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An Efficient Cyber Security and Intrusion Detection System Using CRSR with PXORP-ECC and LTH-CNN

Computers, Materials & Continua 2023年第8期76卷 2061-2078页

作者： nouf saeed alotaibi Department of Computer Science College of Science and Humanities Al DawadmiShaqra UniversityShaqra11961Saudi Arabia

Intrusion Detection System(IDS)is a network security mechanism that analyses all users’and applications’traffic and detectsmalicious activities in *** existing IDSmethods suffer fromlower accuracy and lack the required level of security to prevent sophisticated *** problem can result in the system being vulnerable to attacks,which can lead to the loss of sensitive data and potential system ***,this paper proposes an Intrusion Detection System using Logistic Tanh-based Convolutional Neural Network Classification(LTH-CNN).Here,the Correlation Coefficient based Mayfly Optimization(CC-MA)algorithm is used to extract the input characteristics for the IDS from the input ***,the optimized features are utilized by the LTH-CNN,which returns the attacked and non-attacked *** that,the attacked data is stored in the log file and non-attacked data is mapped to the cyber security and data security *** prevent the system from cyber-attack,the Source and Destination IP address is converted into a complex binary format named 1’s Complement Reverse Shift Right(CRSR),where,in the data security phase the sensed data is converted into an encrypted format using Senders Public key Exclusive OR Receivers Public Key-Elliptic Curve Cryptography(PXORP-ECC)Algorithm to improve the data *** Security Laboratory-Knowledge Discovery inDatabases(NSLKDD)dataset and real-time sensor are used to train and evaluate the proposed *** suggested model is evaluated based on accuracy,sensitivity,and specificity,which outperformed the existing IDS methods,according to the results of the experiments.

关键词： Intrusion detection system logistic tanh-based convolutional neural network classification(LTH-CNN) correlation coefficient based mayfly optimization(CC-MA) cyber security

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Can Ginger (<i>Zingiber officinale</i>) Aqueous Crude Extract Induce Apoptotic Pathways in Drug-Resistance Acute Myeloid Leukemia: <i>In Vitro</i>Study?

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Advances in Biological Chemistry 2019年第3期9卷 99-109页

作者： nouf saeed Al-Abbas Umm Al-Qura University Mecca Mecca Province KSA

While chemotherapy remains to be one of the main using approaches in the clinical treatment of acute myeloid leukemia (AML), multidrug resistance (MDR) is considered the major obstacle that limits the therapeutic efficacy. Nowadays, Herbal therapy as an adjuvant therapy has been used for many health problems. Ginger (Zingiber officinale) is considering one of the promising herbal spices showing high therapeutic and preventive effects against many disorders specially cancer. In the current work, we focused on the role of ginger crude extract in fighting drug resistant AML. MTT assay showed a significant decrease in cell viability and clear cytotoxic effect on HL60/ADR and HL60 cell under the high concentrations (100 and 1000 μg/Ml) of ginger extract. The flow cytometry results showed a significant apoptotic cell death by ginger in HL60 and ADR/Hl60 and also confirmed by immunostaining of nucleus by DAPI which showed apoptotic nuclei. Our data clearly declared that the high concentration of ginger extract is promising anticancer drug by induction of apoptotic cell death in HL60/ADR cells especially in drug resistant AML.

关键词： Acute Myeloid Leukemia Drug Resistance Ginger

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Machine Learning Based Cybersecurity Threat Detection for Secure IoT Assisted Cloud Environment

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Computer Systems Science & Engineering 2023年第10期47卷 855-871页

作者： Z.Faizal Khan saeed M.Alshahrani Abdulrahman Alghamdi Someah Alangari nouf Ibrahim Altamami Khalid A.Alissa Sana Alazwari Mesfer Al Duhayyim Fahd N.Al-Wesabi Department of Computer Engineering College of Computing and Information TechnologyShaqra UniversityShaqraSaudi Arabia Department of Computer Science College of Computing and Information TechnologyShaqra UniversityShaqraSaudi Arabia Department of Computer Science College of Science and Humanities DawadmiShaqra UniversityShaqraSaudi Arabia Department of Mathematics College of EducationShaqra UniversityShaqraSaudi Arabia Saudi Aramco Cybersecurity Chair Networks and Communications DepartmentCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif P.O.Box 11099Taif21944Saudi Arabia Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversityAlaflaj16828Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia

The Internet of Things(IoT)is determine enormous economic openings for industries and allow stimulating innovation which obtain between domains in childcare for eldercare,in health service to energy,and in developed to *** develops a difficult problem in IoT platform whereas the presence of cyber-attack requires that *** progress of automatic devices for cyber-attack classifier and detection employing Artificial Intelligence(AI)andMachine Learning(ML)devices are crucial fact to realize security in IoT *** can be required for minimizing the issues of security based on IoT devices ***,this research proposal establishes novel mayfly optimized with Regularized Extreme Learning Machine technique called as MFO-RELM model for Cybersecurity Threat classification and detection fromthe cloud and IoT *** proposed MFORELM model provides the effective detection of cybersecurity threat which occur in the cloud and IoT *** accomplish this,the MFO-RELM technique pre-processed the actual cloud and IoT data as to meaningful ***,the proposed models will receive the pre-processing data and carry out the classifier *** boosting the efficiency of the proposed models,theMFOtechnique was utilized to *** experiential outcome of the proposed technique was tested utilizing the standard CICIDS 2017 dataset,and the outcomes are examined under distinct aspects.

关键词： Mayfly optimization machine learning artificial intelligence cybersecurity threat detection

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