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  1. 1
    The optimization of stepping stone detection: packet capturing steps

    The optimization of stepping stone detection: packet capturing steps by Omar, Mohd. Nizam, Maarof, Mond. Aizaini, Zainal, Anazida

    Published 2006
    “…This paper proposes an optimized packet capturing stone detection algorithm that can shorten the response time of overall response mechanism. …”
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  2. 2
    Wormhole attack detection mechanism in mobile ad hoc network using neighborhood information and path tracing algorithm

    Wormhole attack detection mechanism in mobile ad hoc network using neighborhood information and path tracing algorithm by Enshaei, Mehdi

    Published 2015
    “…Throughput and packet overhead is improved in the NIPT algorithm with an average of 4% and 6% respctively over the PT algorithm. …”
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  3. 3
    The optimization of stepping stone detection : packet capturing steps

    The optimization of stepping stone detection : packet capturing steps by Omar, Mohd. Nizam, Maarof, Mohd. Aizaini, Zainal, Anazida

    Published 2006
    “…This paper proposes an optimized packet capturing stone detection algorithm that can shorten the response time of overall response mechanism. …”
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  4. 4
    The optimization of stepping stone detection: Packet capturing steps

    The optimization of stepping stone detection: Packet capturing steps by Omar, Mohd Nizam, Maarof, Mohd Aizaini, Zainal, Anazida

    Published 2006
    “…This paper proposes an optimized packet capturing stone detection algorithm that can shorten the response time of overall response mechanism.The aim of the research is to improve the packet capturing step in stepping stone algorithm, thus, improve the response from overall detection and response system.The proposed method is to use small size of user buffer and kernel buffer.Experiments were conducted with two types of packet stream; i) 10 kbps and ii) 10 000 kbps data generated by Tfgen (packet generator) tools and nine combinations of different buffer sizes for each network packet stream were tested.Results from the experiment were analyzed. …”
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  5. 5
    Network instrusion prevention system ( NIPS) based on network intrusion detection system (NIDS) and ID3 algorithm decision tree classifier

    Network instrusion prevention system ( NIPS) based on network intrusion detection system (NIDS) and ID3 algorithm decision tree classifier by Syurahbil, A

    Published 2011
    “…Network security has gained significant attention in research and industrial communities.Due to the increasing threat of the network intrusion,firewalls have become important elements of the security policy.Firewall performance highly depends toward number of rules,because the large more rules the consequence makes downhill performance progressively.Firewall can be allow or deny access network packets incoming and outgoing into Local Area Network(LAN),but firewall can not detect intrusion.To distinguishing an intrusion network packet or normal is very difficult and takes a lot of time.An analyst must review all the network traffics previously.In this study,a new way to make the rules that can determine network packet is intrusion or normal automatically.These rules implemented into firewall as prevention,which if there is a network packet that match these rules then network packet will be dropped.This is called Network Intrusion Prevention System(NIPS).These rules are generated based on Network Intrusion Detection System(NIDS)and Iterative Dichotomiser 3 (ID3)Algorithm Decision Tree Classifier,which as data training is intrusion network packet and normal network packets from previous network traffics.The experiment is successful,which can generate the rules then implemented into a firewall and drop the intrusion network packet automatically.Moreover,this way can minimize number of rules in firewall.…”
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  6. 6
    Anomaly behavior detection using flexible packet filtering and support vector machine algorithms

    Anomaly behavior detection using flexible packet filtering and support vector machine algorithms by Abdul Wahid, Mohammed N.

    Published 2016
    “…FPF of SVM have merged the analyzed results of flexible packet filtering with support vector machine algorithm to get better classification of the captured network traffics and to detect anomalies. …”
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  7. 7
    An integrated anomaly intrusion detection scheme using statistical, hybridized classifiers and signature approach

    An integrated anomaly intrusion detection scheme using statistical, hybridized classifiers and signature approach by Mohamed Yassin, Warusia

    Published 2015
    “…This study aims to improve the detection of an anomalous behaviour by identifying the outlier data points in the packets more precisely, maximizes the detection of packets with similar behaviours more accurately while reducing the detection time. …”
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  8. 8
    Embedded Ethernet System For Real-Time Communication

    Embedded Ethernet System For Real-Time Communication by Thiagarajan, Durgesh

    Published 2018
    “…The Ethernet network model is used to analyze the performance of the backoff algorithm in a different number of nodes. The end delay of each packet, the number of packet collision and number of packet delivered are determined based on the analysis. …”
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  9. 9
    Location-Based Approach for Route Maintenance in Dynamic Source Routing Protocol

    Location-Based Approach for Route Maintenance in Dynamic Source Routing Protocol by Sjaugi, Muhammad Farhan

    Published 2008
    “…The objectives of this research are to propose new algorithm to detect route failure as early warning message to the protocol to take further action, and to propose new algorithm for DSR route maintenance to response the early warning message from route failure detection algorithm. …”
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  10. 10
    Void avoidance opportunistic routing density rank based for underwater sensor networks

    Void avoidance opportunistic routing density rank based for underwater sensor networks by Ismail, Nasarudin

    Published 2021
    “…In UWSNs, the energy consumption used is 125 times more during the forwarding of the packet data from source to destination as compare to during receiving data. …”
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  11. 11
    Non-invasive pathological voice classifications using linear and non-linear classifiers

    Non-invasive pathological voice classifications using linear and non-linear classifiers by Hariharan, Muthusamy

    Published 2010
    “…In the case of detection of specific disorders, wavelet packet and entropy features perform well compared to time-domain energy variations based features and MFCCs and SVD based features. …”
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  12. 12
    Pengoptimuman algoritma pengesanan batu loncatan dalam sistem pengesanan pencerobohan

    Pengoptimuman algoritma pengesanan batu loncatan dalam sistem pengesanan pencerobohan by Omar, Mohd. Nizam

    Published 2005
    “…Time gap is duration between detection and response. …”
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  13. 13
    Optimization of blood vessel detection in retina images using multithreading and native code for portable devices

    Optimization of blood vessel detection in retina images using multithreading and native code for portable devices by Tran, Duc Ngoc, Hussin, Fawnizu Azmadi, Yusoff, Mohd Zuki

    Published 2013
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  14. 14
    Performance evaluation of real-time multiprocessor scheduling algorithms

    Performance evaluation of real-time multiprocessor scheduling algorithms by Alhussian, H., Zakaria, N., Abdulkadir, S.J., Fageeri, S.O.

    Published 2016
    “…These results suggests that optimal algorithms may turn to be non-optimal when practically implemented, unlike USG which reveals far less scheduling overhead and hence could be practically implemented in real-world applications. …”
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  15. 15
    Network intrusion detection and alert system

    Network intrusion detection and alert system by To, Jin Yi

    Published 2024
    “…Signature-based detection compares network traffic packets with a real-time updated database of known attack patterns, while anomaly-based detection algorithms learn normal behavior patterns and identify deviations. …”
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  16. 16
    Development of a Reliable Multicast Protocol in Mobile Ad Hoc Networks

    Development of a Reliable Multicast Protocol in Mobile Ad Hoc Networks by Alahdal, Tariq A. A.

    Published 2008
    “…Our approaches achieve up to 2.19% improvement on average packet delivery ratio, 3.3% on requested packets, and 46% on recovery latency time without incurring any additional communication or intense computation. …”
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  17. 17
    A study of frame timing synchronization for WiMAX applications

    A study of frame timing synchronization for WiMAX applications by V., Jeoti, K.L., Ang, K.M., Yew

    Published 2007
    “…In this paper, we present a study of frame timing synchronization for WiMAX applications. The two synchronization algorithms analyzed are Schmidl and Cox technique and double sliding window packet detection. …”
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  18. 18
    Route Optimization System

    Route Optimization System by Zulkifli, Abdul Hayy

    Published 2005
    “…After much research into the many algorithms available, and considering some, including Genetic Algorithm (GA), the author selected Dijkstra's Algorithm (DA). …”
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  19. 19
    Novel Link Establishment Communication Scheme against Selfish Attack Using Node Reward with Trust Level Evaluation Algorithm in MANET

    Novel Link Establishment Communication Scheme against Selfish Attack Using Node Reward with Trust Level Evaluation Algorithm in MANET by Hemalatha, S., Kshirsagar, P.R., Manoharan, H., Vasantha Gowri, N., Vani, A., Qaiyum, S., Vijayakumar, P., Tirth, V., Haleem, S.L.A., Chakrabarti, P., Teressa, D.M.

    Published 2022
    “…The malicious nodes are available in routing path making the packet loss, in packet transmission for time instance. …”
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  20. 20
    Effective dimensionality reduction of payload-based anomaly detection in TMAD model for HTTP payload

    Effective dimensionality reduction of payload-based anomaly detection in TMAD model for HTTP payload by Kakavand, Mohsen, Mustapha, Norwati, Mustapha, Aida, Abdullah @ Selimun, Mohd Taufik

    Published 2016
    “…This trait causes slow instruction, assessment procedures, high resource consumption and poor detection rate. Due to their expensive computational requirements during both training and detection, IDSs are mostly ineffective for real-time anomaly detection. …”
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