Biblio

An IDS is a system that helps in detecting any kind of doubtful activity on a computer network. It is capable of identifying suspicious activities at both the levels i.e. locally at the system level and in transit at the network level. Since, the system does not have its own dataset as a result it is inefficient in identifying unknown attacks. In order to overcome this inefficiency, we make use of ML. ML assists in analysing and categorizing attacks on diverse datasets. In this study, the efficacy of eight machine learning algorithms based on KDD CUP99 is assessed. Based on our implementation and analysis, amongst the eight Algorithms considered here, Support Vector Machine (SVM), Random Forest (RF) and Decision Tree (DT) have the highest testing accuracy of which got SVM does have the highest accuracy

To detect human behaviour and measure accuracy of classification rate. Materials and Methods: A novel deep belief network with sample size 10 and support vector machine with sample size of 10. It was iterated at different times predicting the accuracy percentage of human behaviour. Results: Human behaviour detection utilizing novel deep belief network 87.9% accuracy compared with support vector machine 87.0% accuracy. Deep belief networks seem to perform essentially better compared to support vector machines \$(\textbackslashmathrmp=0.55)(\textbackslashtextPiˆ0.05)\$. The deep belief algorithm in computer vision appears to perform significantly better than the support vector machine algorithm. Conclusion: Within this human behaviour detection novel deep belief network has more precision than support vector machine.

Aim: To bring off the spam detection in social media using Support Vector Machine (SVM) algorithm and compare accuracy with Artificial Neural Network (ANN) algorithm sample size of dataset is 5489, Initially the dataset contains several messages which includes spam and ham messages 80% messages are taken as training and 20% of messages are taken as testing. Materials and Methods: Classification was performed by KNN algorithm (N=10) for spam detection in social media and the accuracy was compared with SVM algorithm (N=10) with G power 80% and alpha value 0.05. Results: The value obtained in terms of accuracy was identified by ANN algorithm (98.2%) and for SVM algorithm (96.2%) with significant value 0.749. Conclusion: The accuracy of detecting spam using the ANN algorithm appears to be slightly better than the SVM algorithm.

Supervisory control and data acquisition (SCADA) systems play pivotal role in the operation of modern critical infrastructures (CIs). Technological advancements, innovations, economic trends, etc. have continued to improve SCADA systems effectiveness and overall CIs’ throughput. However, the trends have also continued to expose SCADA systems to security menaces. Intrusions and attacks on SCADA systems can cause service disruptions, equipment damage or/and even fatalities. The use of conventional intrusion detection models have shown trends of ineffectiveness due to the complexity and sophistication of modern day SCADA attacks and intrusions. Also, SCADA characteristics and requirement necessitate exceptional security considerations with regards to intrusive events’ mitigations. This paper explores the viability of supervised learning algorithms in detecting intrusions specific to SCADA systems and their communication protocols. Specifically, we examine four supervised learning algorithms: Random Forest, Naïve Bayes, J48 Decision Tree and Sequential Minimal Optimization-Support Vector Machines (SMO-SVM) for evaluating SCADA datasets. Two SCADA datasets were used for evaluating the performances of our approach. To improve the classification performances, feature selection using principal component analysis was used to preprocess the datasets. Using prominent classification metrics, the SVM-SMO presented the best overall results with regards to the two datasets. In summary, results showed that supervised learning algorithms were able to classify intrusions targeted against SCADA systems with satisfactory performances.

During pandemic COVID-19 outbreaks, number of cyber-attacks including phishing activities have increased tremendously. Nowadays many technical solutions on phishing detection were developed, however these approaches were either unsuccessful or unable to identify phishing pages and detect malicious codes efficiently. One of the downside is due to poor detection accuracy and low adaptability to new phishing connections. Another reason behind the unsuccessful anti-phishing solutions is an arbitrary selected URL-based classification features which may produce false results to the detection. Therefore, in this work, an intelligent phishing detection and prevention model is designed. The proposed model employs a self-destruct detection algorithm in which, machine learning, especially supervised learning algorithm was used. All employed rules in algorithm will focus on URL-based web characteristic, which attackers rely upon to redirect the victims to the simulated sites. A dataset from various sources such as Phish Tank and UCI Machine Learning repository were used and the testing was conducted in a controlled lab environment. As a result, a chrome extension phishing detection were developed based on the proposed model to help in preventing phishing attacks with an appropriate countermeasure and keep users aware of phishing while visiting illegitimate websites. It is believed that this smart phishing detection and prevention model able to prevent fraud and spam websites and lessen the cyber-crime and cyber-crisis that arise from year to year.

SQL Injection has been around as a harmful and prolific threat on web applications for more than 20 years, yet it still poses a huge threat to the World Wide Web. Rapidly evolving web technology has not eradicated this threat; In 2017 51 % of web application attacks are SQL injection attacks. Most conventional practices to prevent SQL injection attacks revolves around secure web and database programming and administration techniques. Despite developer ignorance, a large number of online applications remain susceptible to SQL injection attacks. There is a need for a more effective method to detect and prevent SQL Injection attacks. In this research, we offer a unique machine learning-based strategy for identifying potential SQL injection attack (SQL injection attack) threats. Application of the proposed method in a Security Information and Event Management(SIEM) system will be discussed. SIEM can aggregate and normalize event information from multiple sources, and detect malicious events from analysis of these information. The result of this work shows that a machine learning based SQL injection attack detector which uses SIEM approach possess high accuracy in detecting malicious SQL queries.

Today billions of people are accessing the internet around the world. There is a need for new technology to provide security against malicious activities that can take preventive/ defensive actions against constantly evolving attacks. A new generation of technology that keeps an eye on such activities and responds intelligently to them is the intrusion detection system employing machine learning. It is difficult for traditional techniques to analyze network generated data due to nature, amount, and speed with which the data is generated. The evolution of advanced cyber threats makes it difficult for existing IDS to perform up to the mark. In addition, managing large volumes of data is beyond the capabilities of computer hardware and software. This data is not only vast in scope, but it is also moving quickly. The system architecture suggested in this study uses SVM to train the model and feature selection based on the information gain ratio measure ranking approach to boost the overall system's efficiency and increase the attack detection rate. This work also addresses the issue of false alarms and trying to reduce them. In the proposed framework, the UNSW-NB15 dataset is used. For analysis, the UNSW-NB15 and NSL-KDD datasets are used. Along with SVM, we have also trained various models using Naive Bayes, ANN, RF, etc. We have compared the result of various models. Also, we can extend these trained models to create an ensemble approach to improve the performance of IDS.

This paper introduces an application of machine learning algorithms. In fact, support vector machine and decision tree approaches are studied and applied to compare their performances in detecting, classifying, and locating faults in the transmission network. The IEEE 14-bus transmission network is considered in this work. Besides, 13 types of faults are tested. Particularly, the one fault and the multiple fault cases are investigated and tested separately. Fault simulations are performed using the SimPowerSystems toolbox in Matlab. Basing on the accuracy score, a comparison is made between the proposed approaches while testing simple faults, on the one hand, and when complicated faults are integrated, on the other hand. Simulation results prove that the support vector machine technique can achieve an accuracy of 87% compared to the decision tree which had an accuracy of 53% in complicated cases.

The major aim of the study is to predict the type of crime that is going to happen based on the crime hotspot detected for the given crime data with engineered spatial features. crime dataset is filtered to have the following 2 crime categories: crime against society, crime against person. Crime hotspots are detected by using the Novel Hierarchical density based Spatial Clustering of Application with Noise (HDBSCAN) Algorithm with the number of clusters optimized using silhouette score. The sample data consists of 501 crime incidents. Future types of crime for the given location are predicted by using the Support Vector Machine (SVM) and Convolutional Neural Network (CNN) algorithms (N=5). The accuracy of crime prediction using Support Vector Machine classification algorithm is 94.01% and Convolutional Neural Network algorithm is 79.98% with the significance p-value of 0.033. The Support Vector Machine algorithm is significantly better in accuracy for prediction of type of crime than Convolutional Neural Network (CNN).

Classifying and predicting the accuracy of intrusion detection on cybercrime by comparing machine learning methods such as Innovative Decision Tree (DT) with Support Vector Machine (SVM). By comparing the Decision Tree (N=20) and the Support Vector Machine algorithm (N=20) two classes of machine learning classifiers were used to determine the accuracy. The decision Tree (99.19%) has the highest accuracy than the SVM (98.5615%) and the independent T-test was carried out (=.507) and shows that it is statistically insignificant (p\textgreater0.05) with a confidence value of 95%. by comparing Innovative Decision Tree and Support Vector Machine. The Decision Tree is more productive than the Support Vector Machine for recognizing intruders with substantially checked, according to the significant analysis.

To solve the current problem of scarce information security talents, this paper proposes to design a network information security attack and defense practical training platform based on ThinkPHP framework. It provides help for areas with limited resources and also offers a communication platform for the majority of information security enthusiasts and students. The platform is deployed using ThinkPHP, and in order to meet the personalized needs of the majority of users, support vector machine algorithms are added to the platform to provide a more convenient service for users.

With the rapid development of the internet of things and cloud computing, users can instantly transmit a large amount of data to various fields, with the development of communication technology providing convenience for people's life, information security is becoming more and more important. Therefore, it is of great significance to study the technology of image hiding information detection. This paper mainly uses the support vector machine learning algorithm to detect the hidden information of the image, based on a standard image library, randomly selecting images for embedding secret information. According to the bit-plane correlation and the gradient energy change of a single bit-plane after encryption of an image LSB matching algorithm, gradient energy change is selected as characteristic change, and the gradient energy change is innovatively applied to a support vector machine classifier algorithm, and has very good detection effect and good stability on the dense image with the embedding rate of more than 40 percent.

In recent years web applications that are hacked every day estimated to be 30 000, and in most cases, web developers or website owners do not even have enough knowledge about what is happening on their sites. Web hackers can use many attacks to gain entry or compromise legitimate web applications, they can also deceive people by using phishing sites to collect their sensitive and private information. In response to this, the need is raised to take proper measures to understand the risks and be aware of the vulnerabilities that may affect the website and hence the normal business flow. In the scope of this study, mitigations against the most common web application attacks are set, and the web administrator is provided with ways to detect phishing links which is a social engineering attack, the study also demonstrates the generation of web application logs that simplifies the process of analyzing the actions of abnormal users to show when behavior is out of bounds, out of scope, or against the rules. The methods of mitigation are accomplished by secure coding techniques and the methods for phishing link detection are performed by various machine learning algorithms and deep learning techniques. The developed application has been tested and evaluated against various attack scenarios, the outcomes obtained from the test process showed that the website had successfully mitigated these dangerous web application attacks, and for the detection of phishing links part, a comparison is made between different algorithms to find the best one, and the outcome of the best model gave 98% accuracy.

This paper outlines an approach to build an Intrusion detection system for a network interface device. This research work has developed a hybrid intrusion detection system which involves various machine learning techniques along with inference detection for a comparative analysis. It is explained in 2 phases: Training (Model Training and Inference Network Building) and Detection phase (Working phase). This aims to solve all the current real-life problem that exists in machine learning algorithms as machine learning techniques are stiff they have their respective classification region outside which they cease to work properly. This paper aims to provide the best working machine learning technique out of the many used. The machine learning techniques used in comparative analysis are Decision Tree, Naïve Bayes, K-Nearest Neighbors (KNN) and Support Vector Machines (SVM) along with NSLKDD dataset for testing and training of our Network Intrusion Detection Model. The accuracy recorded for Decision Tree, Naïve Bayes, K-Nearest Neighbors (KNN) and Support Vector Machines(SVM) respectively when tested independently are 98.088%, 82.971%, 95.75%, 81.971% and when tested with inference detection model are 98.554%, 66.687%, 97.605%, 93.914%. Therefore, it can be concluded that our inference detection model helps in improving certain factors which are not detected using conventional machine learning techniques.

Even if there is a rapid proliferation with the advantages of low cost, the emerging on-demand cloud services have led to an increase in cybercrime activities. Cyber criminals are utilizing cloud services through its distributed nature of infrastructure and create a lot of challenges to detect and investigate the incidents by the security personnel. The tracing of command flow forms a clue for the detection of malicious activity occurring in the system through System Calls Analysis (SCA). As machine learning based approaches are known to automate the work in detecting malwares, simple Support Vector Machine (SVM) based approaches are often reporting low value of accuracy. In this work, a malware classification system proposed with the supervised machine learning of unknown malware instances through Support Vector Machine - Stochastic Gradient Descent (SVM-SGD) algorithm. The performance of the system evaluated on CIC-IDS2017 dataset with labelled attacks. The system is compared with traditional signature based detection model and observed to report less number of false alerts with improved accuracy. The signature based detection gets an accuracy of 86.12%, while the SVM-SGD gets the best accuracy of 99.13%. The model is found to be lightweight but efficient in detecting malware with high degree of accuracy.

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Support Vector Machine (SVM) is a relatively novel classification technology, which has shown higher performance than traditional learning methods in many applications. Therefore, some security researchers have proposed an intrusion detection method based on SVM. However, the SVM algorithm is very sensitive to the choice of kernel function and parameter adjustment. Once the parameter selection is unscientific, it will lead to poor classification accuracy. To solve this problem, this paper presents a Grey Wolf Optimizer Algorithm based on Particle Swarm Optimization (PSOGWO) algorithm to improve the Intrusion Detection System (IDS) based on SVM. This method uses PSOGWO algorithm to optimize the parameters of SVM to improve the overall performance of intrusion detection based on SVM. The "optimal detection model" of SVM classifier is determined by the fusion of PSOGWO algorithm and SVM. The comparison experiments based on NSL-KDD dataset show that the intrusion detection method based on PSOGWO-SVM achieves the optimization of the parameters of SVM, and has improved significantly in terms of detection rate, convergence speed and model balance. This shows that the method has better performance for network intrusion detection.

The Intrusion Detection System (IDS) is one of the technologies available to protect mobile ad hoc networks. The system monitors the network and detects intrusion from malicious nodes, aiming at passive (eavesdropping) or positive attack to disrupt the network. This paper proposes a new Intrusion detection system using three Machine Learning (ML) techniques. The ML techniques were Random Forest (RF), support vector machines (SVM), and Naïve Bayes(NB) were used to classify nodes in MANET. The data set was generated by the simulator network simulator-2 (NS-2). The routing protocol was used is Dynamic Source Routing (DSR). The type of IDS used is a Network Intrusion Detection System (NIDS). The dataset was pre-processed, then split into two subsets, 67% for training and 33% for testing employing Python Version 3.8.8. Obtaining good results for RF, SVM and NB when applied randomly selected features in the trial and error method from the dataset to improve the performance of the IDS and reduce time spent for training and testing. The system showed promising results, especially with RF, where the accuracy rate reached 100%.

‘Malicious software’ or malware has been a serious threat to the security and privacy of all mobile phone users. Due to the popularity of smartphones, primarily Android, this makes them a very viable target for spreading malware. In the past, many solutions have proved ineffective and have resulted in many false positives. Having the ability to identify and classify malware will help prevent them from spreading and evolving. In this paper, we study the effectiveness of the proposed classification of the malware family using a pixel level as features. This study has implemented well-known machine learning and deep learning classifiers such as K-Nearest Neighbours (k-NN), Support Vector Machine (SVM), Naïve Bayes (NB), Decision Tree, and Random Forest. A binary file of 25 malware families is converted into a fixed grayscale image. The grayscale images were then extracted transforming the size 100x100 into a single format into 100000 columns. During this phase, none of the columns are removed as to remain the patterns in each malware family. The experimental results show that our approach achieved 92% accuracy in Random Forest, 88% in SVM, 81% in Decision Tree, 80% in k-NN and 56% in Naïve Bayes classifier. Overall, the pixel-based feature also reveals a promising technique for identifying the family of malware with great accuracy, especially using the Random Forest classifier.

Energy security program which becomes the part of energy management must ensure the high reliability of the electric power transmission system so that the customer can be served very well. However, there are several problems that can hinder reliability achievement such as the long duration of fault recovery. On the other side, the prediction of fault recovery duration becomes a very challenging task. Because there are still few machine learning-based solution offer this paper proposes a machine learning-based strategy by using Naive-Bayes Classifier (NBC) and Support Vector Machine (SVM) in predicting the fault recovery duration class. The dataset contains 3398 rows of non-temporary-fault type records, six input features (Substation, Asset Type, Fault Category, Outage Start Time, Outage Day, and Outage Month) and single target feature (Fault Recovery Duration). According to the performance test result, those two methods reach around 97-99% of accuracy, average sensitivity, and average specificity. In addition, one of the advantages obtained in field of fault recovery prediction is increasing the accuracy of likelihood level calculation of the long fault recovery time risk.

Today, everyone is highly dependent on the internet. Everyone performed online shopping and online activities such as online Bank, online booking, online recharge and more on internet. Phishing is a type of website threat and phishing is Illegally on the original website Information such as login id, password and information of credit card. This paper proposed an efficient machine learning based phishing detection technique. Overall, experimental results show that the proposed technique, when integrated with the Support vector machine classifier, has the best performance of accurately distinguishing 95.66% of phishing and appropriate websites using only 22.5% of the innovative functionality. The proposed technique exhibits optimistic results when benchmarking with a range of standard phishing datasets of the “University of California Irvine (UCI)” archive. Therefore, proposed technique is preferred and used for phishing detection based on machine learning.

Distributed Denial of Service (DDoS) attacks have been one of the persistent forms of attacks on information technology infrastructure connected to a public network due to the ease of access to DDoS attack tools. Researchers have been able to develop several techniques to curb volumetric DDoS attacks which overwhelms the target with large number of request packets. However, compared to volumetric DDoS, low amount of research has been executed on mitigating slow DDoS. Data mining approaches and various Artificial Intelligence techniques have been proved by researchers to be effective for reduce DDoS attacks. This paper provides the scholarly community with slow DDoS attack detection techniques using Genetic Algorithm and Support Vector Machine aimed at mitigating slow DDoS attack in a Software-Defined Networking (SDN) environment simulated in GNS3. Genetic algorithm was employed to select the features which indicates the presence of an attack and also determine the appropriate regularization parameter, C, and gamma parameter for the Support Vector Machine classifier. Results obtained shows that the classifier had detection accuracy, Area Under Receiver Operating Curve (AUC), true positive rate, false positive rate and false negative rate of 99.89%, 99.89%, 99.95%, 0.18%, and 0.05% respectively. Also, the algorithm for subsequent implementation of the selective adaptive bubble burst mitigation mechanism was presented.

False data injection attack (FDIA) destroys the automatic generation control (AGC) system and leads to unstable operation of the power system. Fast and accurate detection can help prevent and disrupt malicious attacks. This paper proposes an improved detection method, which is combined with fuzzy theory and support vector machine (SVM) to identify various types of attacks. The impacts of different types of FDIAs on the AGC system are analyzed, and the reliability of the method is proved by a large number of experimental data. This experiment is simulated on a single-area LFC system and the effects of adding a wind storage system were compared in a dynamic model. Simulation studies also show a higher accuracy of fuzzy support vector machine (FSVM) than traditional SVM and fuzzy pattern trees (FPTs).

In 5G networks, the Cloud Radio Access Network (C-RAN) is considered a promising future architecture in terms of minimizing energy consumption and allocating resources efficiently by providing real-time cloud infrastructures, cooperative radio, and centralized data processing. Recently, given their vulnerability to malicious attacks, the security of C-RAN networks has attracted significant attention. Among various anomaly-based intrusion detection techniques, the most promising one is the machine learning-based intrusion detection as it learns without human assistance and adjusts actions accordingly. In this direction, many solutions have been proposed, but they show either low accuracy in terms of attack classification or they offer just a single layer of attack detection. This research focuses on deploying a multi-stage machine learning-based intrusion detection (ML-IDS) in 5G C-RAN that can detect and classify four types of jamming attacks: constant jamming, random jamming, deceptive jamming, and reactive jamming. This deployment enhances security by minimizing the false negatives in C-RAN architectures. The experimental evaluation of the proposed solution is carried out using WSN-DS (Wireless Sensor Networks DataSet), which is a dedicated wireless dataset for intrusion detection. The final classification accuracy of attacks is 94.51% with a 7.84% false negative rate.

Hyperspectral image is acquired with a special sensor in which the information is collected continuously. This sensor will provide abundant data from the scene captured. The high voluminous data in this image give rise to the extraction of materials and other valuable items in it. This paper proposes a methodology to extract rich information from the hyperspectral images. As the information collected in a contiguous manner, there is a need to extract spectral bands that are uncorrelated. A factor analysis based dimensionality reduction technique is employed to extract the spectral bands and a weight least square filter is used to get the spatial information from the data. Due to the preservation of edge property in the spatial filter, much information is extracted during the feature extraction phase. Finally, a nonlinear SVM is applied to assign a class label to the pixels in the image. The research work is tested on the standard dataset Indian Pines. The performance of the proposed method on this dataset is assessed through various accuracy measures. These accuracies are 96%, 92.6%, and 95.4%. over the other methods. This methodology can be applied to forestry applications to extract the various metrics in the real world.