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.

The Controller Area Network (CAN) bus is the de-facto standard for connecting the Electronic Control Units (ECUs) in automobiles. However, there are serious cyber-security risks due to the lack of security mechanisms. In order to mine the vulnerabilities in CAN bus, this paper proposes CAN-FT, a fuzz testing method for automotive CAN bus, which uses a Generative Adversarial Network (GAN) based fuzzy message generation algorithm and the Adaptive Boosting (AdaBoost) based anomaly detection mechanism to capture the abnormal states of CAN bus. Experimental results on a real-world vehicle show that CAN-FT can find vulnerabilities more efficiently and comprehensively.

Due to the extraordinary volume of business information, classy cyber-attacks pointing the networks of all enterprise have become more casual, with intruders trying to pierce vast into and grasp broader from the compromised network machines. The vital security essential is that field experts and the network administrators have a common terminology to share the attempt of intruders to invoke the system and to rapidly assist each other retort to all kind of threats. Given the enormous huge system traffic, traditional Machine Learning (ML) algorithms will provide ineffective predictions of the network anomaly. Thereby, a hybridized multi-model system can improve the accuracy of detecting the intrusion in the networks. In this manner, this article presents a novel approach Clustered Generalization oriented Ensemble Learning Model (CGELM) for predicting the network anomaly. The performance metrics of the anticipated approach are Detection Rate (DR) and False Predictive Rate (FPR) for the two heterogeneous data sets namely NSL-KDD and UGR'16. The proposed method provides 98.93% accuracy for DR and 0.14% of FPR against Decision Stump AdaBoost and Stacking Ensemble methods.

Whenever any internet user visits a website, a scripting language runs in the background known as JavaScript. The embedding of malicious activities within the script poses a great threat to the cyberworld. Attackers take advantage of the dynamic nature of the JavaScript and embed malicious code within the website to download malware and damage the host. JavaScript developers obfuscate the script to keep it shielded from getting detected by the malware detectors. In this paper, we propose a novel technique for analysing and detecting JavaScript using sandbox assisted ensemble model. We extract the payload using malware-jail sandbox to get the real script. Upon getting the extracted script, we analyse it to define the features that are needed for creating the dataset. We compute Pearson's r between every feature for feature extraction. An ensemble model consisting of Sequential Minimal Optimization (SMO), Voted Perceptron and AdaBoost algorithm is used with voting technique to detect malicious JavaScript. Experimental results show that our proposed model can detect obfuscated and de-obfuscated malicious JavaScript with an accuracy of 99.6% and 0.03s detection time. Our model performs better than other state-of-the-art models in terms of accuracy and least training and detection time.

This study aims to detect genuine suspicious events and false alarms within a dataset of network traffic alerts. The rapid development of cloud computing and artificial intelligence-oriented automatic services have enabled a large amount of data and information to be transmitted among network nodes. However, the amount of cyber-threats, cyberattacks, and network intrusions have increased in various domains of network environments. Based on the fields of data science and machine learning, this paper proposes a series of solutions involving data preprocessing, exploratory data analysis, new features creation, features selection, ensemble learning, models construction, and verification to identify suspicious network events. This paper proposes a modified form of stacking ensemble machine learning which includes AdaBoost, Neural Networks, Random Forest, LightGBM, and Extremely Randomised Trees (Extra Trees) to realise a high-performance classification. A suspicious network event recognition dataset for a security operations centre, which uses real network log observations from the 2019 IEEE BigData Cup Challenge, is used as an experimental dataset. This paper investigates the possibility of integrating big-data analytics, machine learning, and data science to improve intelligent cybersecurity.

Machine learning is a major area in artificial intelligence, which enables computer to learn itself explicitly without programming. As machine learning is widely used in making decision automatically, attackers have strong intention to manipulate the prediction generated my machine learning model. In this paper we study about the different types of attacks and its countermeasures on machine learning model. By research we found that there are many security threats in various algorithms such as K-nearest-neighbors (KNN) classifier, random forest, AdaBoost, support vector machine (SVM), decision tree, we revisit existing security threads and check what are the possible countermeasures during the training and prediction phase of machine learning model. In machine learning model there are 2 types of attacks that is causative attack which occurs during the training phase and exploratory attack which occurs during the prediction phase, we will also discuss about the countermeasures on machine learning model, the countermeasures are data sanitization, algorithm robustness enhancement, and privacy preserving techniques.

Most anti-phishing solutions that exist today require scanning a large portion of the web, which is vast and equivalent to finding a needle in a haystack. In addition, such solutions are not very efficient. We propose a different approach. Our solution does not rely on the scanning of the entire Internet or a large portion of it and only needs access to the brand's traffic in order to be able to detect phishing attempts against that brand. By analyzing a sample of phishing websites, we find features that can be used to distinguish phishing websites from the legitimate ones. We then use these features to train a machine learning classifier capable of helping brands detect phishing attempts against them. Our approach can detect up to 86% of phishing attacks against the brands and is best used as a complementary tool to the existing anti-phishing solutions.

With the rapid development of the information technology, more and more high-speed networks came out. The 4G LTE network as a recently emerging network has gradually entered the mainstream of the communication network. This paper proposed an effective content-based information filtering based on the 4G LTE high-speed network by combing the content-based filter and traditional simple filter. Firstly, raw information is pre-processed by five-tuple filter. Secondly, we determine the topics and character of the source data by key nearest neighbor text classification after minimum-risk Bayesian classification. Finally, the improved AdaBoost algorithm achieves the four-level content-based information filtering. The experiments reveal that the effective information filtering method can be applied to the network security, big data analysis and other fields. It has high research value and market value.

This paper proposes and describes an active authentication model based on user profiles built from user-issued commands when interacting with GUI-based application. Previous behavioral models derived from user issued commands were limited to analyzing the user's interaction with the *Nix (Linux or Unix) command shell program. Human-computer interaction (HCI) research has explored the idea of building users profiles based on their behavioral patterns when interacting with such graphical interfaces. It did so by analyzing the user's keystroke and/or mouse dynamics. However, none had explored the idea of creating profiles by capturing users' usage characteristics when interacting with a specific application beyond how a user strikes the keyboard or moves the mouse across the screen. We obtain and utilize a dataset of user command streams collected from working with Microsoft (MS) Word to serve as a test bed. User profiles are first built using MS Word commands and identification takes place using machine learning algorithms. Best performance in terms of both accuracy and Area under the Curve (AUC) for Receiver Operating Characteristic (ROC) curve is reported using Random Forests (RF) and AdaBoost with random forests.