There has been an exponential increase in discussions about bias in Artificial Intelligence (AI) systems. Bias in AI has typically been defined as a divergence from standard statistical patterns in the output of an AI...
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Recommendation systems are crucial due to their high relevance in terms of interpretability and performance. A Social Recommendation system explores how social relations influence user choices and how users select ite...
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This study presents a robust framework that leverages advanced deep-learning techniques for ear-based human recognition. Faced with the challenge of dataset sizes, our approach is developed based on a generative adver...
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Plants affect economy, agriculture industry and climate of any country. Therefore it is very important to take care of plants. Like human being, plants are also infected by various disease that are resulted from virus...
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Social media platforms like Twitter are popular for sharing opinions and ideas, but hate speech is a growing concern. Hate speech can harm individuals and communities, leading to discrimination and violence. Detecting...
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The growing sophistication of cyberthreats,among others the Distributed Denial of Service attacks,has exposed limitations in traditional rule-based Security Information and Event Management *** machine learning–based...
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The growing sophistication of cyberthreats,among others the Distributed Denial of Service attacks,has exposed limitations in traditional rule-based Security Information and Event Management *** machine learning–based intrusion detection systems can capture complex network behaviours,their“black-box”nature often limits trust and actionable insight for security *** study introduces a novel approach that integrates Explainable Artificial Intelligence—xAI—with the Random Forest classifier to derive human-interpretable rules,thereby enhancing the detection of Distributed Denial of Service(DDoS)*** proposed framework combines traditional static rule formulation with advanced xAI techniques—SHapley Additive exPlanations and Scoped Rules-to extract decision criteria from a fully trained *** methodology was validated on two benchmark datasets,CICIDS2017 and *** rules were evaluated against conventional Security Information and Event Management Systems rules with metrics such as precision,recall,accuracy,balanced accuracy,and Matthews Correlation *** results demonstrate that xAI-derived rules consistently outperform traditional static ***,the most refined xAI-generated rule achieved near-perfect performance with significantly improved detection of DDoS traffic while maintaining high accuracy in classifying benign traffic across both datasets.
File entropy is one of the major indicators of crypto-ransomware because the encryption by ransomware increases the randomness of file ***,entropy-based ransomware detection has certain limitations;for example,when di...
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File entropy is one of the major indicators of crypto-ransomware because the encryption by ransomware increases the randomness of file ***,entropy-based ransomware detection has certain limitations;for example,when distinguishing ransomware-encrypted files from normal files with inherently high-level entropy,misclassification is very *** addition,the entropy evaluation cost for an entire file renders entropy-based detection impractical for large *** this paper,we propose two indicators based on byte frequency for use in ransomware detection;these are termed EntropySA and DistSA,and both consider the interesting characteristics of certain file subareas termed“sample areas”(SAs).For an encrypted file,both the sampled area and the whole file exhibit high-level randomness,but for a plain file,the sampled area embeds informative structures such as a file header and thus exhibits relatively low-level randomness even though the entire file exhibits high-level *** and DistSA use“byte frequency”and a variation of byte frequency,respectively,derived from sampled *** indicators cause less overhead than other entropy-based detection methods,as experimentally proven using realistic ransomware *** evaluate the effectiveness and feasibility of our indicators,we also employ three expensive but elaborate classification models(neural network,support vector machine and threshold-based approaches).Using these models,our experimental indicators yielded an average Fl-measure of 0.994 and an average detection rate of 99.46%for file encryption attacks by realistic ransomware samples.
Unmanned Aerial Vehicles(UAVs)are widely used and meet many demands in military and civilian *** the continuous enrichment and extensive expansion of application scenarios,the safety of UAVs is constantly being *** ad...
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Unmanned Aerial Vehicles(UAVs)are widely used and meet many demands in military and civilian *** the continuous enrichment and extensive expansion of application scenarios,the safety of UAVs is constantly being *** address this challenge,we propose algorithms to detect anomalous data collected from drones to improve drone *** deployed a one-class kernel extreme learning machine(OCKELM)to detect anomalies in drone *** default,OCKELM uses the radial basis(RBF)kernel function as the kernel function of *** improve the performance ofOCKELM,we choose a TriangularGlobalAlignmentKernel(TGAK)instead of anRBF Kernel and introduce the Fast Independent Component Analysis(FastICA)algorithm to reconstruct UAV *** on the above improvements,we create a novel anomaly detection strategy *** method is finally validated on the UCI dataset and detected on the Aeronautical Laboratory Failures and Anomalies(ALFA)*** experimental results show that compared with other methods,the accuracy of this method is improved by more than 30%,and point anomalies are effectively detected.
The timely identification of mental health issues enables experts to more effectively provide treatment and enhance the well-being of patients. Mental health pertains to an individual’s emotional, mental, and interpe...
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The work proposes a methodology for five different classes of ECG signals. The methodology utilises moving average filter and discrete wavelet transformation for the remove of baseline wandering and powerline interfer...
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