• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.6
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• pp.2895-2921
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• 2018

Security has become one of the major concerns in mobile adhoc networks (MANETs). Data and voice communication amongst roaming battlefield entities (such as platoon of soldiers, inter-battlefield tanks and military aircrafts) served by MANETs throw several challenges. It requires complex securing strategy to address threats such as unauthorized network access, man in the middle attacks, denial of service etc., to provide highly reliable communication amongst the nodes. Intrusion Detection and Prevention System (IDPS) undoubtedly is a crucial ingredient to address these threats. IDPS in MANET is managed by Command Control Communication and Intelligence (C3I) system. It consists of networked computers in the tactical battle area that facilitates comprehensive situation awareness by the commanders for timely and optimum decision-making. Key issue in such IDPS mechanism is lack of Smart Learning Engine. We propose a novel behavioral based "Smart Multi-Instance Multi-Label Intrusion Detection and Prevention System (MIML-IDPS)" that follows a distributed and centralized architecture to support a Robust C3I System. This protocol is deployed in a virtually clustered non-uniform network topology with dynamic election of several virtual head nodes acting as a client Intrusion Detection agent connected to a centralized server IDPS located at Command and Control Center. Distributed virtual client nodes serve as the intelligent decision processing unit and centralized IDPS server act as a Smart MIML decision making unit. Simulation and experimental analysis shows the proposed protocol exhibits computational intelligence with counter attacks, efficient memory utilization, classification accuracy and decision convergence in securing C3I System in a Tactical Battlefield environment.

• Journal of Electrical Engineering and Technology
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• v.13 no.5
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• pp.2086-2098
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• 2018

Security plays a vital role and is the key challenge in Mobile Ad-hoc Networks (MANET). Infrastructure-less nature of MANET makes it arduous to envisage the genre of topology. Due to its inexhaustible access, information disseminated by roaming nodes to other nodes is susceptible to many hazardous attacks. Intrusion Detection and Prevention System (IDPS) is undoubtedly a defense structure to address threats in MANET. Many IDPS methods have been developed to ascertain the exceptional behavior in these networks. Key issue in such IDPS is lack of fast self-organized learning engine that facilitates comprehensive situation awareness for optimum decision making. Proposed "Intelligent Behavioral Hybridized Intrusion Detection and Prevention System (IBH_IDPS)" is built with computational intelligence to detect complex multistage attacks making the system robust and reliable. The System comprises of an Intelligent Client Agent and a Smart Server empowered with fuzzy inference rule-based service engine to ensure confidentiality and integrity of network. Distributed Intelligent Client Agents incorporated with centralized Smart Server makes it capable of analyzing and categorizing unethical incidents appropriately through unsupervised learning mechanism. Experimental analysis proves the proposed model is highly attack resistant, reliable and secure on devices and shows promising gains with assured delivery ratio, low end-to-end delay compared to existing approach.

• International Journal of Computer Science & Network Security
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• v.22 no.12
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• pp.1-12
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• 2022

Besides unexpected growth perceived by IoT's, the variety and volume of threats have increased tremendously, making it a necessity to introduce intrusion detections systems for prevention and detection of such threats. But Intrusion Detection and Prevention System (IDPS) inside the IoT network yet introduces some unique challenges due to their unique characteristics, such as privacy inference, performance, and detection rate and their frequency in the dynamic networks. Our research is focused on the privacy inferences of existing intrusion prevention and detection system approaches. We also tackle the problem of providing unified a solution to implement the open-source IDPS in the IoT architecture for assessing the performance of IDS by calculating; usage consumption and detection rate. The proposed scheme is considered to help implement the human health monitoring system in IoT networks

• Review of KIISC
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• v.5 no.2
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• pp.32-48
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• 1995

Our paper describes an Intrusion Detection Parallel System(IDPS) which detects an anomaly activity corresponding to the actions that interaction between near detection events. IDES uses parallel inductive approaches regarding the problem of real-time anomaly behavior detection on rule-based system. This approach uses sequential rule that describes user's behavior and characteristics dependent on time. and that audits user's activities by using rule base as data base to store user's behavior pattern. When user's activity deviates significantly from expected behavior described in rule base. anomaly behaviors are recorded. Observed behavior is flagged as a potential intrusion if it deviates significantly from the expected behavior or if it triggers a rule in the parallel inductive system.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.04a
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• pp.315-318
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• 2017

Nowadays, Distributed Denial of Service (DDoS) attacks have gained increasing popularity and have been a major factor in a number of massive cyber-attacks. It could easily exhaust the computing and communicating resources of a victim within a short period of time. Therefore, we have to find the method to detect and prevent the DDoS attack. Recently, there have been some researches that provide the methods to resolve above problem, but it still gets some limitations such as low performance of detecting and preventing, scope of method, most of them just use on cloud server instead of network, and the reliability in the network. In this paper, we propose solutions for (1) handling multiple DDoS attacks from multiple IP address and (2) handling the suspicious attacks in the network. For the first solution, we assume that there are multiple attacks from many sources at a times, it should be handled to avoid the conflict when we setup the preventing rule to switches. In the other, there are many attacks traffic with the low volume and same destination address. Although the traffic at each node is not much, the traffic at the destination is much more. So it is hard to detect that suspicious traffic with the sampling based method at each node, our method reroute the traffic to another server and make the analysis to check it deeply.