Detecting Distributed Denial of Service Attacks Using Logistic Regression and SVM Methods

A distributed denial-of-service (DDoS) attack is an attempt to produce humongous traffic within a network by overwhelming a targeted server or its neighboring infrastructure with a flood of service requests ceaselessly coming from multiple remotely controlled malware-infected computers or network-connected devices. Thus, exploring DDoS attacks by recognizing their functionalities and differentiating them from normal traffic services are the primary concerns of network security issues particularly for online businesses. In modern networks, most DDoS attacks occur in the network and application layer including HTTP flood, UDP flood, SIDDOS, SMURF, SNMP flood, IP NULL, etc. The goal of this paper is to detect DDoS attacks from all service requests and classify them according to DDoS classes. In this regard, a standard dataset is collected from the internet which contains several network-related attributes and their corresponding DDoS attack class name. Two(2) different machine learning approaches, SVM and Logistic Regression, are implemented in the dataset for detecting and classifying DDoS attacks, and a comparative study is accomplished among them in terms of accuracy, precision, and recall rates. Logistic Regression and SVM both achieve 98.65% classification accuracy which is the highest achieved accuracy among other previous experiments with the same dataset.

Machine Learning Approaches on Crop Pattern Recognition a Comparative Analysis

Monitoring agricultural activities is important to ensure food security. Remote sensing plays a significant role for large-scale continuous monitoring of cultivation activities. Time series remote sensing data were used for the generation of the cropping pattern. Classification algorithms are used to classify crop patterns and mapped agriculture land used. Some conventional classification methods including support vector machine (SVM) and decision trees were applied for crop pattern recognition. However, in this paper, we are proposing Deep Neural Network (DNN) based classification to improve the performance of crop pattern recognition and make a comparative analysis with two (2) other machine learning approaches including Naive Bayes and Random Forest.

Identify The Beehive Sound Using Deep Learning

Flowers play an essential role in removing the duller from the environment. The life cycle of the flowering plants involves pollination, fertilization, flowering, seed-formation, dispersion, and germination. Honeybees pollinate approximately 75% of all flowering plants. Environmental pollution, climate change, natural landscape demolition, and so on, threaten the natural habitats, thus continuously reducing the number of honeybees. As a result, several researchers are attempting to resolve this issue. Applying acoustic classification to recordings of beehive sounds may be a way of detecting changes within them. In this research, we use deep learning techniques, namely Sequential Neural Network, Convolutional Neural Network, and Recurrent Neural Network, on the recorded sounds to classify bee sounds from the nonbeehive noises. In addition, we perform a comparative study among some popular non-deep learning techniques, namely Support Vector Machine, Decision Tree, Random Forest, and Na\"ive Bayes, with the deep learning techniques. The techniques are also verified on the combined recorded sounds (25-75% noises).