The phishing attack is one of the main cybersecurity threats in web phishing and spear phishing. Phishing websites continue to be a problem. One of the main contributions to our study was working and extracting the URL & Domain Identity feature, Abnormal Features, HTML and JavaScript Features, and Domain Features as semantic features to detect phishing websites, which makes the process of classification using those semantic features, more controllable and more effective. The current study used machine learning model algorithms to detect phishing websites, and comparisons were made. We have used 16 machine learning models adopted with 10 semantic features that represent the most effective features for the detection of phishing webpages extracted from two datasets. The GradientBoostingClassifier and RandomForestClassifier had the best accuracy based on the comparison results (i.e., about 97%). In contrast, GaussianNB and the stochastic gradient descent (SGD) classifier represent the lowest accuracy results; 84% and 81% respectively, in comparison with other classifiers.

In recent years, Botnets have been adopted as a popular method to carry and spread many malicious codes on the Internet. These malicious codes pave the way to execute many fraudulent activities including spam mail, distributed denial-of-service attacks and click fraud. While many Botnets are set up using centralized communication architecture, the peer-to-peer (P2P) Botnets can adopt a decentralized architecture using an overlay network for exchanging command and control data making their detection even more difficult. This work presents a method of P2P Bot detection based on an adaptive multilayer feed-forward neural network in cooperation with decision trees. A classification and regression tree is applied as a feature selection technique to select relevant features. With these features, a multilayer feed-forward neural network training model is created using a resilient back-propagation learning algorithm. A comparison of feature set selection based on the decision tree, principal component analysis and the ReliefF algorithm indicated that the neural network model with features selection based on decision tree has a better identification accuracy along with lower rates of false positives. The usefulness of the proposed approach is demonstrated by conducting experiments on real network traffic datasets. In these experiments, an average detection rate of 99.08 % with false positive rate of 0.75 % was observed.

Forest fires are a serious ecological concern, and smoke is an early warning indicator. Early smoke images barely capture a tiny portion of the total smoke. Because of the irregular nature of smoke’s dispersion and the dynamic nature of the surrounding environment, smoke identification is complicated by minor pixel-based traits. This study presents a new framework that decreases the sensitivity of various YOLO detection models. Additionally, we compare the detection performance and speed of different YOLO models such as YOLOv3, YOLOv5, and YOLOv7 with prior ones such as Fast R-CNN and Faster R-CNN. Moreover, we follow the use of a collected dataset that describes three distinct detection areas, namely close, medium, and far distance, to identify the detection model’s ability to recognize smoke targets correctly. Our model outperforms the gold-standard detection method on a multi-oriented dataset for detecting forest smoke by an mAP accuracy of 96.8% at an IoU of 0.5 using YOLOv5x. Additionally, the findings of the study show an extensive improvement in detection accuracy using several data-augmentation techniques. Moreover, YOLOv7 outperforms YOLOv3 with an mAP accuracy of 95%, compared to 94.8% using an SGD optimizer. Extensive research shows that the suggested method achieves significantly better results than the most advanced object-detection algorithms when used on smoke datasets from wildfires, while maintaining a satisfactory performance level in challenging environmental conditions.