Biblio

Injection attack is one of the best 10 security dangers declared by OWASP. SQL infusion is one of the main types of attack. In light of their assorted and quick nature, SQL injection can detrimentally affect the line, prompting broken and public data on the site. Therefore, this article presents a profound woodland-based technique for recognizing complex SQL attacks. Research shows that the methodology we use resolves the issue of expanding and debasing the first condition of the woodland. We are currently presenting the AdaBoost profound timberland-based calculation, which utilizes a blunder level to refresh the heaviness of everything in the classification. At the end of the day, various loads are given during the studio as per the effect of the outcomes on various things. Our model can change the size of the tree quickly and take care of numerous issues to stay away from issues. The aftereffects of the review show that the proposed technique performs better compared to the old machine preparing strategy and progressed preparing technique.

This paper proposes a machine-learning-based advanced online dynamic security assessment (DSA) method, which provides a detailed evaluation of the system stability after a disturbance by predicting impending loss of synchronism (LOS) of generators. Voltage angles at generator buses are used as the features of the different random forest (RF) classifiers which are trained to consecutively predict LOS of the generators as a contingency proceeds and updated measurements become available. A wide range of contingencies for various topologies and operating conditions of the IEEE 118-bus system has been studied in offline analysis using the GE positive sequence load flow analysis (PSLF) software to create a comprehensive dataset for training and testing the RF models. The performances of the trained models are evaluated in the presence of measurement errors using various metrics. The results reveal that the trained models are accurate, fast, and robust to measurement errors.

The enormous growth of Internet-based traffic exposes corporate networks with a wide variety of vulnerabilities. Intrusive traffics are affecting the normal functionality of network's operation by consuming corporate resources and time. Efficient ways of identifying, protecting, and mitigating from intrusive incidents enhance productivity. As Intrusion Detection System (IDS) is hosted in the network and at the user machine level to oversee the malicious traffic in the network and at the individual computer, it is one of the critical components of a network and host security. Unsupervised anomaly traffic detection techniques are improving over time. This research aims to find an efficient classifier that detects anomaly traffic from NSL-KDD dataset with high accuracy level and minimal error rate by experimenting with five machine learning techniques. Five binary classifiers: Stochastic Gradient Decent, Random Forests, Logistic Regression, Support Vector Machine, and Sequential Model are tested and validated to produce the result. The outcome demonstrates that Random Forest Classifier outperforms the other four classifiers with and without applying the normalization process to the dataset.

Recently, due to the increase of outsourcing in IC design, it has been reported that malicious third-party vendors often insert hardware Trojans into their ICs. How to detect them is a strong concern in IC design process. The features of hardware-Trojan infected nets (or Trojan nets) in ICs often differ from those of normal nets. To classify all the nets in netlists designed by third-party vendors into Trojan ones and normal ones, we have to extract effective Trojan features from Trojan nets. In this paper, we first propose 51 Trojan features which describe Trojan nets from netlists. Based on the importance values obtained from the random forest classifier, we extract the best set of 11 Trojan features out of the 51 features which can effectively detect Trojan nets, maximizing the F-measures. By using the 11 Trojan features extracted, the machine-learning based hardware Trojan classifier has achieved at most 100% true positive rate as well as 100% true negative rate in several TrustHUB benchmarks and obtained the average F-measure of 74.6%, which realizes the best values among existing machine-learning-based hardware-Trojan detection methods.