A machine learning-based intrusion detection for detecting internet of things network attacks

The increased demand for network security nowadays is becoming a crucial strategy. Accordingly, a need for intrusion detection system is essential to track cybersecurity attacks. Thus, some protection strategies are necessary for this purpose. However, the current intrusion detection systems are still developing and looking for more accuracy. In this paper, supervised learning algorithms (Random Forest, XGBoost, K-Nearest Neighbors (k = 5), Artificial Neural Network, Logistic Regression, Support Vector Machine, and LASSO-LARS) are trained and tested to a preprocessed dataset. It contains benign and up-to-date common attacks (DoS, Probe, R2L, and U2R). In order to measure the performance of each supervised learning algorithm, the F1-score is calculated. As a result, the random forest, XGBoost, and K-nearest neighbors (k = 5) algorithms have better accuracy than the others, having values of 99.7%, 99.1%, and 97.6% prediction success rate, respectively. The least performance is for logistic regression algorithm with a prediction accuracy rate value of 71.4%.

Evaluation of Supervised Machine Learning Algorithms for Multi-class Intrusion Detection Systems

In this research, a new level set based segmentation algorithm was proposed for human face segmentation. At first, the human facial images were collected from FAce Semantic SEGmentation (FASSEG) dataset. After collecting the images, pre-processing was accomplished by utilizing Contrast limited adaptive histogram equalization (CLAHE). The undertaken methodology effectively improves the quality of facial images by removing the unwanted noise. Then, segmentation was done by using Adaptively Regularized Kernel Based Fuzzy Clustering Means (ARKFCM) clustering with level set, which was a high level machine learning algorithm for localizing the face parts in complex template. Simulation outcome shows that the proposed segmentation algorithm effectively segments the facial parts in light of precision, recall, jaccard coefficient, dice coefficient, accuracy, and miss rate. The proposed segmentation algorithm enhanced the segmentation accuracy in face segmentation upto 4.5% compared to the existing methodology (pixel wise segmentation).

Automatic Face Segmentation Using Adaptively Regularized Kernel-Based Fuzzy Clustering Means With Level Set Algorithm

Software-defined networking (SDN) can provide flexible traffic control and is an important part of the next-generation computer network. Distributed Denial of Service (DDoS) attack targeting the controller can seriously affect the performance of SDN. Although there are many schemes to detect and defend against this type of attack, the detection accuracy and efficiency of these schemes are severely limited due to the large scale and high dimension of traffic in SDN. According to the characteristics of SDN, this paper presents a new feature selection method to detect and defend against DDoS attacks targeting the controller. Firstly, Spearman’s rank correlation coefficient and Gini impurity were used to extract the optimal feature subset. Then the attack detection module will detect the DDoS attack. Finally, attack defense module is introduced to filter attack packets and protect controller computing resources. We used the NSL-KDD dataset for evaluation and comparison with other schemes. Experimental results show that our scheme can detect and defend against DDoS attacks accurately. 

Detection and Defense Against DDoS Attack on SDN Controller Based on Feature Selection

Software testing is a critical aspect of the software development process. It involves test cases or test suites to ensure the software product conforms to the user’s requirements. However, some issues currently affect the software testing process, such as human error, an unstable environment, and incorrect and incomplete requirements documentation. This paper explains how artificial intelligence (AI) concepts, including machine learning, deep learning, neural networks, and expert systems, can be applied to software testing to ease its rigorous and tedious process. This paper would also overview the different types of software testing, the advantages of using artificial intelligence pillars to software testing, and AI challenges in software testing. 

Applications of Artificial Intelligence in Software Testing

The rapid development of various services that are provided by information technology has been widely accepted by the users who are making use of such services in their day-to-day life activities. ...

An Approach to Feature Selection in Intrusion Detection Systems Using Machine Learning Algorithms

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An Approach to Feature Selection in Intrusion Detection Systems Using Machine Learning Algorithms