Search Results - (( based optimization method algorithm ) OR ( attack detection device algorithm ))

A Cryptojacking Detection System With Product Moment Correlation Coefficient (Pmcc) Heatmap Intelligent by Kong, Jun Hao

“…This study aims to develop a cryptojacking detection system based to the random forest algorithm.…”
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Industrial datasets with ICS testbed and attack detection using machine learning techniques by Mubarak, Sinil, Habaebi, Mohamed Hadi, Islam, Md Rafiqul, Balla, Asaad, Tahir, Mohammad, Elsheikh, Elfatih A A, Suliman, F M

“…The performance metrics such as accuracy, precision, recall, F1 score are evaluated and cross validated for different ML algorithms for anomaly detection. The decision tree (DT) ML technique is optimized with pruning method which provides an attack detection accuracy of 96.5%. …”
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Ethereum blockchain-based three factor authentication and multi-contract access control for secure smart home environment in 5G networks by Atiewi S., Al-Rahayfeh A., Almiani M., Abuhussein A., Yussof S.

“…To increase security, we perform secure authentication by creating an effective methodology based on the Touch Well before Use (TWU) method which protects against attacks. …”

Implement edge pruning to enhance attack graph generation using the naïve approach algorithm by Al-Araji, Zaid J., Mutlag, Ammar Awad, Syed Ahmad, Sharifah Sakinah

“…The next generation cyber defence concentrating on predictive analysis seems more proactive than existing technologies based on intrusion detection. Recently, many approaches have been proposed to detect and predict attacks; one of these approaches is attack graphs. …”
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Android malware detection using PMCC heatmap and Fuzzy Unordered Rule Induction Algorithm (FURIA) by Nur Khairani, Kamarudin, Ahmad Firdaus, Zainal Abidin, Azlee, Zabidi, Ferda, Ernawan, Syifak, Izhar Hisham, Mohd Faizal, Ab Razak

“…Many security practitioners have adopted different approaches to detect malware. However, its attacks continuously evolve and spread, and the number of attacks is still increasing. …”
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Attack prediction to enhance attack path discovery using improved attack graph by Al-Araji, Zaid Jasim, Abdullah, Raihana Syahirah, Syed Ahmad, Sharifah Sakinah

“…For the attack graph analysis, the random forest algorithm was used to detect, predict, and dynamically ascertain the attack location in the network. …”
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Attack graph construction for enhancing intrusion prediction based on vulnerabilities metrics by Al-Araji, Zaid Jasim Mohammed

“…This study employs use Random Forest algorithm to identify and forecast attacks to dynamically locate the attack location in the network for attack graph analysis. …”
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Bio-inspired for Features Optimization and Malware Detection by Mohd Faizal, Ab Razak, Nor Badrul, Anuar, Fazidah, Othman, Ahmad, Firdaus, Firdaus, Afifi, Rosli, Salleh

“…Therefore, an effective Android malware detection system is necessary. However, detecting the attack is challenging due to the similarity of the permissions in malware with those seen in benign applications. …”
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Bio-inspired for Features Optimization and Malware Detection by Razak, Mohd Faizal Ab, Anuar, Nor Badrul, Othman, Fazidah, Firdaus, Ahmad, Afifi, Firdaus, Salleh, Rosli

“…Therefore, an effective Android malware detection system is necessary. However, detecting the attack is challenging due to the similarity of the permissions in malware with those seen in benign applications. …”
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Detection of DDoS attacks in IoT networks using machine learning algorithms by Abdulrahman Alwan, Alwan Ahmed, Shah, Asadullah, Abdulrahman Alwan, Alwan Abdullah, Laghari, Shams Ul Arfeen

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The need for an enhanced IoT-based malware detection model using artificial intelligence (AI) algorithm: a review by Maidin, Siti Sarah, Yahya, Norzariyah

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Hybrid intelligent approach for network intrusion detection by Al-Mohammed, Wael Hasan Ali

“…Hence, there must be substantial improvement in network intrusion detection techniques and systems. Due to the prevailing limitations of finding novel attacks, high false detection, and accuracy in previous intrusion detection approaches, this study has proposed a hybrid intelligent approach for network intrusion detection based on k-means clustering algorithm and support vector machine classification algorithm. …”
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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

“…However, the insecure nature of IoT devices has led to several difficulties, one of which is distributed denial-of-service (DDoS) attacks. …”
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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

“…However, the insecure nature of IoT devices has led to several difficulties, one of which is distributed denial-of-service (DDoS) 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

“…However, the insecure nature of IoT devices has led to several difficulties, one of which is distributed denial-of-service (DDoS) attacks. …”
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An enhanced android botnet detection approach using feature refinement by Anwar, Shahid

“…In order to detect botnet attacks which causes immense chaos and problems to smartphones, first the Android botnet need to be analysed. …”
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Crytojacking Classification based on Machine Learning Algorithm by Mansor W.N.A.B.W., Ahmad A., Zainudin W.S., Kama M.N., Madihah Mohd Saudi

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

“…However, the insecure nature of IoT devices has led to several difficulties, one of which is distributed denial-of-service (DDoS) attacks. …”
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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

“…However, the insecure nature of IoT devices has led to several difficulties, one of which is distributed denial-of-service (DDoS) attacks. …”
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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

“…However, the insecure nature of IoT devices has led to several difficulties, one of which is distributed denial-of-service (DDoS) attacks. …”
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