Towards the Development of a Cloud Computing Intrusion Detection Framework Using an Ensemble Hybrid Feature Selection Approach by Noah Oghenefego Ogwara, Krassie Petrova, Mee Loong Yang

“…The binary model achieved a classification accuracy of 99.55% with a very low false alarm rate of 0.45%. The classification accuracy of the multiclassification model was also high (98.92%). …”
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Abnormal Event Detection in Wireless Sensor Networks Based on Multiattribute Correlation by Mengdi Wang, Anrong Xue, Huanhuan Xia

“…Experimental results show that the proposed algorithm can reduce the impact of interference factors and the rate of the false alarm effectively; it can also improve the accuracy of event detection.…”
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Wear Prediction and Visual Decision Support of Drop Forged Rivetless Chain for Conveyors by Liu Manxian, Xu Zijia, Shen Xujia, Li Xiaodong, Zhang Zhijun, Yang Yi

“…The experiment results show that the wear life prediction model has high fitting accuracy, low false alarm rates, and low missed detection rates of wear early-warning, which can satisfy the requirements of wear life prediction. …”
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A Real-Valued Negative Selection Algorithm Based on Grid for Anomaly Detection by Ruirui Zhang, Tao Li, Xin Xiao

“…Theory analysis and experimental results demonstrate that GB-RNSA lowers the number of detectors, time complexity, and false alarm rate.…”
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Advanced Fuzzy-Logic-Based Traffic Incident Detection Algorithm by Changhong Zhu, Zhenjun Guo, Jie Ke

“…Further, the performance of this algorithm is evaluated using commonly used criteria such as mean time to detection (MTTD), false alarm rate (FAR), and detection rate (DR). Initially, an experiment is conducted for selecting the algorithm architecture that yields the optimal detection performance. …”
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Gender differences in the recognition of emotional faces: are men less efficient? by Ana Ruiz-Ibáñez, José T. Boyano

“…The results showed statistically significant differences in face corrected recognition (hit rate - false alarm rate): the women demonstrated a better recognition than the men. …”
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A multi-band spectrum sensing method based on sticky hidden Markov model by Zhongjie JIA, Ming JIN, Xiaoqun SONG

“…Existing multi-band spectrum sensing methods often use the sparsity of broadband spectrum.However, high spectrum occupancy rate of primary users degrades their performance severely.To address this issue, a novel multi-band spectrum sensing method was proposed by exploiting the state correlation among adjacent frequency bands.Firstly, a sticky hidden Markov model (SHMM) was established by regarding the multi-band states and measured energies as hidden and observed variables.In the SHMM, a sticky factor was introduced to represent the state correlation among adjacent frequency bands.Secondly, iterative expressions for the parameters of SHMM were derived.Finally, multi-band spectrum sensing was implemented by obtaining the posterior mean of observations from every frequency bands.Simulation results show that the proposed method outperforms existing methods, and when the false alarm probability is 0.1, the average frequency band occupancy rate is 50%, and the average signal-to-noise ratio is -12 dB, the detection probability can reach close to 0.99, which is about 30% higher than the detection probability of other methods.In addition, the proposed method had a faster convergence rate than existing methods and therefore has lower computational complexity.…”
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A Dynamic Intrusion Detection System Based on Multivariate Hotelling’s T2 Statistics Approach for Network Environments by Aneetha Avalappampatty Sivasamy, Bose Sundan

“…Performance of the model, as evaluated through validation and testing using KDD Cup’99 dataset, has shown very high detection rates for all classes with low false alarm rates. Accuracy of the model presented in this work, in comparison with the existing models, has been found to be much better.…”
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Blending Static and Dynamic Analysis for Web Application Vulnerability Detection: Methodology and Case Study by Paulo Nunes, Jose Fonseca, Marco Vieira

“…In this paper, we blend SA and DA to simultaneously improve the detection and decrease the false alarms. Our approach starts with SA to identify an initial set of potential vulnerabilities. …”
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Learning-Based Spectrum Sensing for Cognitive Radio Systems by Yasmin Hassan, Mohamed El-Tarhuni, Khaled Assaleh

“…Simulation results in terms of detection and false alarm probabilities of all proposed schemes are presented. …”
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An intelligent manufacturing system based on a recursive control structure by Bingyan Teng

“…Meanwhile, a negative selection algorithm is used to construct an antibody training system that considers perturbation problems.ResultThe results indicate that immune sensing nodes can effectively monitor manufacturing systems, reducing false alarm rates by over 4%. In the scheduling experiment, the completion time and equipment load improvement rate demonstrated by the research model were 3.29% and 12.38%, respectively. …”
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Improved TLBO-JAYA Algorithm for Subset Feature Selection and Parameter Optimisation in Intrusion Detection System by Mohammad Aljanabi, Mohd Arfian Ismail, Vitaly Mezhuyev

“…This condition is attributed to the increasing number of audit data features and the decreasing performance of human-based smart intrusion detection systems regarding classification accuracy, false alarm rate, and classification time. Feature selection and classifier parameter tuning are important factors that affect the performance of any intrusion detection system. …”
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Compressed Measurements Based Spectrum Sensing for Wideband Cognitive Radio Systems by Taha A. Khalaf, Mohammed Y. Abdelsadek, Mohammed Farrag

“…We also derive closed form expressions for the false alarm and detection probabilities. Numerical results show that the analytical expressions coincide with the exact probabilities obtained from simulations.…”
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Multi-objective optimization algorithms for intrusion detection in IoT networks: A systematic review by Shubhkirti Sharma, Vijay Kumar, Kamlesh Dutta

“…The Intrusion detection system faces a trade-off between various parameters such as detection accuracy, relevance, redundancy, false alarm rate, and other objectives. The paper presents a systematic review of intrusion detection in Internet of Things (IoT) networks using multi-objective optimization algorithms (MOA), to identify attempts at exploiting security vulnerabilities and reducing the chances of security attacks. …”
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A Secure Cooperative Spectrum Sensing Scheme in Mobile Cognitive Radio Networks by Jun Du, Daoxing Guo, Bangning Zhang, Ligang Shang

“…In comparison to the existing schemes, simulation results demonstrate that the proposed secure CSS scheme improves the primary user detection rate by 10% at false alarm rate of 0.1 when the mobility pattern of the SUs is different.…”
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Risk-taking propensity and its influence on lexical decision performance: a comparative study of high- and low-risk groups by Sangyub Kim, Joonwoo Kim, Solbin Lee, Kichun Nam

“…Following the classification of participants into high- and low-risk-taking propensity groups using the Balloon Analogue Risk Task, we assessed lexical decision-making with behavioral responses (RTs, ACC), signal detection measures (hit, false alarm, miss, correct rejection) and qualitative processing using lexical variable effects (number of syllables, first syllable frequency, stem frequency, word frequency) between the groups. …”
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Uncertainty-Aware Sensor Deployment Strategy in Mixed Wireless Sensor Networks by Yan Chang, Shukui Zhang, Yang Zhang, Jianxi Fan, Jin Wang

“…We consider the Gaussian white noise in the environment and present a centralized algorithm (FABGM for short) which discoveres vacancies by using detection model based on false alarm and moves the mobile nodes according to the method based on bipartite graph matching in this study. …”
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Edge Detection via Edge-Strength Estimation Using Fuzzy Reasoning and Optimal Threshold Selection Using Particle Swarm Optimization by Ajay Khunteta, D. Ghosh

“…This threshold is selected such that the overall probability of error in identifying edge pixels, that is, the sum of the probability of misdetection and the probability of false alarm, is minimum. This minimization is achieved via particle swarm optimization (PSO). …”
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Robust Wiener filter‐based time gating method for detection of shallowly buried objects by Ali Gharamohammadi, Fereidoon Behnia, Arash Shokouhmand, George Shaker

“…Abstract A robust method for ultra‐wideband (UWB) imaging of buried shallow objects based on time gating, Wiener filtering, as well as constant false alarm rate (CFAR) is proposed. Moreover, it is demonstrated that Wiener filtering can be used as a clutter removal tool in UWB signal applications. …”
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Multifeature Fusion Vehicle Detection Algorithm Based on Choquet Integral by Wenhui Li, Peixun Liu, Ying Wang, Hongyin Ni

“…Our method can not only improve the detection rate but also reduce the false alarm rate, which meets the engineering requirements of Advanced Driving Assistance Systems (ADAS).…”
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