Biblio

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.

Users prefer to do e-banking and e-shopping now-a-days because of the exponential growth of the internet. Because of this paradigm shift, hackers are finding umpteen ways to steal our personal information and critical details like details of debit and credit cards, by disguising themselves as reputed websites, just by changing the spelling or making minor modifications to the URL. Identifying whether an URL is benign or malicious is a challenging job, because it makes use of the weakness of the user. While there are several works carried out to detect phishing websites, they only use heuristic methods and list based techniques and therefore couldn't avoid phishing effectively. In this paper an anti-phishing system was proposed to protect the users. It uses an ensemble model that uses both LSTM and CNN with a massive data set containing nearly 2,00,000 URLs, that is balanced. After analyzing the accuracy of different existing approaches, it has been found that the ensemble model that uses both LSTM and CNN performed better with an accuracy of 96% and the precision is 97% respectively which is far better than the existing solutions.

Currently and particularly with remote working scenarios during COVID-19, phishing attack has become one of the most significant threats faced by internet users, organizations, and service providers. In a phishing attack, the attacker tries to steal client sensitive data (such as login, passwords, and credit card details) using spoofed emails and fake websites. Cybercriminals, hacktivists, and nation-state spy agencies have now got a fertilized ground to deploy their latest innovative phishing attacks. Timely detection of phishing attacks has become most crucial than ever. Machine learning algorithms can be used to accurately detect phishing attacks before a user is harmed. This paper presents a novel ensemble model to detect phishing attacks on the website. We select three machine learning classifiers: Artificial Neural Network (ANN), K-Nearest Neighbors (KNN), and Decision Tree (C4.5) to use in an ensemble method with Random Forest Classifier (RFC). This ensemble method effectively detects website phishing attacks with better accuracy than existing studies. Experimental results demonstrate that the ensemble of KNN and RFC detects phishing attacks with 97.33% accuracy.

Phishing URL is a type of cyberattack, based on falsified URLs. The number of phishing URL attacks continues to increase despite cybersecurity efforts. According to the Anti-Phishing Working Group (APWG), the number of phishing websites observed in 2020 is 1 520 832, doubling over the course of a year. Various algorithms, techniques and methods can be used to build models for phishing URL detection and classification. From our reading, we observed that Machine Learning (ML) is one of the recent approaches used to detect and classify phishing URL in an efficient and proactive way. In this paper, we evaluate eleven of the most adopted ML algorithms such as Decision Tree (DT), Nearest Neighbours (KNN), Gradient Boosting (GB), Logistic Regression (LR), Naïve Bayes (NB), Random Forest (RF), Support Vector Machines (SVM), Neural Network (NN), Ex-tra\_Tree (ET), Ada\_Boost (AB) and Bagging (B). To do that, we compute detection accuracy metric for each algorithm and we use lexical analysis to extract the URL features.

In the industrial control system, in order to solve the problem that the installation of smart devices in a transparent environment are faced with the unknown attack problems, because most of the industrial control equipment to detect unknown risks, Therefore, by studying the security protection of the current industrial control system and the trust mechanism that should be widely used in the Internet of things, this paper presents the abnormal node detection mode based on comprehensive trust applied to the industrial control system scenarios. This model firstly proposes a model, which fuses direct and indirect trust values into current trust values through support algorithm and vector similarity algorithm, and then combines them with historical trust values, and gives the calculation method of each trust value. Finally, a method to determine abnormal nodes based on comprehensive trust degree is given to realize a detection process for all industrial control nodes. By analyzing the real data case provided by Melbourne Water, it is concluded that this model can improve the detection range and detection accuracy of abnormal nodes. It can accurately judge and effectively resist malicious behavior and also have a good resistance to aggression.

The advent of IoT edge devices has enabled the collection of rich datasets, as part of Mobile Crowd Sensing (MCS), which has emerged as a key enabler for a wide gamut of safety-critical applications ranging from traffic control, environmental monitoring to assistive healthcare. Despite the clear advantages that such unprecedented quantity of data brings forth, it is also subject to inherent data trustworthiness challenges due to factors such as malevolent input and faulty sensors. Compounding this issue, there has been a plethora of proposed solutions, based on the use of traditional machine learning algorithms, towards assessing and sifting faulty data without any assumption on the trustworthiness of their source. However, there are still a number of open issues: how to cope with the presence of strong, colluding adversaries while at the same time efficiently managing this high influx of incoming user data. In this work, we meet these challenges by proposing the hybrid use of Deep Learning schemes (i.e., LSTMs) and conventional Machine Learning classifiers (i.e. One-Class Classifiers) for detecting and filtering out false data points. We provide a prototype implementation coupled with a detailed performance evaluation under various (attack) scenarios, employing both real and synthetic datasets. Our results showcase how the proposed solution outperforms various existing resilient aggregation and outlier detection schemes.

Vehicular Ad-Hoc Network (VANET) is a subcategory of Intelligent Transportation Systems (ITS) that allows vehicles to communicate with other vehicles and static roadside infrastructure. However, the integration of cyber and physical systems introduce many possible points of attack that make VANET vulnerable to cyber attacks. In this paper, we implemented a machine learning-based intrusion detection system that identifies False Data Injection (FDI) attacks on a vehicular network. A co-simulation framework between MATLAB and NS-3 is used to simulate the system. The intrusion detection system is installed in every vehicle and processes the information obtained from the packets sent by other vehicles. The packet is classified into either trusted or malicious using Support Vector Machines (SVM). The comparison of the performance of the system is evaluated in different scenarios using the following metrics: classification rate, attack detection rate, false positive rate, and detection speed. Simulation results show that the SVM-based IDS is able to provide high accuracy detection, low false positive rate, consequently improving the traffic congestion in the simulated highway.

Under the background of "Internet +", in order to solve the problem of deeply mining credit risk behind online supply chain financial big data, this paper proposes an online supply chain financial credit risk assessment method based on deep belief network (DBN). First, a deep belief network evaluation model composed of Restricted Boltzmann Machine (RBM) and classifier SOFTMAX is established, and the performance evaluation test of three kinds of data sets is carried out by using this model. Using factor analysis to select 8 indicators from 21 indicators, and then input them into RBM for conversion to form a more scientific evaluation index, and finally input them into SOFTMAX for evaluation. This method of online supply chain financial credit risk assessment based on DBN is applied to an example for verification. The results show that the evaluation accuracy of this method is 96.04%, which has higher evaluation accuracy and better rationality compared with SVM method and Logistic method.

A text is a meaningful source of information. Capturing the right patterns in written text gives metrics to measure and infer to what extent this text belongs or is relevant to a specific author. This research aims to introduce a new feature that goes more in deep in the language structure. The feature introduced is based on an attempt to differentiate stylistic changes among authors according to the different sentence structure each author uses. The study showed the effect of introducing this new feature to machine learning models to enhance their performance. It was found that the prediction of authors was enhanced by adding sentence structure as an additional feature as the f1\_scores increased by 0.3% and when normalizing the data and adding the feature it increased by 5%.

Social bots are computer algorithms able to produce content and interact with other users on social media autonomously, trying to emulate and possibly influence humans’ behavior. Indeed, bots are largely employed for malicious purposes, like spreading disinformation and conditioning electoral campaigns. Nowadays, bots’ capability of emulating human behaviors has become increasingly sophisticated, making their detection harder. In this paper, we aim at recognizing bot-driven accounts by evaluating the consistency of users’ writing style over time. In particular, we leverage the intuition that while bots compose posts according to fairly deterministic processes, humans are influenced by subjective factors (e.g., emotions) that can alter their writing style. To verify this assumption, by using stylistic consistency indicators, we characterize the writing style of more than 12,000 among bot-driven and human-operated Twitter accounts and find that statistically significant differences can be observed between the different types of users. Thus, we evaluate the effectiveness of different machine learning (ML) algorithms based on stylistic consistency features in discerning between human-operated and bot-driven Twitter accounts and show that the experimented ML algorithms can achieve high performance (i.e., F-measure values up to 98%) in social bot detection tasks.

The accurate classification of WiFi-based activity patterns is still an open problem and is critical to detect behavior for non-visualization applications. This paper proposes a novel approach that uses WiFi-based IQ data and short-time Fourier transform (STFT) time-frequency images to automatically and accurately classify human activities. The offsets features, calculated from time-domain values and one-dimensional principal component analysis (1D-PCA) values and two-dimensional principal component analysis (2D-PCA) values, are applied as features to input the classifiers. The machine learning methods such as the bagging, boosting, support vector machine (SVM), random forests (RF) as the classifier to output the performance. The experimental data validate our proposed method with 15000 experimental samples from five categories of WiFi signals (empty, marching on the spot, rope skipping, both arms rotating;singlearm rotating). The results show that the method companying with the RF classifier surpasses the approach with alternative classifiers on classification performance and finally obtains a 62.66% classification rate, 85.06% mean accuracy, and 90.67% mean specificity.

Threats, posed by ransomware, are rapidly increasing, and its cost on both national and global scales is becoming significantly high as evidenced by the recent events. Ransomware carries out an irreversible process, where it encrypts victims' digital assets to seek financial compensations. Adversaries utilize different means to gain initial access to the target machines, such as phishing emails, vulnerable public-facing software, Remote Desktop Protocol (RDP), brute-force attacks, and stolen accounts. To combat these threats of ransomware, this paper aims to help researchers gain a better understanding of ransomware application profiles through static analysis, where we identify a list of suspicious indicators and similarities among 727 active ran-somware samples. We start with generating portable executable (PE) metadata for all the studied samples. With our domain knowledge and exploratory data analysis tasks, we introduce some of the suspicious indicators of the structure of ransomware files. We reduce the dimensionality of the generated dataset by using the Principal Component Analysis (PCA) technique and discover clusters by applying the KMeans algorithm. This motivates us to utilize the one-class classification algorithms on the generated dataset. As a result, the algorithms learn the common data boundary in the structure of our studied ransomware samples, and thereby, we achieve the data-driven similarities. We use the findings to evaluate the trained classifiers with the test samples and observe that the Local Outlier Factor (LoF) performs better on all the selected feature spaces compared to the One-Class SVM and the Isolation Forest algorithms.

High-performance implementation of non-linear support vector machine (SVM) function is important in many applications. This paper develops a hardware design of Gaussian kernel function with high-performance since it is one of the most modules in non-linear SVM. The designed Gaussian kernel function consists of Norm unit and exponentiation function unit. The Norm unit uses fewer subtractors and multiplexers. The exponentiation function unit performs modified coordinate rotation digital computer algorithm with wide range of convergence and high accuracy. The presented circuit is implemented on a Xilinx field-programmable gate array platform. The experimental results demonstrate that the designed circuit achieves low resource utilization and high efficiency with relative error 0.0001.

In this study, it is aimed to reduce False-Alarm levels and increase the correct detection rate in order to reduce this uncertainty. Within the scope of the study, 13157 SQLi and XSS type malicious and 10000 normal HTTP Requests were used. All HTTP requests were received from the same web server, and it was observed that normal requests and malicious requests were close to each other. In this study, a novel approach is presented via both digitization and expressing the data with words in the data preprocessing stages. LSTM, MLP, CNN, GNB, SVM, KNN, DT, RF algorithms were used for classification and the results were evaluated with accuracy, precision, recall and F1-score metrics. As a contribution of this study, we can clearly express the following inferences. Each payload even if it seems different which has the same impact maybe that we can clearly view after the preprocessing phase. After preprocessing we are calculating euclidean distances which brings and gives us the relativity between expressions. When we put this relativity as an entry data to machine learning and/or deep learning models, perhaps we can understand the benign request or the attack vector difference.

Facial emotion recognition (FER) is useful in many different applications and could offer significant benefit as part of feedback systems to train children with Autism Spectrum Disorder (ASD) who struggle to recognize facial expressions and emotions. This project explores the potential of real time FER based on the use of local regions of interest combined with a machine learning approach. Histogram of Oriented Gradients (HOG) was implemented for feature extraction, along with 3 different classifiers, 2 based on k-Nearest Neighbor and 1 using Support Vector Machine (SVM) classification. Model performance was compared using accuracy of randomly selected validation sets after training on random training sets of the Oulu-CASIA database. Image classes were distributed evenly, and accuracies of up to 98.44% were observed with small variation depending on data distributions. The region selection methodology provided a compromise between accuracy and number of extracted features, and validated the hypothesis a focus on smaller informative regions performs just as well as the entire image.

State estimation algorithm ensures an effective realtime monitoring of the modern smart grid leading to an accurate determination of the current operating states. Recently, a new genre of data integrity attacks namely false data injection attack (FDIA) has shown its deleterious effects by bypassing the traditional bad data detection technique. Modern grid operators must detect the presence of such attacks in the raw field measurements to guarantee a safe and reliable operation of the grid. State forecasting based FDIA identification schemes have recently shown its efficacy by determining the deviation of the estimated states due to an attack. This work emphasizes on a scalable deep learning state forecasting model which can accurately determine the presence of FDIA in real-time. An optimal set of hyper-parameters of the proposed architecture leads to an effective forecasting of the operating states with minimal error. A diligent comparison between other state of the art forecasting strategies have promoted the effectiveness of the proposed neural network. A comprehensive analysis on the IEEE 14 bus test bench effectively promotes the proposed real-time attack identification strategy.

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.

Privacy-Preserving Data Mining (PPDM) has become an exciting topic to discuss in recent decades due to the growing interest in big data and data mining. A technique of securing data but still preserving the privacy that is in it. This paper provides an alternative perturbation-based PPDM technique which is carried out by modifying the RNP algorithm. The novelty given in this paper are modifications of some steps method with a specific purpose. The modifications made are in the form of first narrowing the selection of the disturbance value. With the aim that the number of attributes that are replaced in each record line is only as many as the attributes in the original data, no more and no need to repeat; secondly, derive the perturbation function from the cumulative distribution function and use it to find the probability distribution function so that the selection of replacement data has a clear basis. The experiment results on twenty-five perturbed data show that the modified RNP algorithm balances data utility and security level by selecting the appropriate disturbance value and perturbation value. The level of security is measured using privacy metrics in the form of value difference, average transformation of data, and percentage of retains. The method presented in this paper is fascinating to be applied to actual data that requires privacy preservation.

The most widely used method to prevent adversaries from eavesdropping on sensitive sensor, robot, and war fighter communications is mathematically strong cryptographic algorithms. However, prevailing cryptographic protocol mandates are often made without consideration of resource constraints of devices in the internet of Battlefield Things (IoBT). In this article, we address the challenges of IoBT sensor data exchange in contested environments. Battlefield IoT (Internet of Things) devices need to exchange data and receive feedback from other devices such as tanks and command and control infrastructure for analysis, tracking, and real-time engagement. Since data in IoBT systems may be massive or sparse, we introduced a machine learning classifier to determine what type of data to transmit under what conditions. We compared Support Vector Machine, Bayes Point Match, Boosted Decision Trees, Decision Forests, and Decision Jungles on their abilities to recommend the optimal confidentiality preserving data and transmission path considering dynamic threats. We created a synthesized dataset that simulates platoon maneuvers and IED detection components. We found Decision Jungles to produce the most accurate results while requiring the least resources during training to produce those results. We also introduced the JointField blockchain network for joint and allied force data sharing. With our classifier, strategists, and system designers will be able to enable adaptive responses to threats while engaged in real-time field conflict.

Cloud Computing is a favored choice of any IT organization in the current context since that provides flexibility and pay-per-use service to the users. Moreover, due to its open and inclusive architecture which is accessible to attackers. Security and privacy are a big roadblock to its success. For any IT organization, intrusion detection systems are essential to the detection and endurance of effective detection system against attacker aggressive attacks. To recognize minor occurrences and become significant breaches, a fully managed intrusion detection system is required. The most prevalent approach for intrusion detection on the cloud is the Intrusion Detection System (IDS). This research introduces a cloud-based deep learning-LSTM IDS model and evaluates it to a hybrid Stacked Contractive Auto Encoder (SCAE) + Support Vector Machine (SVM) IDS model. Deep learning algorithms like basic machine learning can be built to conduct attack detection and classification simultaneously. Also examine the detection methodologies used by certain existing intrusion detection systems. On two well-known Intrusion Detection datasets (KDD Cup 99 and NSL-KDD), our strategy outperforms current methods in terms of accurate detection.

Intellectual Property Rights (IPR) results from years of research and wisdom by property owners, and it plays an increasingly important role in promoting economic development, technological progress, and cultural prosperity. Thus, we need to strengthen the degree of protection of IPR. However, as internet technology continues to open up the market for IPR, the ease of network operation has led to infringement of IPR in some cases. Intellectual property infringement has occurred in some cases. Also, Internet development's concealed and rapid nature has led to the fact that IPR infringers cannot be easily detected. This paper addresses how to protect the rights and interests of IPR holders in the context of the rapid development of the internet. This paper explains the IPR and proposes an algorithm to enhance security for a better security model to protect IPR. This proposes optimization techniques to detect intruder attacks for securing IPR, by using support vector machines (SVM), it provides better results to secure public and private intellectual data by optimizing technologies.

Digital images are becoming the backbone of the social platform. To day of life of the people, the high impact of the images has raised the concern of its authenticity. Image forensics need to be done to assure the authenticity. In this paper, a novel technique is proposed for digital image forensics. The proposed technique is applied for detection of median, averaging and Gaussian filtering in the images. In the proposed method, a first image is normalized using optimal range to obtain a better statistical information. Further, difference arrays are calculated on the normalized array and a proposed thresholding is applied on the normalized arrays. In the last, co-occurrence features are extracted from the thresholding difference arrays. In experimental analysis, significant performance gain is achieved. The detection capability of the proposed method remains upstanding on small size images even with low quality JPEG compression.

In the recent time due to advancement of technology, Malware and its clan have continued to advance and become more diverse. Malware otherwise Malicious Software consists of Virus, Trojan horse, Adware, Spyware etc. This said software leads to extrusion of data (Spyware), continuously flow of Ads (Adware), modifying or damaging the system files (Virus), or access of personal information (Trojan horse). Some of the major factors driving the growth of these attacks are due to poorly secured devices and the ease of availability of tools in the Internet with which anyone can attack any system. The attackers or the developers of Malware usually lean towards blending of malware into the executable file, which makes it hard to detect the presence of malware in executable files. In this paper we have done experimental study on various algorithms of Machine Learning for detecting the presence of Malware in executable files. After testing Naïve Bayes, KNN and SVM, we found out that SVM was the most suited algorithm and had the accuracy of 94%. We then created a web application where the user could upload executable file and test the authenticity of the said executable file if it is a Malware file or a benign file.

At present, machine learning (ML) algorithms are essential components in designing the sophisticated intrusion detection system (IDS). They are building-blocks to enhance cyber threat detection and help in classification at host-level and network-level in a short period. The increasing global connectivity and advancements of network technologies have added unprecedented challenges and opportunities to network security. Malicious attacks impose a huge security threat and warrant scalable solutions to thwart large-scale attacks. These activities encourage researchers to address these imminent threats by analyzing a large volume of the dataset to tackle all possible ranges of attack. In this proposed method, we calculated the fitness value of each feature from the population by using a genetic algorithm (GA) and selected them according to the fitness value. The fitness values are presented in hierarchical order to show the effectiveness of problem decomposition. We implemented Support Vector Machine (SVM) to verify the consistency of the system outcome. The well-known NSL-knowledge discovery in databases (KDD) was used to measure the performance of the system. From the experiments, we achieved a notable classification accuracies using a SVM of the current state of the art intrusion detection.

The extraordinary number of alerts generated by network intrusion detection systems (NIDS) can desensitize security analysts tasked with incident response. Security information and event management systems (SIEMs) perform some rudimentary automation but cannot replicate the decision-making process of a skilled analyst. Machine learning and artificial intelligence (AI) can detect patterns in data with appropriate training. In practice, the majority of the alert data comprises false alerts, and true alerts form only a small proportion. Consequently, a naive engine that classifies all security alerts into the majority class can yield a superficial high accuracy close to 100%. Without any correction for the class imbalance, the false alerts will dominate algorithmic predictions resulting in poor generalization performance. We propose a machine-learning approach to address the class imbalance problem in multi-appliance security alert data and automate the security alert analysis process performed in security operations centers (SOCs). We first used the neighborhood cleaning rule (NCR) to identify and remove ambiguous, noisy, and redundant false alerts. Then, we applied the support vector machine synthetic minority oversampling technique (SVMSMOTE) to generate synthetic training true alerts. Finally, we fit and evaluated the decision tree and random forest classifiers. In the experiments, using alert data from eight security appliances, we demonstrated that the proposed method can significantly reduce the need for manual auditing, decreasing the number of uninspected alerts and achieving a performance of 99.524% in recall.