Currently, numerous types of cybercrime are organized through the internet. Hence, this study mainly focuses on phishing attacks. Although phishing was first used in 1996, it has become the most severe and dangerous cybercrime on the internet. Phishing utilizes email distortion as its underlying mechanism for tricky correspondences, followed by mock sites, to obtain the required data from people in question. Different studies have presented their work on the precaution, identification, and knowledge of phishing attacks; however, there is currently no complete and proper solution for frustrating them. Therefore, machine learning plays a vital role in defending against cybercrimes involving phishing attacks. The proposed study is based on the phishing URL-based dataset extracted from the famous dataset repository, which consists of phishing and legitimate URL attributes collected from 11000+ website datasets in vector form. After preprocessing, many machine learning algorithms have been applied and designed to prevent phishing URLs and provide protection to the user. This study uses machine learning models such as decision tree (DT), linear regression (LR), random forest (RF), naive Bayes (NB), gradient boosting classifier (GBM), K-neighbors classifier (KNN), support vector classifier (SVC), and proposed hybrid LSD model, which is a combination of logistic regression, support vector machine, and decision tree (LR+SVC+DT) with soft and hard voting, to defend against phishing attacks with high accuracy and efficiency. The canopy feature selection technique with cross fold valoidation and Grid Search Hyperparameter Optimization techniques are used with proposed LSD model. Furthermore, to evaluate the proposed approach, different evaluation parameters were adopted, such as the precision, accuracy, recall, F1-score, and specificity, to illustrate the effects and efficiency of the models. The results of the comparative analyses demonstrate that the proposed approach outperforms the other models and achieves the best results.

The paper introduces a novel method for identifying the location of harmonic-generating sources in smartgrids. The method utilizes a Dual-Tree Complex Wavelet Transform (DTCWT) of voltage and current signals measured at a specific point in the network. By applying DTCWT Transform, the signals are decomposed, and three non-active power quantities are extracted to represent the harmonic components within the system exclusively. These chosen non-active power quantities serve as indicators of the presence of harmonics in the system. Through analysis and comparison of these quantities, the method enables determining the precise location of the dominant harmonic generating source. This information is valuable for effectively addressing and mitigating harmonic issues in the network. Leveraging DTCWT and focusing on non-active power quantities provides a valuable tool for power system engineers and operators to diagnose and mitigate harmonic issues, ultimately improving power quality and system performance. This study presents a new feature extraction method to compute Non-active power quantities based on DTCWT due to its shift-invariant property.

The output power of a photovoltaic (PV) system is dependent on the perfect operation of the PV array. PV arrays may experience various fault conditions as a result of open circuit, short circuit, and partial shading. A fault is an aberrant situation that can interfere with the effective execution of a PV system. To reduce power loss and harm to the PV system, design engineers must identify faults. The most common problems in PV array systems are open circuit, partial shading, hotspots, cracks and short circuit faults. Several scientists suggested fault diagnosis methods to recognize faults in PV arrays, however the majority failed to detect problems accurately and quickly. In this paper, a two-stage algorithm based on wavelet transform and ensemble KNN method is used to detect and classify faults in PV array. A 6X6 series parallel PV array is used to test the proposed methodology. MATLAB Simulink is used to model the 6X6 PV array. Wavelet Analysis tool bar is used for signal analysis.

High impedance fault (HIF) is difficult to detect by conventional overcurrent protection relays due to the lower fault current values, which are normally lower than the normal current. A fast and reliable algorithm is required to detect this type of fault. This paper proposes a novel method for detecting the location of HIF fault zone in a distribution system by using a novel graph theory‐based zone detection technique along with a Random Search Multilevel Support Vector Machine (RSMSVM) algorithm to classify the faulted zone. Due to shift in‐variance property of “Dual Tree Complex Wavelet Transform (DTCWT),” which has been used, in this paper, to decompose the voltage/current waveform to collect the signature of the signals and feed to the optimized RSMSVM model for classifying fault zone. The proposed method is evaluated on the IEEE 33‐bus system and also IEEE 39 bus test system under normal and noisy conditions. The proposed method is also evaluated for distribution network with the integration of distributed generation.

Fault is the abnormal condition in power system, which must be detected as early as possible. It is very important to detect the fault as quick as possible to reduce the effects of fault like equipment damage, property loss and human loss. Arc faults have high power discharge property between two conductors; this property causes damage to the conductors which leads to electric fire between the conductors. It is very necessary to detect these faults immediately to avoid fire accidents. There are various methods to detect these arc faults in microgrid. In this paper voltage and current signals are measured through instrumental transformers and voltage signal is decomposed by the discrete wavelet transform signal processing technique. The decomposed signals are further processed in various machines learning classifier’s for detecting the arc fault. The Proposed methodology studies the performance of various machine learning classifiers to detect arc fault in microgrid and it is carried out in MATLAB/Simulink Software.