Search Results - (( parameter evaluation method algorithm ) OR ( attack detection sensor algorithm ))

Modelling of intelligent intrusion detection system: making a case for snort by Olanrewaju, Rashidah Funke, Ku zahir, Ku Nor Afiza, Asnawi, Ani Liza, Sanni, Mistura Laide, Ahmed, Abdulkadir Adekunle

“…The performance of this classifier was evaluated based on three parameters: accuracy, sensitivity, and False Positive Rate (FPR). …”
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A new sinkhole attack detection algorithm for RPL in wireless sensor networks (WSN) by Ali, Mohammed Qasim

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Detecting sybil attacks in clustered wireless sensor networks based on energy trust system (ETS) by Alsaedi, Noor, Hashim, Fazirulhisyam, Sali, Aduwati, Rokhani, Fakhrul Zaman

“…Then, a trust algorithm is applied based on the energy of each sensor node. …”
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A novel energy-efficient sybil node detection algorithm for intrusion detection system in wireless sensor networks by Karuppiah, A. Babu, Dalfiah, J., Yuvashri, K., Rajaram, S., Pathan, Al-Sakib Khan

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Presentation attack detection for face recognition on smartphones: a comprehensive review by Abdul Ghaffar, Idris, Haji Mohd, Mohd Norzali

“…Face Presentation Attack Detection through the sensor level technique involved in using additional hardware or sensor to protect recognition system from spoofing while feature level techniques are purely software-based algorithms and analysis. …”
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Cooperative multi agents for intelligent intrusion detection and prevention systems / Shahaboddin Shamshirband by Shamshirband, Shahaboddin

“…This thesis evaluates the proposed solution using flooding attacks in wireless sensor networks (i.e. a type of DDoS attack). …”
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Algorithm enhancement for host-based intrusion detection system using discriminant analysis by Dahlan, Dahliyusmanto

“…Misuse detection algorithms model know attack behavior. They compare sensor data to attack patterns learned from the training data. …”
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A study on advanced statistical analysis for network anomaly detection by Ngadi, Md. Asri, Idris, Mohd. Yazid, Abdullah, Abd. Hanan

“…Misuse detection algorithms model know attack behavior. They compare sensor data to attack patterns learned from the training data. …”
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Energy-efficient intrusion detection in wireless sensor network by Salehian, Solmaz, Masoumiyan, Farzaneh, Udzir, Nur Izura

“…Attacks can occur from any direction and any node in WSNs, so one crucial security challenge is to detect networks' intrusion. …”
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ETERS: A comprehensive energy aware trust-based efficient routing scheme for adversarial WSNs by Khan, T., Singh, K., Hasan, M.H., Ahmad, K., Reddy, G.T., Mohan, S., Ahmadian, A.

“…However, a trust-based attack detection algorithm (TADA) assesses the reliability of SNs to detect internal attacks. …”
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Cyber attacks analysis and mitigation with machine learning techniques in ICS SCADA systems by Mubarak, Sinil, Habaebi, Mohamed Hadi, Abdul Rahman, Farah Diyana, Khan, Sheroz, Islam, Md Rafiqul

“…Mitigation techniques such as honeypot simulation which helps in vulnerability assessment, along with machine learning algorithms, suitable for intrusion detection and prevention of cyber-attacks in SCADA systems has been detailed.…”
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Secure and Energy-Efficient Data Aggregation Method Based on an Access Control Model by Jasim, Ahmed Abdulhadi, Idris, Mohd Yamani Idna, Azzuhri, Saaidal Razalli, Issa, Noor Riyadh, Noor, Noorzaily Mohamed, Kakarla, Jagadeesh, Amiri, Iraj Sadegh

“…The secure node authentication algorithm prevents attacks from accessing the network. …”
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Machine learning-based anomaly detection in NFV: a comprehensive survey by Sehar Zehra, Ummay Faseeha, Hassan Jamil Syed, Fahad Samad, Ashraf Osman Ibrahim Elsayed, Anas W. Abulfaraj, Wamda Nagmeldin

“…It proposes the utilization of anomaly detection techniques as a means to mitigate the potential risks of cyber attacks. …”
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Deep learning in distributed denial-ofservice attacks detection method for Internet of Things networks by Mohammed Aswad, Firas, Saleh Ahmed,, Ali Mohammed, Ali Majeed Alhammadi, Nafea, Ahmad Khalaf, Bashar, A. Mostafa, Salama

“…The RNN, CNN, LSTM, and CNN-BiLSTM are implemented and tested to determine the most effective model against DDoS attacks that can accurately detect and distinguish DDoS from legitimate traffic. …”
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Deep learning in distributed denial-ofservice attacks detection method for Internet of Things networks by Mohammed Aswad, Firas, Ahmed, Ali Mohammed Saleh, Majeed Alhammadi, Nafea Ali, Ahmad Khalaf, Bashar, A. Mostafa, Salama

“…The RNN, CNN, LSTM, and CNN-BiLSTM are implemented and tested to determine the most effective model against DDoS attacks that can accurately detect and distinguish DDoS from legitimate traffic. …”
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Deep learning in distributed denial-ofservice attacks detection method for Internet of Things networks by Mohammed Aswad, Firas, Saleh Ahmed, Ali Mohammed, Ali Majeed Alhammadi, Nafea, Ahmad Khalaf, Bashar, A. Mostafa, Salama

“…The RNN, CNN, LSTM, and CNN-BiLSTM are implemented and tested to determine the most effective model against DDoS attacks that can accurately detect and distinguish DDoS from legitimate traffic. …”
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Anomaly detection in ICS datasets with machine learning algorithms by Mubarak, Sinil, Habaebi, Mohamed Hadi, Islam, Md Rafiqul, Abdul Rahman, Farah Diyana, Tahir, Mohammad

“…The ICS cyber threats are growing at an alarming rate on industrial automation applications. Detection techniques with machine learning algorithms on public datasets, suitable for intrusion detection of cyber-attacks in SCADA systems, as the first line of defense, have been detailed. …”
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Back Propagation Algorithm-Based Intelligent Model for Botnet Detection by Nik Quosthoni, Sunaidi, Ahmed, Abdulghani Ali

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Deep learning in distributed denial-ofservice attacks detection method for Internet of Things networks by Firas Mohammed Aswad, Firas Mohammed Aswad, Ali Mohammed Saleh Ahmed, Ali Mohammed Saleh Ahmed, Nafea Ali Majeed Alhammadi, Nafea Ali Majeed Alhammadi, Bashar Ahmad Khalaf, Bashar Ahmad Khalaf, Salama A. Mostafa, Salama A. Mostafa

“…The RNN, CNN, LSTM, and CNN-BiLSTM are implemented and tested to determine the most effective model against DDoS attacks that can accurately detect and distinguish DDoS from legitimate traffic. …”
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Deep learning in distributed denial-ofservice attacks detection method for Internet of Things networks by Mohammed Aswad, Firas, Ahmed, Ali Mohammed Saleh, Ali Majeed Alhammadi, Nafea, Bashar Ahmad Khalaf, Bashar Ahmad Khalaf, Salama A. Mostafa, Salama A. Mostafa

“…The RNN, CNN, LSTM, and CNN-BiLSTM are implemented and tested to determine the most effective model against DDoS attacks that can accurately detect and distinguish DDoS from legitimate traffic. …”
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