Kunda Suresh Babu, Yamarthi Narasimha Rao^*

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5887-5909, 2023, DOI:10.32604/cmc.2023.037486

Abstract The demand for cybersecurity is rising recently due to the rapid improvement of network technologies. As a primary defense mechanism, an intrusion detection system (IDS) was anticipated to adapt and secure computing infrastructures from the constantly evolving, sophisticated threat landscape. Recently, various deep learning methods have been put forth; however, these methods struggle to recognize all forms of assaults, especially infrequent attacks, because of network traffic imbalances and a shortage of aberrant traffic samples for model training. This work introduces deep learning (DL) based Attention based Nested U-Net (ANU-Net) for intrusion detection to address these issues and enhance detection performance.… More >

R. S. Aashmi^1,*, T. Jaya²

Computer Systems Science and Engineering, Vol.45, No.2, pp. 1295-1308, 2023, DOI:10.32604/csse.2023.027216

Abstract The integration of clusters, grids, clouds, edges and other computing platforms result in contemporary technology of jungle computing. This novel technique has the aptitude to tackle high performance computation systems and it manages the usage of all computing platforms at a time. Federated learning is a collaborative machine learning approach without centralized training data. The proposed system effectively detects the intrusion attack without human intervention and subsequently detects anomalous deviations in device communication behavior, potentially caused by malicious adversaries and it can emerge with new and unknown attacks. The main objective is to learn overall behavior of an intruder while… More >

N. Karthikeyan², P. Sivaprakash^1,*, S. Karthik²

Computer Systems Science and Engineering, Vol.45, No.1, pp. 671-685, 2023, DOI:10.32604/csse.2023.029984

Abstract Intrusion detection systems (IDS) are one of the most promising ways for securing data and networks; In recent decades, IDS has used a variety of categorization algorithms. These classifiers, on the other hand, do not work effectively unless they are combined with additional algorithms that can alter the classifier’s parameters or select the optimal sub-set of features for the problem. Optimizers are used in tandem with classifiers to increase the stability and with efficiency of the classifiers in detecting invasion. These algorithms, on the other hand, have a number of limitations, particularly when used to detect new types of threats.… More >

D. Karthikeyan^1,*, V. Mohan Raj², J. Senthilkumar², Y. Suresh²

Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 645-659, 2023, DOI:10.32604/iasc.2023.027039

Abstract The number of attacks is growing tremendously in tandem with the growth of internet technologies. As a result, protecting the private data from prying eyes has become a critical and tough undertaking. Many intrusion detection solutions have been offered by researchers in order to decrease the effect of these attacks. For attack detection, the prior system has created an SMSRPF (Stacking Model Significant Rule Power Factor) classifier. To provide creative instance detection, the SMSRPF combines the detection of trained classifiers such as DT (Decision Tree) and RF (Random Forest). Nevertheless, it does not generate any accurate findings that are adequate.… More >

Mohammad Hafiz Mohd Yusof^1,*, Abdullah Mohd Zin², Nurhizam Safie Mohd Satar²

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 2445-2466, 2022, DOI:10.32604/cmc.2022.023571

Abstract Due to polymorphic nature of malware attack, a signature-based analysis is no longer sufficient to solve polymorphic and stealth nature of malware attacks. On the other hand, state-of-the-art methods like deep learning require labelled dataset as a target to train a supervised model. This is unlikely to be the case in production network as the dataset is unstructured and has no label. Hence an unsupervised learning is recommended. Behavioral study is one of the techniques to elicit traffic pattern. However, studies have shown that existing behavioral intrusion detection model had a few issues which had been parameterized into its common… More >

S. Navaneetha Krishnan¹, P. Sundara Vadivel^2,*, D. Yuvaraj³, T. Satyanarayana Murthy⁴, Sree Jagadeesh Malla⁵, S. Nachiyappan⁶, S. Shanmuga Priya⁷

Computer Systems Science and Engineering, Vol.43, No.1, pp. 17-26, 2022, DOI:10.32604/csse.2022.021590

Abstract Wireless Sensor Networks (WSN) are commonly used to observe and monitor precise environments. WSNs consist of a large number of inexpensive sensor nodes that have been separated and distributed in different environments. The base station received the amount of data collected by the numerous sensors. The current developments designate that the attentFgion in applications of WSNs has been increased and extended to a very large scale. The Trust-Based Adaptive Acknowledgement (TRAACK) Intrusion-Detection System for Wireless Sensor Networks (WSN) is described based on the number of active positive deliveries and The Kalman filter used in Modified Particle Swarm Optimization (MPSO) has… More >

Gautham Praveen Ramalingam¹, R. Arockia Xavier Annie¹, Shobana Gopalakrishnan^2,*

Intelligent Automation & Soft Computing, Vol.32, No.3, pp. 1479-1492, 2022, DOI:10.32604/iasc.2022.022211

Abstract Detection of intrusion plays an important part in data protection. Intruders will carry out attacks from a compromised user account without being identified. The key technology is the effective detection of sundry threats inside the network. However, process automation is experiencing expanded use of information communication systems, due to high versatility of interoperability and ease off 34 administration. Traditional knowledge technology intrusion detection systems are not completely tailored to process automation. The combined use of fuzziness-based and RNN-IDS is therefore highly suited to high-precision classification, and its efficiency is better compared to that of conventional machine learning approaches. This model… More >

Youseef Alotaibi^*

CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1879-1895, 2021, DOI:10.32604/cmc.2020.013739

Abstract A new secured database management system architecture using intrusion detection systems (IDS) is proposed in this paper for organizations with no previous role mapping for users. A simple representation of Structured Query Language queries is proposed to easily permit the use of the worked clustering algorithm. A new clustering algorithm that uses a tube search with adaptive memory is applied to database log files to create users’ profiles. Then, queries issued for each user are checked against the related user profile using a classifier to determine whether or not each query is malicious. The IDS will stop query execution or… More >

Jin Yang¹, Tao Li¹, Gang Liang^1,*, Wenbo He², Yue Zhao³

CMES-Computer Modeling in Engineering & Sciences, Vol.120, No.1, pp. 1-23, 2019, DOI:10.32604/cmes.2019.04727

Abstract Deep Learning presents a critical capability to be geared into environments being constantly changed and ongoing learning dynamic, which is especially relevant in Network Intrusion Detection. In this paper, as enlightened by the theory of Deep Learning Neural Networks, Hierarchy Distributed-Agents Model for Network Risk Evaluation, a newly developed model, is proposed. The architecture taken on by the distributed-agents model are given, as well as the approach of analyzing network intrusion detection using Deep Learning, the mechanism of sharing hyper-parameters to improve the efficiency of learning is presented, and the hierarchical evaluative framework for Network Risk Evaluation of the proposed… More >