• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.12 no.6
• /
• pp.2895-2921
• /
• 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 the Korea Society of Computer and Information
• /
• v.10 no.6 s.38
• /
• pp.9-16
• /
• 2005

Recently, since the number of internet users is increasing rapidly and, by using the Public hacking tools, general network users can intrude computer systems easily, the hacking problem is setting more serious. In order to prevent the intrusion. it is needed to detect the sign in advance of intrusion in a Positive Prevention by detecting the various forms of hackers intrusion trials to know the vulnerability of systems. The existing network-based anomaly detection algorithms that cope with port-scanning and the network vulnerability scans have some weakness in intrusion detection. they can not detect slow scans and coordinated scans. therefore, the new concept of algorithm is needed to detect effectively the various. In this Paper, we propose a detection algorithm for session patterns and FCM.

• Korean Journal of Cognitive Science
• /
• v.18 no.2
• /
• pp.139-157
• /
• 2007

We compared brain activation areas during participants drawn from contrasting two college majors performed intentionality detection (known as the basic mechanism of theory of mind) task using fMRI. The main purpose of this study was to identify whether individual differences are present in intentionality detection or not. In psychology major, the left inferior frontal gyrus, the fusiform gyrus, the superior temporal gyrus and the right fusiform gyrus, the supramarginal gyrus were activated. In engineering major, the inferior parietal lobule and the superior parietal lobule were found. This result suggests that according to participants' major, different brain areas were activated. The relations between performance of the intentionality detection task and the individual variants of participants were discussed.

• Convergence Security Journal
• /
• v.7 no.1
• /
• pp.19-27
• /
• 2007

Detecting unknown threats is a paradox ; how do you detect a threat if it is not known to exist? The answer is that unknown threat detection is the process of making a previously unknown threat identifiable in the shortest possible time frame. This paper examines the possibility of creating an unknown threat detection mechanism that security experts can use for developing a flexible protection system for networks. A system that allows the detection of unknown threats through monitoring system and the incorporation of dynamic and flexible logics with situational knowledge is described as well as the mechanisms used to develop such a system is illustrated. The system not only allows the detection of new threats but does so in a fast and efficient manner to increase the available time for responding to these threats.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.14 no.1
• /
• pp.203-209
• /
• 2014

Today, Distributed denial of service (DDoS) attack present a very serious threat to the stability of the internet. The DDoS attack, which is consuming all of the computing or communication resources necessary for the service, is known very difficult to protect. The DDoS attack usually transmits heavy traffic data to networks or servers and they cannot handle the normal service requests because of running out of resources. It is very hard to prevent the DDoS attack. Therefore, an intrusion detection system on large network is need to efficient real-time detection. In this paper, we propose the detection mechanism using analysis of flow information against DDoS attacks in order to guarantee the transmission of normal traffic and prevent the flood of abnormal traffic. The OPNET simulation results show that our ideas can provide enough services in DDoS attack.

• Journal of Broadcast Engineering
• /
• v.26 no.1
• /
• pp.14-25
• /
• 2021

Change detection, which finds changes in remote sensing images of the same location captured at different times, is very important because it is used in various applications. However, registration errors, building displacement errors, and shadow errors cause false positives. To solve these problems, we propose a novle deep convolutional network called CADNet (Change Attention Dense Siamese Network). CADNet uses FPN (Feature Pyramid Network) to detect multi-scale changes, applies a Change Attention Module that attends to the changes, and uses DenseNet as a feature extractor to use feature maps that contain both low-level and high-level features for change detection. CADNet performance measured from the Precision, Recall, F1 side is 98.44%, 98.47%, 98.46% for WHU datasets and 90.72%, 91.89%, 91.30% for LEVIR-CD datasets. The results of this experiment show that CADNet can offer better performance than any other traditional change detection method.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.26 no.3
• /
• pp.389-395
• /
• 2022

Because of improvement of deep learning techniques, deep learning using computer vision such as classification, detection and segmentation has also been used widely at many fields. Expecially, automatic driving is one of the major fields that applies computer vision systems. Also there are a lot of works and researches to combine multiple tasks in a single network. In this study, we propose the network that predicts the individual depth of pedestrians and vehicles. Proposed model is constructed based on YOLOv3 for object detection and Monodepth for depth estimation, and it process object detection and depth estimation consequently using encoder and decoder based on hard parameter sharing. We also used attention module to improve the accuracy of both object detection and depth estimation. Depth is predicted with monocular image, and is trained using self-supervised training method.

• Nuclear Engineering and Technology
• /
• v.56 no.3
• /
• pp.893-899
• /
• 2024

In a secondary cooling system of a sodium-cooled fast reactor (SFR), rapid detection of hydrogen due to sodium-water reaction (SWR) caused by water leakage from a heat exchanger tube of a steam generator (SG) is important in terms of safety and property protection of the SFR. For hydrogen detection, the hydrogen detectors using atomic transmission phenomenon of hydrogen within Ni-membrane were used in Japanese proto-type SFR "Monju". However, during the plant operation, detection signals of water leakage were observed even in the situation without SWR concerning temperature up and down in the cooling system. For this reason, the study of a new hydrogen detector has been carried out to improve stability, accuracy and reliability. In this research, the authors focus on the difference in composition of hydrogen and the difference between the background hydrogen under normal plant operation and the one generated by SWR and theoretically estimate the hydrogen behavior in liquid sodium by using ultra-accelerated quantum chemical molecular dynamics (UA-QCMD). Based on the estimation, dissolved H or NaH, rather than molecular hydrogen (H₂), is the predominant form of the background hydrogen in liquid sodium in terms of energetical stability. On the other hand, it was found that hydrogen molecules produced by the sodium-water reaction can exist stably as a form of a fine bubble concerning some confinement mechanism such as a NaH layer on their surface. At the same time, we observed experimentally that the fine H₂ bubbles exist stably in the liquid sodium, longer than previously expected. This paper describes the comparison between the theoretical estimation and experimental results based on hydrogen form in sodium in the development of the new hydrogen detector in Japan.

• Archives of Pharmacal Research
• /
• v.7 no.1
• /
• pp.65-68
• /
• 1984

Mechanism of the monoamine oxidase inhibition by tranylcypromine was studied in relation to its metabolism to reactive apecies. A metabolic study performed to collect general biotransformation pathway in rats provided GC/MS evidence for the detection of two new metabolites, N-acetyl and hydroxylated N-acetyltranylacypromine.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 2003.05a
• /
• pp.417-424
• /
• 2003

In this paper, we introduce and analyze a feedback control model of TCP/AQM dynamics. Then, we propose the Pro-active Queue Management (PAQM) mechanism, which can provide proactive congestion avoidance and control using an adaptive congestion indicator and a control function for wide range of traffic environments. The PAQM stabilizes the queue length around a desired level while giving smooth and low packet loss rates independent of the traffic load level under a wide range of traffic environment. The PAQM outperforms other AQM algorithms such as Random Early Detection (RED) [1] and PI-controller [2]