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Journal articles on the topic 'Distributed intrusion detection system'

Author: Grafiati
Published: 3 June 2025
Last updated: 7 July 2025

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1

Ujeniya, A. A., R. D. Pawar, S. A. Sonawane, S. B. Shingade, and S. R. Khonde. "Hybrid Distributed Intrusion Detection System." International Journal of Computer Sciences and Engineering 6, no. 12 (2018): 232–37. http://dx.doi.org/10.26438/ijcse/v6i12.232237.

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2

Hong, Bo, Hui Wang, and Zijian Cao. "An Effective Fault-Tolerant Intrusion Detection System under Distributed Environment." Wireless Communications and Mobile Computing 2021 (October 19, 2021): 1–9. http://dx.doi.org/10.1155/2021/2716881.

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Traditional intrusion detection system is limited to a single network or several hosts, which has been seriously unable to fulfill the growing information security problems. This paper uses the distributed technology to design and implement an intrusion detection system (IDS) based on the hybrid of Hadoop with some effective open-source technologies. On the one hand, it can efficiently realize the data acquisition and analysis under distributed environment. On the other hand, it can solve the problems of single-point fault-tolerant and the insufficient data processing capacity of the traditional intrusion detection system. In this IDS, RabbitMQ, Flume, and MongoDB are utilized to act as the middleware of this system to build the system environment which includes the collector, analyzer, and data storage. By detecting the CPU and memory usage of hosts, TCP connections, network bandwidth, web server operation logs, and the logs of user behavior, the proposed IDS especially focuses on monitoring the first four parts, which can better detect external distributed denial of service attacks and intrusions and send automatically alarm service information to the administrators.
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3

A. M., Riyad, M. S. Irfan Ahmed, and R. L. Raheemaa Khan. "An adaptive distributed Intrusion detection system architecture using multi agents." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 6 (2019): 4951. http://dx.doi.org/10.11591/ijece.v9i6.pp4951-4960.

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Intrusion detection systems are used for monitoring the network data, analyze them and find the intrusions if any. The major issues with these systems are the time taken for analysis, transfer of bulk data from one part of the network to another, high false positives and adaptability to the future threats. These issues are addressed here by devising a framework for intrusion detection. Here, various types of co-operating agents are distributed in the network for monitoring, analyzing, detecting and reporting. Analysis and detection agents are the mobile agents which are the primary detection modules for detecting intrusions. Their mobility eliminates the transfer of bulk data for processing. An algorithm named territory is proposed to avoid interference of one analysis agent with another one. A communication layout of the analysis and detection module with other modules is depicted. The inter-agent communication reduces the false positives significantly. It also facilitates the identification of distributed types of attacks. The co-ordinator agents log various events and summarize the activities in its network. It also communicates with co-ordinator agents of other networks. The system is highly scalable by increasing the number of various agents if needed. Centralized processing is avoided here to evade single point of failure. We created a prototype and the experiments done gave very promising results showing the effectiveness of the system.
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Riyad, A. M., S. Irfan Ahmed M., and L. Raheemaa Khan R. "An adaptive distributed intrusion detection system architecture using multi agents." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 6 (2019): 4951–60. https://doi.org/10.11591/ijece.v9i6.pp4951-4960.

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Intrusion detection systems are used for monitoring the network data, analyze them and find the intrusions if any. The major issues with these systems are the time taken for analysis, transfer of bulk data from one part of the network to another, high false positives and adaptability to the future threats. These issues are addressed here by devising a framework for intrusion detection. Here, various types of co-operating agents are distributed in the network for monitoring, analyzing, detecting and reporting. Analysis and detection agents are the mobile agents which are the primary detection modules for detecting intrusions. Their mobility eliminates the transfer of bulk data for processing. An algorithm named territory is proposed to avoid interference of one analysis agent with another one. A communication layout of the analysis and detection module with other modules is depicted. The inter-agent communication reduces the false positives significantly. It also facilitates the identification of distributed types of attacks. The co-ordinator agents log various events and summarize the activities in its network. It also communicates with co-ordinator agents of other networks. The system is highly scalable by increasing the number of various agents if needed. Centralized processing is avoided here to evade single point of failure. We created a prototype and the experiments done gave very promising results showing the effectiveness of the system.
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5

Kaur, Harpreet. "NETWORK INTRUSION DETECTION AND PREVENTION ATTACKS." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 2, no. 3 (2012): 21–23. http://dx.doi.org/10.24297/ijct.v2i3a.2669.

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Intrusion detection is an important technology in business sector as well as an active area of research. It is an important tool for information security. A Network Intrusion Detection System is used to monitor networks for attacks or intrusions and report these intrusions to the administrator in order to take evasive action. Today computers are part of networked; distributed systems that may span multiple buildings sometimes located thousands of miles apart. The network of such a system is a pathway for communication between the computers in the distributed system. The network is also a pathway for intrusion. This system is designed to detect and combat some common attacks on network systems. It follows the signature based IDs methodology for ascertaining attacks. A signature based IDS will monitor packets on the network and compare them against a database of signatures or attributes from known malicious threats. In this system the attack log displays the list of attacks to the administrator for evasive action. This system works as an alert device in the event of attacks directed towards an entire network.
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Shrikant, Vanve* Prof. Sarita Patil. "OGEDIDS: OPPOSITIONAL GENETIC PROGRAMMING ENSEMBLE FOR DISTRIBUTED INTRUSION DETECTION SYSTEMS." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 7 (2016): 756–62. https://doi.org/10.5281/zenodo.57737.

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Due to the wide range application of internet and computer networks, the securing of information is indispensable one. In order to secure the information system more effectively, various distributed intrusion detection has been developed in the literature. In this paper, we utilize the oppositional genetic algorithm for Distributed Network Intrusion Detection utilizing the oppositional set based population selection mechanism. This system is mostly useful for detecting unauthorized & malicious attack in distributed network. Here, Oppositional genetic algorithm (OGA) is utilized in OGA ensemble for learning the intrusion detection behavior of networks. Also, OGA ensemble is adapted for distributed intrusion detection system by creating the network profile which classifies normal and abnormal behavior of network. For experimentation, network profile contains different classifier which uses training data set of KDD Cup 99 to generate intrusion rules. For validation, we utilize the confusion matrix, sensitivity, specificity and accuracy and the results are proved that the proposed OGEdIDS are better for intrusion detection
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7

Kaur, Avneet, Shruti Pawar, Neha Jore, Varsha Chavan, and Nikita Mule. "Intrusion Detection System using Blockchain." International Journal for Research in Applied Science and Engineering Technology 12, no. 2 (2024): 569–71. http://dx.doi.org/10.22214/ijraset.2024.58361.

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Abstract: This paper investigates the integration of an Intrusion Detection System (IDS) within the context of blockchain technology. The objective is to enhance the security posture of blockchain networks by detecting and mitigating potential intrusions. Through a meticulous examination of the current threat landscape and the unique challenges posed by blockchain systems, this research proposes a robust IDS framework tailored to the specific requirements of decentralized and distributed ledger environments. The study employs [specific methodology/approach] to assess the effectiveness of the proposed IDS, presenting conclusive findings that contribute to the ongoing discourse on securing blockchain ecosystems. The implications of this research extend to bolstering the resilience of blockchain networks against emerging threat.
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8

Seyedeh, Yasaman Rashida. "HYBRID ARCHITECTURE FOR DISTRIBUTED INTRUSION DETECTION SYSTEM IN WIRELESS NETWORK." International Journal of Network Security & Its Applications (IJNSA) 5, no. 3 (2013): 45–54. https://doi.org/10.5281/zenodo.4267213.

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In order to the rapid growth of the network application, new kinds of network attacks are emerging endlessly. So it is critical to protect the networks from attackers and the Intrusion detection technology becomes popular. Therefore, it is necessary that this security concern must be articulate right from the beginning of the network design and deployment. The intrusion detection technology is the process of identifying network activity that can lead to a compromise of security policy. Lot of work has been done in detection of intruders. But the solutions are not satisfactory. In this paper, we propose a novel Distributed Intrusion Detection System using Multi Agent In order to decrease false alarms and manage misuse and anomaly detects. 
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9

Xie, Ping, and Wei Wang. "The Study and Simulation on Campus Network Intrusion Detection System." Advanced Materials Research 490-495 (March 2012): 2657–61. http://dx.doi.org/10.4028/www.scientific.net/amr.490-495.2657.

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In this paper, the current intrusion detection systems are analyzed in the full study of the development trend of domestic and foreign country. According to the campus network can be divided into functional independence of the structural characteristics of the subnet, while taking full advantage of agent technology in the intrusion detection system technology, we have referenced to the agent technology and a variety of detection methods for the analysis and comparison, and have analyzed the existing distributed intrusion detection system ,we propose a monitoring and management center with a multi-agent intrusion detection model framework. This model uses a distributed architecture that combines network-and host-based intrusion detection method for intrusion detection.
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10

Manikandan, Deepa, and Jayaseelan Dhilipan. "Machine learning approach for intrusion detection system using dimensionality reduction." Indonesian Journal of Electrical Engineering and Computer Science 34, no. 1 (2024): 430. http://dx.doi.org/10.11591/ijeecs.v34.i1.pp430-440.

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As cyberspace has emerged, security in all the domains like networks, cloud, and databases has become a greater concern in real-time distributed systems. Existing systems for detecting intrusions (IDS) are having challenges coping with constantly changing threats. The proposed model, DR-DBMS (dimensionality reduction in database management systems), creates a unique strategy that combines supervised machine learning algorithms, dimensionality reduction approaches and advanced rule-based classifiers to improve intrusion detection accuracy in terms of different types of attacks. According to simulation results, the DR-DBMS system detected the intrusion attack in 0.07 seconds and with a smaller number of features using the dimensionality reduction and feature selection techniques efficiently.
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Manikandan, Deepa, and Jayaseelan Dhilipan. "Machine learning approach for intrusion detection system using dimensionality reduction." Indonesian Journal of Electrical Engineering and Computer Science 34, no. 1 (2024): 430–40. https://doi.org/10.11591/ijeecs.v34.i1.pp430-440.

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As cyberspace has emerged, security in all the domains like networks, cloud, and databases has become a greater concern in real-time distributed systems. Existing systems for detecting intrusions (IDS) are having challenges coping with constantly changing threats. The proposed model, DR-DBMS (dimensionality reduction in database management systems), creates a unique strategy that combines supervised machine learning algorithms, dimensionality reduction approaches and advanced rule-based classifiers to improve intrusion detection accuracy in terms of different types of attacks. According to simulation results, the DR-DBMS system detected the intrusion attack in 0.07 seconds and with a smaller number of features using the dimensionality reduction and feature selection techniques efficiently.
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12

Khonde, Shraddh, and Ulagamuthalvi Venugopal. "Hybrid Architecture for Distributed Intrusion Detection System." Ingénierie des systèmes d information 24, no. 1 (2019): 19–28. http://dx.doi.org/10.18280/isi.240102.

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13

LiuHui and Liu Songhua. "Distributed Firewall with Intrusion Detection System Techniques." Advanced Science Letters 19, no. 11 (2013): 3297–300. http://dx.doi.org/10.1166/asl.2013.5139.

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14

Luo, Ke. "A distributed SDN-based intrusion detection system for IoT using optimized forests." PLOS ONE 18, no. 8 (2023): e0290694. http://dx.doi.org/10.1371/journal.pone.0290694.

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Along with the expansion of Internet of Things (IoT), the importance of security and intrusion detection in this network also increases, and the need for new and architecture-specific intrusion detection systems (IDS) is felt. In this article, a distributed intrusion detection system based on a software defined networking (SDN) is presented. In this method, the network structure is divided into a set of sub-networks using the SDN architecture, and intrusion detection is performed in each sub-network using a controller node. In order to detect intrusion in each sub-network, a decision tree optimized by black hole optimization (BHO) algorithm is used. Thus, the decision tree deployed in each sub-network is pruned by BHO, and the split points in its decision nodes are also determined in such a way that the accuracy of each tree in detecting sub-network attacks is maximized. The performance of the proposed method is evaluated in a simulated environment and its performance in detecting attacks using the NSLKDD and NSW-NB15 databases is examined. The results show that the proposed method can identify attacks in the NSLKDD and NSW-NB15 databases with an accuracy of 99.2% and 97.2%, respectively, which indicates an increase compared to previous methods.
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15

Hofmeyr, Steven A., and Stephanie Forrest. "Architecture for an Artificial Immune System." Evolutionary Computation 8, no. 4 (2000): 443–73. http://dx.doi.org/10.1162/106365600568257.

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An artificial immune system (ARTIS) is described which incorporates many properties of natural immune systems, including diversity, distributed computation, error tolerance, dynamic learning and adaptation, and self-monitoring. ARTIS is a general framework for a distributed adaptive system and could, in principle, be applied to many domains. In this paper, ARTIS is applied to computer security in the form of a network intrusion detection system called LISYS. LISYS is described and shown to be effective at detecting intrusions, while maintaining low false positive rates. Finally, similarities and differences between ARTIS and Holland's classifier systems are discussed.
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16

Gondal, Farzana Kausar. "Mobile Agent (MA) Based Intrusion Detection Systems (IDS): A Systematic Review." Innovative Computing Review 1, no. 2 (2021): 85–102. http://dx.doi.org/10.32350/icr.0102.05.

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An Intrusion Detection System (IDS) identifies the attacks by analysing the events, considered undesirable from a security perspective, in systems and networks. It is necessary for organizations to install IDS for the protection of sensitive data due to an increase in the number of incidents related to network security. It is difficult to detect intrusions from a segment that is outside a network as well as an intrusion that originated from inside a distributed network. It should be the responsibility of IDS to analyse a huge amount of data without overloading the networks and monitoring systems. Mobile agents (MA) emerged due to the deficiencies and limitations in centralized IDS. These agents can perform predefined actions by detecting malicious activities. From previously published literature, it was deduced that most of the existing IDS based on MA are not significantly effective due to limited intrusion detection and high detection time. This study categorized existing IDS and available MA-IDS to conduct a strategic review focusing on the classification of each category, that is, data collection modes, architecture, analysis techniques, and security. The limitations and strengths of the discussed IDS are presented/showcased wherever applicable. Additionally, this study suggested ways to improve the efficiency of available MA-IDS in order to secure distributed networks in the future. This overview also includes different implementations of agent based IDS.
 INDEX TERMS: data mining, distributed systems, Intrusion Detection System (IDS), Mobile Agents (MA), network security
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17

Ganapathy, S., P. Yogesh, and A. Kannan. "Intelligent Agent-Based Intrusion Detection System Using Enhanced Multiclass SVM." Computational Intelligence and Neuroscience 2012 (2012): 1–10. http://dx.doi.org/10.1155/2012/850259.

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Intrusion detection systems were used in the past along with various techniques to detect intrusions in networks effectively. However, most of these systems are able to detect the intruders only with high false alarm rate. In this paper, we propose a new intelligent agent-based intrusion detection model for mobile ad hoc networks using a combination of attribute selection, outlier detection, and enhanced multiclass SVM classification methods. For this purpose, an effective preprocessing technique is proposed that improves the detection accuracy and reduces the processing time. Moreover, two new algorithms, namely, an Intelligent Agent Weighted Distance Outlier Detection algorithm and an Intelligent Agent-based Enhanced Multiclass Support Vector Machine algorithm are proposed for detecting the intruders in a distributed database environment that uses intelligent agents for trust management and coordination in transaction processing. The experimental results of the proposed model show that this system detects anomalies with low false alarm rate and high-detection rate when tested with KDD Cup 99 data set.
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18

Özalp, Murat, Cihan Karakuzu, and Ahmet Zengin. "Distributed Intrusion Detection Systems: A Survey." Academic Perspective Procedia 2, no. 3 (2019): 400–407. http://dx.doi.org/10.33793/acperpro.02.03.18.

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In this paper, distributed intrusion detection systems (IDSs)in the literature are reviewed. There are two types of IDS, depending on the interoperability. Stand-alone systems decide on their own. Distributed systems are composed of multiple components processing different data and work together to make a global decision. Distributed IDSs present some difficulties compared to stand-alone systems. For example, problems such as the structure of message communication, establishment of a trust mechanism, joint decision making are the issues discussed in the studies related to such systems. A detailed literature review has been made for the distributed IDSs which are the focus of our study. The studies considered to be within the scope of our study were investigated and presented comparatively. Although the initial studies on interoperable systems began in the 1990s, the issue is still open to improvement, as there is no widespread system that has become "product". On the other hand, due to the development of artificial intelligence systems, innovative studies are being conducted on cyber threat detection. Therefore, the subject is thought to be open to improvement and in the last part of the study, suggestions are given for those who want to work on the subject.
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19

Grzech, Adam Piotr. "Optimal monitoring system for a distributed intrusion detection system." Artificial Life and Robotics 14, no. 4 (2009): 453–56. http://dx.doi.org/10.1007/s10015-009-0740-8.

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20

Tang, Qian Jun, Yan Zhang, and Yong Ju Li. "A Case Study of Distributed Network Fault Detection Technology in Distance Education." Advanced Materials Research 760-762 (September 2013): 1282–87. http://dx.doi.org/10.4028/www.scientific.net/amr.760-762.1282.

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The intrusion detection under the environment of IPv6 is an important security technology along with firewall in system security defense system, which can be used for real-time detection and monitoring of the system in the whole process of system invasion. This paper puts forward an intrusion detection system under IPv6 platform based on intrusion detection feature attribute reduction by using pattern matching, so as to expand the range of application and user group of the security products. By the analysis and comparison of various pattern matching algorithms, the new algorithm realizes the intrusion feature module matching under IPv6, and make detection system be of high efficiency. Later experiments have proved this view.
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21

Liu, Yang Bin, Liang Shi, Bei Zhan Wang, Yuan Qin Wu, and Pan Hong Wang. "An New Agent Based Distributed Adaptive Intrusion Detection System." Advanced Materials Research 532-533 (June 2012): 624–29. http://dx.doi.org/10.4028/www.scientific.net/amr.532-533.624.

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In order to overcome the excessive dependence among the traditional intrusion detection system components, high rate false-alarm phenomenon caused by multiple alarms to the same invasion, inability to adaptively replace mining algorithm when testing environment has changed and other issues, this paper puts forward an Agent based distributed adaptive intrusion detection system, which employs Joint Detection mechanism for mining algorithm module, and Dynamic Election algorithm for the recovery mechanism, thereby improving the system adaptive ability to the external change.
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22

Jaiswal, Ms Rashmi, and Ms Chandramala Amarji. "A Distributed Intrusion Detection System for AODV Network." International Journal for Research in Applied Science and Engineering Technology 10, no. 8 (2022): 1576–86. http://dx.doi.org/10.22214/ijraset.2022.46247.

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Abstract: The Ad hoc On-Demand Distance Vector (AODV) routing protocol, designed for mobile ad hoc networks, offers quick adaptation to dynamic link conditions, low processing and memory overhead, and low network utilization. However, without keeping in mind the security issues in the protocol design, AODV is vulnerable to various kinds of attacks. This thesis analyzes some of the vulnerabilities, specifically discussing attacks against AODV that manipulate the routing messages. We propose a solution based on specification-based intrusion detection to detect attacks on AODV. Briefly, our approach involves the use of finite state machines for specifying correct AODV routing behavior and distributed network monitors for detecting run-time violation of the specifications. In addition, one additional field in the protocol message is proposed to enable the monitoring. We illustrate that our algorithm, which employs a tree data structure, can effectively detect most of the serious attacks in real time and with minimum overhead. Routing attacks will have distressing effects over the network and bequest a significant challenge once planning strong security mechanisms for vehicular communication. In this paper, we examine the effect and malicious activities of a number of the foremost common attacks and also mention some security schemes against some major attacks in VANET. The attacker's aim is only to modify the actual route or provides the false data about the route to the sender and also some attackers are only flooding unwanted packets to consume resources in available network. Various routing approaches are also mentioned in the paper because the routing of data is very important to deliver the traffic information to leading vehicles. It's advised that a number of the ways that to approach this made field of analysis issues in VANET might be to fastidiously design new secure routing protocols in which attacks are often rendered meaningless and because of the inherent constraints found in the network, there's a desire for light-weight and sturdy security mechanisms.
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23

Kumar, Aravendra. "Distributed Intrusion Detection System for Wireless Sensor Networks." IOSR Journal of Computer Engineering 14, no. 1 (2013): 61–70. http://dx.doi.org/10.9790/0661-1416170.

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24

HUANG, Wen-wen. "Communication mechanism designed for distributed intrusion detection system." Journal of Computer Applications 28, no. 4 (2008): 843–45. http://dx.doi.org/10.3724/sp.j.1087.2008.00843.

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25

Abraham, Ajith, Ravi Jain, Johnson Thomas, and Sang Yong Han. "D-SCIDS: Distributed soft computing intrusion detection system." Journal of Network and Computer Applications 30, no. 1 (2007): 81–98. http://dx.doi.org/10.1016/j.jnca.2005.06.001.

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26

Ke, Yun, and Yan Li. "Distributed Intrusion Detection and Research of Fragment Attack Based-on IPv6." Advanced Materials Research 268-270 (July 2011): 1797–801. http://dx.doi.org/10.4028/www.scientific.net/amr.268-270.1797.

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IPv6, the address has to aggregation, thus greatly reducing the length of the routing equipment routing table to improve the efficiency of routing and security, but then there is any possibility of network intrusion attack. This paper used to implement IPv6 Snort intrusion detection software, intrusion detection system is proposed as long as the server itself TCP / IP stack on the handling of data packets are different, the packet will bypass the intrusion detection system from the ground to produce a TCP fragment attack.
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Sharma, Vishwas, Dharmesh Shah, Sachin Sharma, and Sunil Gautam. "Artificial Intelligence based Intrusion Detection System – A Detailed Survey." ITM Web of Conferences 65 (2024): 04002. http://dx.doi.org/10.1051/itmconf/20246504002.

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The Internet and communications have rapidly expanded, leading to a significant rise in data generation and heterogeneity. Intrusion detection systems play a crucial role in ensuring the security and integrity of computer systems. These systems have been developed by researchers, academicians, and practitioners to effectively detect and mitigate network attacks. Intrusion detection systems are designed to analyze network traffic and compare it with a baseline of normal behavior, allowing them to identify any deviations or inconsistencies that may indicate an intrusion. Furthermore, the cooperative and distributed architecture of intrusion detection systems enables them to effectively detect attacks and protect the network from unauthorized access. Additionally, to enhance the performance of intrusion detection systems, techniques such as resampling the dataset and utilizing classifier ensemble are used to improve the classification accuracy. Moreover, intrusion detection systems have been integrated with intrusion response systems to ensure a timely and effective response to detected attacks. AI-based Intrusion Detection Systems have emerged as a crucial tool in ensuring network security and combating cyber threats. These systems utilize artificial intelligence algorithms to analyze network traffic, identify patterns of malicious activity, and detect potential cyber-attacks. They have proven to be highly effective in improving the detection accuracy, reducing false alarms, and even detecting previously unknown types of attacks. In summary, the development of accurate and efficient intrusion detection systems is crucial for ensuring network security. In today’s rapidly changing world, the significance of accurate intrusion detection systems cannot be overstated.
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Jasmin Salma, S., and B. Aysha Banu. "Revealing of Reducing Manners in Ad Hoc Networks with Crosslayer Approach Using SVM and FDA in Distributed Architecture." Asian Journal of Computer Science and Technology 1, no. 1 (2012): 76–79. http://dx.doi.org/10.51983/ajcst-2012.1.1.1666.

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Ad hoc network is a structure less network with independent nodes. In the ad hoc network, the nodes have to cooperate for services like routing and data forwarding. The routing attacks in ad hoc networks have given rise to the need for designing novel intrusion detection algorithms, different from those present in conventional networks. In this work, distributed intrusion detection system (IDS) have proposed for detecting malicious sinking behavior in ad hoc network. Detection process of that sinking behavior node is very important to do the further forwarding process in network. Intrusion detection system use linear classifiers for training the intrusion detection model. Cross -layer approach is involved to increase the accuracy of intrusion detection process in ad hoc network. A machine learning algorithm in non linear manner named as Support Vector Machine (SVM) involved for training the detection system and used together with Fisher Discriminant Analysis (FDA). The proposed cross-layer approach aided by a combination of SVM and FDA reduces the feature set of MAC layer without reducing information content.
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29

Gupta, Piyush. "Federated Learning for Distributed Intrusion Detection." International Journal for Research in Applied Science and Engineering Technology 13, no. 5 (2025): 1942–45. https://doi.org/10.22214/ijraset.2025.70578.

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With the proliferation of distributed systems such as IoT, industrial control systems, and underwater sensor networks (UWSNs), sustaining cybersecurity in a decentralized, bandwidth-constrained, and privacy-sensitive environment has become increasingly difficult. Traditional intrusion detection systems (IDS) are unable to expand successfully due to their reliance on centralized data aggregation. Federated Learning (FL) provides a promising approach since it trains models locally and only shares model updates, ensuring data privacy and lowering communication overhead. In this research, we present a Federated Learning-based Distributed Intrusion Detection System (FL-DIDS) that combines energy economy, node mobility management, and asynchronous learning to operate well in limited contexts. Drawing on the communication architecture of UWSNs, we provide a robust and adaptive security framework.
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Joseph, Jennifer E., Ngozi Tracy Aleke, and Onyinyechukwu Prisca Onyeanisi. "Deep Learning Based Intrusion Detection System for Network Security in IoT System." International Journal of Education, Management, and Technology 3, no. 1 (2025): 119–38. https://doi.org/10.58578/ijemt.v3i1.4539.

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The Internet of Things (IoT) has grown rapidly, leading to unparalleled connectivity and vast amounts of data. Anomaly detection plays a crucial role in identifying unusual behavior that deviates from the system's normal operation, enabling the swift detection and resolution of these anomalies. The integration of artificial intelligence (AI) with IoT significantly improves the effectiveness of anomaly detection, enhancing the performance, dependability, and security of IoT systems. AI-powered anomaly detection methods can recognize a wide array of threats within IoT environments, such as brute force attacks, buffer overflows, injection attacks, replay attacks, Distributed Denial of Service (DDoS) attacks, SQL injection, and backdoor threats. Intelligent Intrusion Detection Systems (IDS) are essential for IoT devices, as they help monitor networks for intrusions or anomalies. With the increasing adoption of IoT across various industries and its extensive attack surface, it offers more opportunities for malicious actors to exploit vulnerabilities. This paper reviews existing literature on anomaly detection in IoT systems using machine learning and deep learning approaches. It discusses the challenges associated with detecting intrusions and anomalies in IoT environments, emphasizing the rise in attacks. Recent advancements in machine learning and deep learning techniques for anomaly detection in IoT networks are examined, and the paper concludes that there is a need for further enhancement of these systems through the use of diverse datasets, real-time testing, and scalability improvements.
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31

Zhang, Hanqing. "Distributed Intrusion Detection Model in Wireless Sensor Network." International Journal of Online Engineering (iJOE) 11, no. 9 (2015): 61. http://dx.doi.org/10.3991/ijoe.v11i9.5067.

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The security issues of Wireless Sensor Network (WSN) are significant, among which intrusion detection can improve the defense detection performance of WSN, and also balance the security and energy-saving accurately and efficiently. In this paper, we focus on the intrusion detection problem in WSN. Specifically, we propose a cluster-based collaborative detection structure, and the detection algorithm is based on immunity system and Ant Colony Optimization (ACO). The basic idea is to formulate intrusion detection as an optimization problem and introduce immune mechanism into ACO during iterations. Finally, the experiment shows that proposed algorithm outperforms other methods.
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Et. al., T. Sushma,. "A Review of the cluster based Mobile Adhoc Network Intrusion Detection System." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 2 (2021): 2070–76. http://dx.doi.org/10.17762/turcomat.v12i2.1811.

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The Mobile Ad-hoc Network is decentralized and consisting of numerous different communication devices. Its distributed design and lack of infrastructure are the means of numerous network assaults. For personal computer users, companies, and the military, network security has become more important. Safety becomes a significant issue with the rise of the internet, and the past of security enables a better understanding of the evolution of security technology. Via the audit and monitoring phase, the implementation of Intrusion Detection Systems (IDS) in ad-hoc node securities was improved. This framework is made up of clustering protocols that are extremely efficient in finding intrusions with low resource and overhead computing costs. Current protocols have been related to routes that are not popular in intrusion detection. The cluster is barely impacted by the weak road layout and route renewal. The cluster is unpredictable and results in processing maximization together with network traffic. In general, battery-based ad hoc networks are organized and dependent on power constraints. To detect and react rapidly against intrusions, an active monitoring node is required. Only if the clusters are strong and extensive maintaining capabilities can it be accomplished. The routes also shift as the cluster shifts and it would not be feasible to prominently process the achievement of intrusion detection. This raises the need for a better clustering algorithm that addresses these disadvantages and guarantees the protection of the network in any way. A powerful clustering algorithm that is ahead of the current routing protocol is the cluster-based Intrusion Detection Method. Regardless of routes that perfectly track the intrusion, it is permanent. This streamlined technique of clustering achieves strong intrusion detection speeds with low processing as well as memory overhead. It also overcomes the other limitations of traffic, connections, and node mobility on the network, regardless of the routes. In detecting the attack or malicious node, the individual nodes in the network are not active.
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Elmasry, Wisam, Akhan Akbulut, and Abdul Halim Zaim. "A Design of an Integrated Cloud-based Intrusion Detection System with Third Party Cloud Service." Open Computer Science 11, no. 1 (2021): 365–79. http://dx.doi.org/10.1515/comp-2020-0214.

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Abstract Although cloud computing is considered the most widespread technology nowadays, it still suffers from many challenges, especially related to its security. Due to the open and distributed nature of the cloud environment, this makes the cloud itself vulnerable to various attacks. In this paper, the design of a novel integrated Cloud-based Intrusion Detection System (CIDS) is proposed to immunise the cloud against any possible attacks. The proposed CIDS consists of five main modules to do the following actions: monitoring the network, capturing the traffic flows, extracting features, analyzing the flows, detecting intrusions, taking a reaction, and logging all activities. Furthermore an enhanced bagging ensemble system of three deep learning models is utilized to predict intrusions effectively. Moreover, a third-party Cloud-based Intrusion Detection System Service (CIDSS) is also exploited to control the proposed CIDS and provide the reporting service. Finally, it has been shown that the proposed approach overcomes all problems associated with attacks on the cloud raised in the literature.
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Chen, Rong. "Design and Protection Strategy of Distributed Intrusion Detection System in Big Data Environment." Computational Intelligence and Neuroscience 2022 (June 29, 2022): 1–7. http://dx.doi.org/10.1155/2022/4720169.

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One of the important research topics is protecting the host from threats by developing a reliable and accurate intrusion detection system. However, since the amount of data has grown fast due to the emergence of big data, the performance of traditional systems designed to identify breaches has suffered several flaws. One of them, for example, is known as single-point failure; low adaptability and a high false alarm rate are also typical. Hadoop is used to detect intrusions to tackle these difficulties. The Java system is used to create a framework with a significant data flow that detects intrusions when a distributed system is built. The proposed solution employs a distributed operating system for data collection, storage, and analysis. The results indicate that external distributed denial of service (DDoS) attacks are recognized quickly. The single-point failure issue is overcome, alleviating the bottleneck problem of data processing ability.
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35

Awajan, Albara. "A Novel Deep Learning-Based Intrusion Detection System for IoT Networks." Computers 12, no. 2 (2023): 34. http://dx.doi.org/10.3390/computers12020034.

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The impressive growth rate of the Internet of Things (IoT) has drawn the attention of cybercriminals more than ever. The growing number of cyber-attacks on IoT devices and intermediate communication media backs the claim. Attacks on IoT, if they remain undetected for an extended period, cause severe service interruption resulting in financial loss. It also imposes the threat of identity protection. Detecting intrusion on IoT devices in real-time is essential to make IoT-enabled services reliable, secure, and profitable. This paper presents a novel Deep Learning (DL)-based intrusion detection system for IoT devices. This intelligent system uses a four-layer deep Fully Connected (FC) network architecture to detect malicious traffic that may initiate attacks on connected IoT devices. The proposed system has been developed as a communication protocol-independent system to reduce deployment complexities. The proposed system demonstrates reliable performance for simulated and real intrusions during the experimental performance analysis. It detects the Blackhole, Distributed Denial of Service, Opportunistic Service, Sinkhole, and Workhole attacks with an average accuracy of 93.74%. The proposed intrusion detection system’s precision, recall, and F1-score are 93.71%, 93.82%, and 93.47%, respectively, on average. This innovative deep learning-based IDS maintains a 93.21% average detection rate which is satisfactory for improving the security of IoT networks.
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Kumavat, Kavita S., and Joanne Gomes. "Common Mechanism for Detecting Multiple DDoS Attacks." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 4 (2023): 81–90. http://dx.doi.org/10.17762/ijritcc.v11i4.6390.

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An important principle of an internet-based system is information security. Information security is a very important aspect of distributed systems and IoT (Internet of Things) based wireless systems. The attack which is more harmful to the distributed system and IoT-based wireless system is a DDoS (Distributed Denial of Service) attack since in this attack, an attacker can stop the work of all other connected devices or users to the network. For securing distributed applications, various intrusion detection mechanisms are used. But most existing mechanisms are only concentrated on one kind of DDoS attack. This paper focuses on the basic architecture of IoT systems and an overview of single intrusion detection systems. This paper presents a single detection method for different DDoS attacks on distributed systems with an IoT interface. In the future, the system will provide support for detecting and preventing different DDoS attacks in IoT-based systems.
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Amjad, Amjad, Nizar Alhafez, and Iyad Al Al-khayat. "An adaptive distributed intrusion detection system in local network: Hybrid classification methods." Journal of Intelligent Systems and Internet of Things 12, no. 1 (2024): 129–43. http://dx.doi.org/10.54216/jisiot.120110.

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In the realm of cybersecurity, the incessant evolution of network attacks necessitates advanced and robust intrusion detection systems (IDS). The major issues with these systems are numerous: false positivenegative alarms, delayed response and detection time, size of processed data, adaptability to future threats, scalability of the system, difficulty in detecting distributed attacks, and downtime (fault tolerance). We propose a system that introduces a distributed framework aimed at enhancing network security by effectively identifying subtle deviations from normal network behavior. This is achieved through transfer learning based on artificial neural networks, and support vector machine (SVM), capitalizing on their complementary strengths in recognizing complex patterns and addressing high-dimensional datasets. To validate the efficacy of the proposed approach, the NSL-KDD dataset is utilized within a distributed IDS architecture. It consists of several intrusion detection nodes representing subnetworks. A node consists of two agents that work collaboratively. A way is proposed to avoid interference between analysis agents: the network agents manager monitors the functioning of the nodes and displays the results of each vulnerability-detecting node in each subnet separately. Such communication between agents should reduce FPAS (false positive alarms) significantly. The Detection engine extracts relevant features of network attacks to solve the problem of SVM in processing huge sizes of data and detect adaptive future threats to detect famous distributed denial of services (DDOS) attacks in real-time. The system is highly scalable by increasing the number of intrusion detection system nodes if necessary. Central processing is avoided to circumvent a system failure situation, where processing and decision-making take place at the detection node level within each subnet.
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Zeng, Yiming, Jianwei Zhang, Yuzhong Zhong, Lin Deng, and Maoning Wang. "STNet: A Time-Frequency Analysis-Based Intrusion Detection Network for Distributed Optical Fiber Acoustic Sensing Systems." Sensors 24, no. 5 (2024): 1570. http://dx.doi.org/10.3390/s24051570.

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Distributed optical fiber acoustic sensing (DAS) is promising for long-distance intrusion-anomaly detection tasks. However, realistic settings suffer from high-intensity interference noise, compromising the detection performance of DAS systems. To address this issue, we propose STNet, an intrusion detection network based on the Stockwell transform (S-transform), for DAS systems, considering the advantages of the S-transform in terms of noise resistance and ability to detect disturbances. Specifically, the signal detected by a DAS system is divided into space–time data matrices using a sliding window. Subsequently, the S-transform extracts the time-frequency features channel by channel. The extracted features are combined into a multi-channel time-frequency feature matrix and presented to STNet. Finally, a non-maximum suppression algorithm (NMS), suitable for locating intrusions, is used for the post-processing of the detection results. To evaluate the effectiveness of the proposed method, experiments were conducted using a realistic high-speed railway environment with high-intensity noise. The experimental results validated the satisfactory performance of the proposed method. Thus, the proposed method offers an effective solution for achieving high intrusion detection rates and low false alarm rates in complex environments.
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39

Korani, Ravinder, and Dr P. Chandra Sekhar Reddy. "Anomaly based Intrusion Detection by Heuristics to Predict Intrusion Scope of IOT Network Transactions." International Journal of Engineering & Technology 7, no. 2.7 (2018): 797. http://dx.doi.org/10.14419/ijet.v7i2.7.10982.

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Conventional intrusion detection mechanisms face serious limitations in identifying heterogeneous and distributed type of intrusions over the IoT environment. This is due to inadequate resources and open deployment environment of IoT. Accordingly, ensuring data security and privacy are tough challenges in the practical context. This manuscript discusses various aspects of networking security and related challenges along with the concepts of system architecture. Further, endeavored to define a machine learning model that outlines two heuristics called Intrusion Scope Heuristic ( ), and benign scope heuristic ( ), which further uses in predictive analysis to identify the IOT network transaction is prone to intrusion or benign. The experimental study revealed the significance of the proposal with maximal detection accuracy, and minimal miss rate.
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40

AISHWARYA.S, CHITRA.M, MRS.M.VIVEKA, and PRIYA NIVETHA. "APPROACHES OF DATAMINING IN NETWORK INTRUSION DETECTION SYSTEM." International Journal of Advances in Engineering & Scientific Research 1, no. 5 (2014): 129–34. https://doi.org/10.5281/zenodo.10725167.

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<strong><em>ABSTRACT</em></strong> <em>Network security technology has become crucial in protecting government and industry computing infrastructure. Due to the network attacks over the past few years the intrusion detection system (IDS) is increasingly becoming a crucial component to secure network. In recent years, data mining - based intrusion detection systems (IDSs) have demonstrated high accuracy, good generalization to novel types of intrusion, and robust behaviour in a changing environment. . Instrumenting components such as model deployment, data transformations, and cooperative distributed detection remain a labour intensive and complex engineering endeavour. In data mining based intrusion detection system we should have thorough knowledge about the particular domain in relation to intrusion detection so as to efficiently extract relative rule from huge amounts of records. &nbsp;Modern intrusion detection applications face complex requirements - they need to be reliable, extensible, easy to manage, and have low maintenance cost. Still, significant challenges exist in design and implementation of production quality IDSs. This paper gives the classifier algorithm model for attack category and ensemble approach for detection.</em> <strong><em>KEYWORDS: </em></strong><em>&nbsp;Data mining, Ensemble approach, Network intrusion detection system, classifier, network security, Algorithm selection</em>
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41

Platonov, V. V., and P. O. Semenov. "An adaptive model of a distributed intrusion detection system." Automatic Control and Computer Sciences 51, no. 8 (2017): 894–98. http://dx.doi.org/10.3103/s0146411617080168.

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42

Renjit .J, Arokia, and Shunmuganathan K.L. "DISTRIBUTED ANOMALY INTRUSION DETECTION SYSTEM BASED ON MULTI-AGENTS." International Journal on Information Sciences and Computing 5, no. 1 (2011): 7–12. http://dx.doi.org/10.18000/ijisac.50084.

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43

Shan, Guohou, Boxin Zhao, James R. Clavin, Haibin Zhang, and Sisi Duan. "Poligraph: Intrusion-Tolerant and Distributed Fake News Detection System." IEEE Transactions on Information Forensics and Security 17 (2022): 28–41. http://dx.doi.org/10.1109/tifs.2021.3131026.

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44

Xin Li, Wenjie Wang, Jingchun Li, Xuefeng Zheng, and Shaojie Wang. "Improving The Communication Resiliency of Distributed Intrusion Detection System." Journal of Convergence Information Technology 7, no. 21 (2012): 550–58. http://dx.doi.org/10.4156/jcit.vol7.issue21.66.

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45

Wang, Jin. "An Autonomous Agent-Based Adaptive Distributed Intrusion Detection System." Journal of Computer Research and Development 42, no. 11 (2005): 1934. http://dx.doi.org/10.1360/crad20051116.

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46

Renjit, J. Arokia. "Distributed and cooperative multi-agent based intrusion detection system." Indian Journal of Science and Technology 3, no. 10 (2010): 1070–74. http://dx.doi.org/10.17485/ijst/2010/v3i10.2.

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Chenniappanadar, Santhosh Kumar, Sundharamurthy Gnanamurthy, Vinoth Kumar Sakthivelu, and Vishnu Kumar Kaliappan. "A Supervised Machine Learning Based Intrusion Detection Model for Detecting Cyber-Attacks Against Computer System." International Journal of Communication Networks and Information Security (IJCNIS) 14, no. 3 (2022): 16–25. http://dx.doi.org/10.17762/ijcnis.v14i3.5567.

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Internet usage has become essential for correspondence in almost every calling in our digital age. To protect a network, an effective intrusion detection system (IDS) is vital. Intrusion Detection System is a software application to detect network intrusion using various machine learning algorithms. The function of the expert has been lessened by machine learning approaches since knowledge is taken directly from the data. The fact that it makes use of all the features of an information packet spinning in the network for intrusion detection is weakened by the employment of various methods for detecting intrusions, such as statistical models, safe system approaches, etc. Machine learning has become a fundamental innovation for cyber security. Two of the key types of attacks that plague businesses, as proposed in this paper, are Denial of Service (DOS) and Distributed Denial of Service (DDOS) attacks. One of the most disastrous attacks on the Internet of Things (IOT) is a denial of service. Two diverse Machine Learning techniques are proposed in this research work, mainly Supervised learning. To achieve this goal, the paper represents a regression algorithm, which is usually used in data science and machine learning to forecast the future. An innovative approach to detecting is by using the Machine Learning algorithm by mining application-specific logs. Cyber security is a way of providing their customers the peace of mind they need knowing that they have secured their information and money.
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Cepheli, Özge, Saliha Büyükçorak, and Güneş Karabulut Kurt. "Hybrid Intrusion Detection System for DDoS Attacks." Journal of Electrical and Computer Engineering 2016 (2016): 1–8. http://dx.doi.org/10.1155/2016/1075648.

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Distributed denial-of-service (DDoS) attacks are one of the major threats and possibly the hardest security problem for today’s Internet. In this paper we propose a hybrid detection system, referred to as hybrid intrusion detection system (H-IDS), for detection of DDoS attacks. Our proposed detection system makes use of both anomaly-based and signature-based detection methods separately but in an integrated fashion and combines the outcomes of both detectors to enhance the overall detection accuracy. We apply two distinct datasets to our proposed system in order to test the detection performance of H-IDS and conclude that the proposed hybrid system gives better results than the systems based on nonhybrid detection.
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49

Levy Rocha, Savio, Fabio Lucio Lopes de Mendonca, Ricardo Staciarini Puttini, Rafael Rabelo Nunes, and Georges Daniel Amvame Nze. "DCIDS—Distributed Container IDS." Applied Sciences 13, no. 16 (2023): 9301. http://dx.doi.org/10.3390/app13169301.

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Intrusion Detection Systems (IDS) still prevail as an important line of defense in modern computing environments. Cloud environment characteristics such as resource sharing, extensive connectivity, and agility in deploying new applications pose security risks that are increasingly exploited. New technologies like container platforms require IDS to evolve to effectively detect intrusive activities in these environments, and advancements in this regard are still necessary. In this context, this work proposes a framework for implementing an IDS focused on container platforms using machine learning techniques for anomaly detection in system calls. We contribute with the ability to build a dataset of system calls and share it with the community; the generation of anomaly detection alerts in open-source applications to support the SOC through the analysis of these system calls; the possibility of implementing different machine learning algorithms and approaches to detect anomalies in system calls (such as frequency, sequence, and arguments among other type of data) aiming greater detection efficiency; and the ability to integrate the framework with other tools, improving collaborative security. A five-layer architecture was built using free tools and tested in a corporate environment emulated in the GNS3 software version 2.2.29. In an experiment conducted with a public system call dataset, it was possible to validate the operation and integration of the framework layers, achieving detection results superior to the work that originated the dataset.
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Jerzak, Marcin, and Mariusz Wojtysiak. "Distributed Intrusion Detection Systems – MetalDS case study." Computational Methods in Science and Technology Special Issue, no. 1 (2010): 133–45. http://dx.doi.org/10.12921/cmst.2010.si.01.135-145.

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