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Article
Automation & Control Systems
Mahendra Prasad et al.
Summary: A Mobile Ad-Hoc Network (MANET) is a widely used temporary network that is vulnerable to routing attacks. Many intrusion detection methods have been proposed to tackle this vulnerability. This paper presents a performance reliability evaluation model for intrusion detection methods in MANETs, which analyzes performance and hardware dependability and computes the performance reliability using a fuzzy logic system. The experimental results show that the proposed detection method outperforms existing methods in terms of maintaining high scheme reliability.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Review
Computer Science, Hardware & Architecture
Mudhafar Nuaimi et al.
Summary: In recent years, the Industrial Internet of Things (IIoT) has experienced rapid growth and provided significant benefits to various aspects of life. However, the heterogeneity and limited resources of IIoT devices pose challenges to the security of the system. Intrusion detection systems (IDS) based on intelligent approaches such as machine learning have been enhanced to combat threats and protect IIoT systems. This survey focuses on the application of machine learning algorithms in intrusion detection in IIoT and suggests future research directions.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Sydney Mambwe Kasongo
Summary: In recent years, advances in technologies such as cloud computing, vehicular networks systems, and the Internet of Things (IoT) have led to a spike in the amount of information transmitted through communication infrastructures. Consequently, attackers have increased their efforts to exploit vulnerabilities in network systems. Therefore, it is crucial to enhance the security of these network systems. This study implements an IDS framework using Machine Learning techniques and evaluates its performance using benchmark datasets.
COMPUTER COMMUNICATIONS
(2023)
Review
Computer Science, Information Systems
Zhen Yang et al.
Summary: With the rapid evolution of network techniques, network attacks are becoming more sophisticated and threatening. Network intrusion detection is widely recognized as an effective method to address network threats. Anomaly-based network intrusion detection is an important research direction, but there is a lack of systematic literature reviews on recent techniques and datasets. In this study, we conducted a systematic literature review of 119 top-cited papers on anomaly-based intrusion detection, investigating the technical landscape of the field from various perspectives, and identifying unsolved research challenges and future research directions.
COMPUTERS & SECURITY
(2022)
Article
Chemistry, Multidisciplinary
Bo Cao et al.
Summary: In this study, a network intrusion detection model that combines a convolutional neural network and a gated recurrent unit is proposed to address the low accuracy and class imbalance problems in existing intrusion detection models. By using a hybrid sampling algorithm, feature selection, and attention mechanism, the proposed model achieves higher classification accuracy and effectively handles class imbalance. The experimental results demonstrate its superiority over existing models.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Information Systems
Marian B. Gorzalczany et al.
Summary: This article proposes an accurate and interpretable intrusion detection method for IoT systems using a knowledge-discovery data-mining/machine-learning approach. The approach, implemented as a fuzzy rule-based classifier, optimizes the tradeoff between accuracy and interpretability of IoT intrusion detection systems (IoT IDSs) through a multiobjective evolutionary optimization algorithm. The main contribution of this work is the design of accurate and interpretable IoT IDSs based on recently published MQTT-IOT-IDS2020 data sets, which describe the behavior of an MQTT-protocol-based IoT system. A comparison with seven alternative approaches demonstrates that the proposed method significantly outperforms others in terms of interpretability while remaining competitive or superior in accuracy.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Computer Science, Information Systems
Murtaza Ahmed Siddiqi et al.
Summary: This study proposes an optimal framework for a network intrusion detection system based on image processing, which enhances network security efficiency through steps such as feature selection, image transformation, and enhancement.
Article
Computer Science, Information Systems
Azzam Mourad et al.
Summary: The article discusses the challenges of intrusion detection in Internet of Vehicles and vehicular networks, and proposes a vehicular-edge computing (VEC) fog-enabled scheme to offload intrusion detection tasks with minimal latency. The scheme aims to maximize offloading survivability while minimizing computation execution time and energy consumption.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Article
Telecommunications
Arash Bozorgchenani et al.
Summary: The study investigates a flexible way of deploying fog computing services at the network edge, utilizing various service deployment models to meet client needs. The simulation results analyze the performance of different methods in terms of customer satisfaction and service delay.
IEEE COMMUNICATIONS LETTERS
(2021)
Review
Computer Science, Hardware & Architecture
Sang-Woong Lee et al.
Summary: This article focuses on the application of deep learning in intrusion detection systems, discussing how different methods utilize deep learning networks for better results. It classifies the studied IDS schemes and compares their features in different aspects. Finally, comparisons of deep IDS approaches and future research directions are provided.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2021)
Article
Engineering, Chemical
Muhammad Ashfaq Khan
Summary: Network attacks are a crucial problem in modern society, and developing effective intrusion detection systems is essential to mitigate the impact of malicious threats. Utilizing deep learning and machine learning techniques, researchers have designed a hybrid convolutional recurrent neural network intrusion detection system that achieves high accuracy in detecting malicious cyberattacks.
Article
Computer Science, Hardware & Architecture
Zemin Sun et al.
Summary: The study proposes a two-period game approach to optimize QoS and security in VANETs, with the first period focusing on external decision-making to maximize throughput, and the second period addressing internal decision-making through a game model for communication and security resource competition.
Article
Computer Science, Information Systems
Murtaza Ahmed Siddiqi et al.
Summary: This paper discusses the importance of intrusion detection in improving network security, the application in the field of machine learning, and the selection of suitable normalization methods for datasets.
Article
Computer Science, Information Systems
Deris Stiawan et al.
Summary: This study aims to find the best relevant selected features for intrusion detection system by using six feature selection methods, combining them with four classification methods to generate optimized ensemble IDSs, and evaluating them through various validation approaches to improve performance.
Article
Computer Science, Information Systems
Mohammad Mehedi Hassan et al.
INFORMATION SCIENCES
(2020)
Article
Computer Science, Information Systems
Rabia Khan et al.
IEEE COMMUNICATIONS SURVEYS AND TUTORIALS
(2020)
Article
Computer Science, Information Systems
Zina Chkirbene et al.
Article
Computer Science, Cybernetics
Reza Parsamehr et al.
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
(2019)
Article
Computer Science, Information Systems
Lionel N. Tidjon et al.
IEEE COMMUNICATIONS SURVEYS AND TUTORIALS
(2019)
Article
Computer Science, Theory & Methods
Mhamed Zineddine
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2018)
Article
Engineering, Electrical & Electronic
Akhil Gupta et al.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2018)
Review
Computer Science, Hardware & Architecture
Mohamed Amine Ferrag et al.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2018)
Article
Computer Science, Information Systems
Haojie Ji et al.
Article
Computer Science, Artificial Intelligence
Nathan Shone et al.
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE
(2018)
Article
Engineering, Electrical & Electronic
Zubair Md. Fadlullah et al.
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
(2017)
Article
Computer Science, Artificial Intelligence
Xuancai Zhao et al.
EXPERT SYSTEMS WITH APPLICATIONS
(2016)
Article
Computer Science, Artificial Intelligence
Karen A. Garcia et al.
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART C-APPLICATIONS AND REVIEWS
(2012)
Article
Computer Science, Information Systems
Tarik Taleb et al.
SECURITY AND COMMUNICATION NETWORKS
(2012)
