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In the age of sophisticated cyber threats, botnet detection remains a crucial yet complex security challenge. Existing detection systems are continually outmaneuvered by the relentless advancement of botnet strategies, necessitating a more dynamic and proactive approach. Our research introduces a ground-breaking solution to the persistent botnet pr...
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Phishing attacks persist as a prevalent cyber threat exploiting the vulnerability of internet users, tricking them into clicking malicious attachments or links under the guise of legitimacy. This study aims to investigate the impact of users' attitudes on cybersecurity awareness, specifically focusing on phishing attacks and related social engineer...
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... However, a more methodical approach is required for high-dimensional datasets. Because redundant and irrelevant characteristics have no substantial impact on machine learning, eliminating them from the learning process will aid in increasing learning speed, reducing overfitting, avoiding the curse of dimensionality, and creating simple models [34]. The standard approach for supervised feature selection consists of four steps, as shown in Figure 3. Two of the most popular feature selection methods (ANOVA and mutual information) were used in the suggested Sleep-1D-CNN model. ...
One of the most essential components of human life is sleep. One of the first steps in spotting abnormalities connected to sleep is classifying sleep stages. Based on the kind and frequency of signals obtained during a polysomnography test, sleep phases can be separated into groups. Accurate classification of sleep stages from electroencephalogram (EEG) signals plays a crucial role in sleep disorder diagnosis and treatment. This study proposes a novel approach that combines feature selection techniques with convolutional neural networks (CNNs) to enhance the classification performance of sleep stages using EEG signals. Firstly, a comprehensive feature selection process was employed to extract discriminative features from raw EEG data, aiming to reduce dimensionality and enhance the efficiency of subsequent classification using mutual information (MI) and analysis of variance (ANOVA) after splitting the dataset into two sets—the training set (70%) and testing set (30%)—then processing it using the standard scalar method. Subsequently, a 1D-CNN architecture was designed to automatically learn hierarchical representations of the selected features, capturing complex patterns indicative of different sleep stages. The proposed method was evaluated on a publicly available EDF-Sleep dataset, demonstrating superior performance compared to traditional approaches. The results highlight the effectiveness of integrating feature selection with CNNs in improving the accuracy and reliability of sleep stage classification from EEG signals, which reached 99.84% with MI-50. This approach not only contributes to advancing the field of sleep disorder diagnosis, but also holds promise for developing more efficient and robust clinical decision support systems.
... Hossain, M.A. et al. [38] amalgamates common algorithms, including categorical analysis, mutual information, and principal component analysis, to extract representative features from the dataset. The extracted features are then used to train an ML model. ...
Botnets pose a significant challenge to network security but are difficult to detect because of their dynamic and evolving nature, which limits the effectiveness of conventional supervised neural network detection methods. To address this problem, the present study proposes a novel neural network-based self-training framework for botnet detection, in which pseudo-labels are generated from unlabeled data by a trained classifier, which is iteratively refined over time using a combined dataset containing both training and pseudo-labeled data. Although not all of the generated pseudo-labels are applicable to every botnet, the self-training framework can label unseen botnets with behaviors similar to those of known botnets with high confidence. Several strategies are proposed for enhancing the robustness of the classification performance by minimizing the number of incorrect pseudo-labels, mitigating the effects of erroneous pseudo-labels on the overall performance of the network, and optimizing the proportion of unlabeled data for labeling. Experiments conducted on both synthetic datasets confirm the superiority of the proposed method over the base model, particularly when the training data constitutes only a small portion of the total amount dataset. Subsequent experiments also demonstrate the efficacy of the framework in successfully detecting unseen botnet variants and its commendable performance in real-world campus network traffic.
... Correlation Analysis (CA) [14] is a technique used to measure the linear relationship or association between two variables. In the context of our approach for detecting botnetbased DDoS attacks in the SDN environment, CA was applied to identify relevant features. ...
The burgeoning adoption of Software Defined Networking (SDN) has revolutionized network management, yet it introduces unprecedented challenges, notably the susceptibility to Distributed Denial-of-Service (DDoS) attacks. Recognizing this imperative, our research delves into fortifying SDN security, proposing a novel approach that marries machine learning prowess with the intricacies of SDN architecture. This study endeavors to bolster DDoS detection within SDN environments, strategically leveraging an ensemble-based Random Forest (RF) algorithm and Recursive Feature Elimination. The overarching goal is to enhance the efficacy of SDN security measures, providing a dynamic defense against evolving DDoS threats. An implementation process unfolds through comprehensive data pre-processing, featuring the strategic selection of key features via Recursive Feature Elimination. Central to our approach is the application of an ensemble-based Random Forest algorithm, which has been rigorously trained using a dedicated dataset tailored for Software Defined Networking. A comprehensive assessment follows, where critical performance indicators such as Recall, Accuracy, Precision, F-1 Score, and Area Under the Curve (AUC) substantiate the reliability of our method. The outcome is a paradigm shift in DDoS detection within SDN. Our ensemble-based RF algorithm not only exhibits commendable accuracy but also outperforms traditional methods across key metrics. The strategic feature selection contributes not only to heightened efficiency but also bolsters the overall resilience of SDN networks against DDoS incursions. Beyond the confines of conventional methodologies, this model, attaining almost 100% accuracy, heralds a milestone in SDN security.
In the realm of cybersecurity, the detection and analysis of obfuscated malware remain a critical challenge, especially in the context of memory dumps. This research paper presents a novel machine learning-based framework designed to enhance the detection and analytical capabilities against such elusive threats for binary and multi type’s malware. Our approach leverages a comprehensive dataset comprising benign and malicious memory dumps, encompassing a wide array of obfuscated malware types including Spyware, Ransomware, and Trojan Horses with their sub-categories. We begin by employing rigorous data preprocessing methods, including the normalization of memory dumps and encoding of categorical data. To tackle the issue of class imbalance, a Synthetic Minority Over-sampling Technique is utilized, ensuring a balanced representation of various malware types. Feature selection is meticulously conducted through Chi-Square tests, mutual information, and correlation analyses, refining the model’s focus on the most indicative attributes of obfuscated malware. The heart of our framework lies in the deployment of an Ensemble-based Classifier, chosen for its robustness and effectiveness in handling complex data structures. The model’s performance is rigorously evaluated using a suite of metrics, including accuracy, precision, recall, F1-score, and the area under the ROC curve (AUC) with other evaluation metrics to assess the model’s efficiency. The proposed model demonstrates a detection accuracy exceeding 99% across all cases, surpassing the performance of all existing models in the realm of malware detection.
The usefulness of ensemble-based total time series analysis in Wi-Fi sensor networks is examined in this paper. A device to uses an ensemble approach combines multiple strategies to enhance overall predictive performance. This research assesses various tactics using unique metrics, such as robustness and accuracy. It contrasts the effectiveness of traditional time series methods with ensemble-based total fashions. An experimental approach focusing mostly on exceptional Wi-Fi sensor network scenarios is employed to evaluate the overall effectiveness of the suggested methods. Additionally, this study looks into how changes to community features like energy delivery, conversation range, and node density affect how effective the suggested methods are. The study's findings maintain the capacity to create effective Wi-Fi sensor networks with improved predicted overall performance. The usefulness of ensemble-based time collecting and analysis techniques for wireless sensor networks is investigated in this research. This study primarily looks upon function extraction and seasonality discounting of time series records in WSNs. In this analysis, seasonality is discounted using an ensemble median filter, and feature extraction is accomplished by primary component assessment. To assess the performance of the suggested ensemble technique on every simulated and real-world international WSN fact, multiple experiments are carried out. The findings suggest that the ensemble approach can improve the exceptional time-gathering records within WSNs and reduce seasonality. Furthermore, when compared to single-sensor strategies, the ensemble technique further improves the accuracy of the function extraction system. This work demonstrates the applicability of the ensemble approach for the investigation of time collection data in WSNs
