technique is applied as a preprocessing step to enhance the overall quality of network intrusion detection and maximize the accuracy of the network security measures. …”
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A Dynamic Intrusion Detection System Based on Multivariate Hotelling’s T2 Statistics Approach for Network Environments by Aneetha Avalappampatty Sivasamy, Bose Sundan

“…Attempts are made continuously for designing more efficient and dynamic network intrusion detection models. In this work, an approach based on Hotelling’s T2 method, a multivariate statistical analysis technique, has been employed for intrusion detection, especially in network environments. …”
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Network Packet Sniffer: A Case Study of Kabale University. by Mugarurebye, Shawn, Gumoshabe, Rebecca

“…Thus, it plays a crucial and essential role in dealing with network intrusion detection and unwanted traffic control. …”
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An intrusion detection model based on Convolutional Kolmogorov-Arnold Networks by Zhen Wang, Anazida Zainal, Maheyzah Md Siraj, Fuad A. Ghaleb, Xue Hao, Shaoyong Han

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Trust in Intrusion Detection Systems: An Investigation of Performance Analysis for Machine Learning and Deep Learning Models by Basim Mahbooba, Radhya Sahal, Wael Alosaimi, Martin Serrano

“…Two datasets are used to classify the IDS attack type, including wireless sensor network detection system (WSN-DS) and KDD Cup network intrusion dataset. A detailed comparison of the eight techniques’ performance using all features and selected features is made by measuring the accuracy, precision, recall, and F1-score. …”
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Data augmentation based multi-view contrastive learning graph anomaly detection by LI Yifan, LI Jiayin, LIN Xingpeng, DAI Yuanfei, XU Li

“…Graph anomaly detection is valuable in preventing harmful events such as financial fraud and network intrusion. Although contrast-based anomaly detection methods could effectively mine anomaly information based on the inconsistency of anomalous node instance pairs, avoiding the drawback of using self-coding architecture that led to the need for full graph training for the model. …”
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A two-tier optimization strategy for feature selection in robust adversarial attack mitigation on internet of things network security by Kashi Sai Prasad, P Udayakumar, E. Laxmi Lydia, Mohammed Altaf Ahmed, Mohamad Khairi Ishak, Faten Khalid Karim, Samih M. Mostafa

“…Numerous research works were keen to project intelligent network intrusion detection systems (NIDS) to avert the exploitation of IoT data through smart applications. …”
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Machine learning algorithm for intelligent detection of WebShell by Hua DAI, Jing LI, Xin-dai LU, Xin SUN

“…WebShell is a common tool for network intrusions,which has the characteristics of great harm and good concealment.The current detection method is relatively simple,and easy to be bypassed,so it is difficult to deal with complex and flexible WebShell.To solve these problems,a supervised machine learning algorithm was put forward to detect WebShell intelligently.By learning the features of existing WebShell and non-existing WebShell pages,the algorithm can make prediction of the unknown pages,and the flexibility and adaptability were both very good.Compared with the traditional WebShell detection methods,the experiment proves that the algorithm has higher detection efficiency and accuracy,and at the same time there is a certain probability to detect new types of WebShell.…”
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Intrusion detection scheme based on neural network in vehicle network by Yi-liang LIU, Ya-li SHI, Hao FENG, Liang-min WANG

“…Vehicle networking intrusion detection solutions (IDS) can be used to confirm the authenticity of the events described in the notice of traffic incidents.The current Vehicle networking IDS frequently use detection scheme based on the consistency of redundant data,to reduce dependence on redundant data,an intrusion detection scheme based on neural network is presented.The program can be described as a lot of traffic event types ,and the integrated use of the back-propagation (BP) and support vector machine (SVM) two learning algorithms.The two algorithms respectively applicable to personal safety driving fast and efficient transportation system with high detection applications.Simulation results and performance analysis show that our scheme has a faster speed intrusion detection,and has a high detection rate and low false alarm rate.…”
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An Evaluation of Variational Autoencoder in Credit Card Anomaly Detection by Faleh Alshameri, Ran Xia

“…The anomaly can occur in many forms, such as fraudulent credit card transactions, network intrusions, and anomalous imageries or documents. …”
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