Types Of Cyber-attacks Research Articles

Insights into Organizational Security Readiness: Lessons Learned from Cyber-Attack Case Studies

This paper focuses on understanding the characteristics of multiple types of cyber-attacks through a comprehensive evaluation of case studies of real-world cyber-attacks. For each type of attack, we identify and link the attack type to the characteristics of that attack and the factors leading up to the attack, as observed from the review of case studies for that type of attack. We explored both the quantitative and qualitative characteristics for the types of attacks, including the type of industry, the financial intensity of the attack, non-financial intensity impacts, the number of impacted customers, and the impact on users’ trust and loyalty. In addition, we investigated the key factors leading up to an attack, including the human behavioral aspects; the organizational–cultural factors at play; the security policies adapted; the technology adoption and investment by the business; the training and awareness of all stakeholders, including users, customers and employees; and the investments in cybersecurity. In our study, we also analyzed how these factors are related to each other by evaluating the co-occurrence and linkage of factors to form graphs of connected frequent rules seen across the case studies. This study aims to help organizations take a proactive approach to the study of relevant cyber threats and aims to educate organizations to become more knowledgeable through lessons learned from other organizations experiencing cyber-attacks. Our findings indicate that the human behavioral aspects leading up to attacks are the weakest link in the successful prevention of cyber threats. We focus on human factors and discuss mitigation strategies.

Improving the Accuracy of Network Intrusion Detection with Causal Machine Learning

In recent years, machine learning (ML) algorithms have been approved effective in the intrusion detection. However, as the ML algorithms are mainly applied to evaluate the anomaly of the network, the detection accuracy for cyberattacks with multiple types cannot be fully guaranteed. The existing algorithms for network intrusion detection based on ML or feature selection are on the basis of spurious correlation between features and cyberattacks, causing several wrong classifications. In order to tackle the abovementioned problems, this research aimed to establish a novel network intrusion detection system (NIDS) based on causal ML. The proposed system started with the identification of noisy features by causal intervention, while only the features that had a causality with cyberattacks were preserved. Then, the ML algorithm was used to make a preliminary classification to select the most relevant types of cyberattacks. As a result, the unique labeled cyberattack could be detected by the counterfactual detection algorithm. In addition to a relatively stable accuracy, the complexity of cyberattack detection could also be effectively reduced, with a maximum reduction to 94% on the size of training features. Moreover, in case of the availability of several types of cyberattacks, the detection accuracy was significantly improved compared with the previous ML algorithms.

Investigating Various Approaches and Ways to Detect Cybercrime

Cyber security is one of the major concepts of cyber world which provides protection to the cyberspace from various sorts of cybercrimes. This research paper provides detailed information of cybersecurity. We conduct the survey of security of recent prominent researches and categorise the recent incidents in context to varied fundamental principles of cybersecurity. We have proposed a new taxonomy of cybercrime which can cover all types of cyberattacks. We have analysed multiple cyberattacks in accordance with the updated cybercrime taxonomy to highlight the challenges in the field of cybersecurity and future research directions added in this field. In India, most of the cases are found where crimes are committed due to lack of data or by mistake. In this paper, I even have discussed various categories and cases of cybercrime which is committed thanks to lack of data or sometimes thanks to intention behind. In India also, suggested various preventive measures against these unlawful acts in day-to-day life. Now day-by-day cybercrime has increased, and criminal finds new ways of crime, use new techniques for crime completion. Here, we have mentioned techniques to solve cases. We have researched various approaches for detection of cybercrime. In this paper, we have provided the details.

A review of phishing attacks and countermeasures for internet of things‐based smart business applications in industry 4.0

Due to the advancement in industry 4.0, cybercrimes in internet-based business applications are rapidly increasing. To encounter such attacks, the intruders use various tools and techniques such as Phishing, Malware, SQL Injection, Ransomware, Cross-Site Scripting (XSS), Denial of Service (DoS), Session Hijacking, and Credential Reuse. Phishing is most commonly in practice in the current digital era. Phishing is a fraudulent attempt to obtain sensitive information from deceiving users. These types of cyber-attacks are used to harm individuals or entire businesses depending on the targeted objective of the attacker. To combat these attacks, the research community provides various phishing detection techniques. In this article, we classify state-of-the-art fishing detection techniques in five major categories, that are, (1) Data Mining, Deep Learning & Machine Learning-Based Approach, (2) Search Engine-Based Approach, (3) URL Scan-Based Approach, (4) Blacklisting-Whitelisting Approach, and (5) Visual Similarity-Based Approach. Further, a comparative analysis is provided within each category concerning their strength and weaknesses. Moreover, the article provides phishing prevention best practices to the readers to avoid such attacks.

Cyberattacks, cyber threats, and attitudes toward cybersecurity policies

Abstract Does exposure to cyberattacks influence public support for intrusive cybersecurity policies? How do perceptions of cyber threats mediate this relationship? While past research has demonstrated how exposure to cyberattacks affects political attitudes, the mediating role played by threat perception has been overlooked. This study employs a controlled randomized survey experiment design to test the effect of exposure to lethal and nonlethal cyberattacks on support for different types of cybersecurity policies. One thousand twenty-two Israeli participants are exposed to scripted and simulated television reports of lethal or nonlethal cyberattacks against national infrastructure. Findings suggest that exposure to cyberattacks leads to greater support for stringent cybersecurity regulations, through a mechanism of threat perception. Results also indicate that different types of exposure relate to heightened support for different types of regulatory policies. People exposed to lethal cyberattacks tend to support cybersecurity policies that compel the government to alert citizens about cyberattacks. People who were exposed to nonlethal attacks, on the other hand, tend to support oversight policies at higher levels. More broadly, our research suggests that peoples’ willingness to accept government cybersecurity policies that limit personal civil liberties and privacy depends on the type of cyberattacks to which they were exposed and the perceptions associated with such exposure.

Human Factors in Phishing Attacks: A Systematic Literature Review

Phishing is the fraudulent attempt to obtain sensitive information by disguising oneself as a trustworthy entity in digital communication. It is a type of cyber attack often successful because users are not aware of their vulnerabilities or are unable to understand the risks. This article presents a systematic literature review conducted to draw a “big picture” of the most important research works performed on human factors and phishing. The analysis of the retrieved publications, framed along the research questions addressed in the systematic literature review, helps in understanding how human factors should be considered to defend against phishing attacks. Future research directions are also highlighted.

Preventing Medjacking.

Advances in medical device technology have led to greater connectivity with other devices, networks, and systems, raising concerns about associated security risks. Many clinicians, as well as the patients who rely on these lifesaving devices, are unaware of these risks and how they can be mitigated. This article describes several types of cyberattacks, including medjacking, hacking, and ransomware, and what nurses can do to guard against security threats and educate patients.

An extensive review of data security infrastructure and legislature

AbstractThe last few decades have witnessed technology progress with leaps and bounds leading to the creation and global adoption of different types of digital devices and platforms that can make personalized recommendations to an individual. One of the consequences of the ubiquity of these devices is the daily generation of data in large quantities. This data includes the sensitive data of an individual as well as multinational organizations and therefore, it must be always kept confidential to prevent its theft and misuse by malicious parties. However, the large volumes of data generated make it difficult to create a robust security solution to safeguard the data from different types of cyberattacks. The paper contributes to the data security industry by consolidating numerous data security algorithms that change with the infrastructure that the data is amid and also outlines the regulations surrounding it. This review paper also aims to highlight the benefits and drawbacks of the security algorithms proposed by researchers to encourage further discussion and consideration of the algorithms for their potential implementation in appropriate domains and also inspire them to develop more powerful and robust security algorithms to cover the drawbacks of the existing ones.

A Review on Security of Smart Farming and Precision Agriculture: Security Aspects, Attacks, Threats and Countermeasures

In recent years, Smart Farming (SF) and Precision Agriculture (PA) have attracted attention from both the agriculture industry as well as the research community. Altogether, SF and PA aim to help farmers use inputs (such as fertilizers and pesticides) more efficiently through using Internet of Things (IoT) devices, but in doing so, they create new security threats that can defeat this purpose in the absence of adequate awareness and proper countermeasures. A survey on different security-related challenges is required to raise awareness and pave they way for further research in this area. In this paper, we first itemize the security aspects of SF and PA. Next, we review the types of cyber attacks that can violate each of these aspects. Accordingly, we present a taxonomy on cyber-threats to SF and PA on the basis of their relations to different stages of Cyber-Kill Chain (CKC). Among cyber-threats, we choose Advanced Persistent Threats (APTs) for further study. Finally, we studied related risk mitigation strategies and countermeasure, and developed a future road map for further study in this area. This paper’s main contribution is a categorization of security threats within the SF/PA areas and provide a taxonomy of security threats for SF environments so that we may detect the behavior of APT attacks and any other security threat in SF and PA environments.

Implementasi Metode K-Medoids Untuk Masalah Intrusion Detection System Menggunakan Bahasa Pemrograman Matlab

Based on data from the National Cyber And Crypto Agency (BSSN) of the Republic of Indonesia from 2018 to 2021, the threat of cyber attacks continues to experience a significant increase. In 2021, a significant change that is likely to be faced is with the emergence of new smart devices, which are more than just end-users and remotely connected networked devices. Surely, gives it the attention of all parties. There are many types of cyberattacks including Malware, Phishing, Ransomeware, etc. IDS (Intrusion Detection System) is a method that can detect suspicious activity in a system or network. Implementation of the Fuzzy K-Medoids method by using the Matlab programming language that retrieves data from KDDCUP’99 which has been normalized. The data used are normal data and anomaly attack data which are categorized as DoS, Probe, R2L, and U2R. From the research conducted the accuracy percentage is around 60-89% with three types of data preprocessing

Criminal law and criminological threats in the Internet during a pandemic

A general description of criminal law and criminological threats on the Internet, which have become particularly common or arose during the pandemic in Ukraine and the world have been analyzed in the scientific paper. It is noted that during this pandemic people began to rely more than ever on computer systems, mobile devices and the Internet for remote working, education, communication, shopping, sharing and receiving information, minimization the influence of the social distancing, etc. Increasing of the number of Internet users and the time spent them online, combined with the feelings of confusion, anxiety and fear has become an enabling environment for criminal activity in the cyber space. The risks associated with the digitalization of health care in Ukraine and abroad have been identified. It is noted, that Interpol has identified three the most common types of cyberattacks at the international level, which affected individuals and legal entities during the pandemic: 1) the functioning of malicious domains; 2) distribution of malicious software; 3) download extortion programs. Phishing and other types of cyber fraud, related to COVID-19, were analyzed. It is emphasized, that the perception of fake information (depending on its content) can lead to the panic spreading among the population, the formation of aggression against certain groups of people and so on. It was determined, that during the COVID-19 pandemic, the increasing of the amount of "screen time" among both adults and children contributed to the intensification of actions aimed at sexual abuse of children on the Internet, cyberbullying, incitement to risky online behavior, distribution of potentially dangerous content. It is stated, that according to experts opinion, the implementation of criminal law and criminological threats on the Internet in 2020 caused losses of more than 1% of world GDP.

Secure Cyber Defense: An Analysis of Network Intrusion-Based Dataset CCD-IDSv1 with Machine Learning and Deep Learning Models

Anomaly detection and multi-attack classification are major concerns for cyber defense. Several publicly available datasets have been used extensively for the evaluation of Intrusion Detection Systems (IDSs). However, most of the publicly available datasets may not contain attack scenarios based on evolving threats. The development of a robust network intrusion dataset is vital for network threat analysis and mitigation. Proactive IDSs are required to tackle ever-growing threats in cyberspace. Machine learning (ML) and deep learning (DL) models have been deployed recently to detect the various types of cyber-attacks. However, current IDSs struggle to attain both a high detection rate and a low false alarm rate. To address these issues, we first develop a Center for Cyber Defense (CCD)-IDSv1 labeled flow-based dataset in an OpenStack environment. Five different attacks with normal usage imitating real-life usage are implemented. The number of network features is increased to overcome the shortcomings of the previous network flow-based datasets such as CIDDS and CIC-IDS2017. Secondly, this paper presents a comparative analysis on the effectiveness of different ML and DL models on our CCD-IDSv1 dataset. In this study, we consider both cyber anomaly detection and multi-attack classification. To improve the performance, we developed two DL-based ensemble models: Ensemble-CNN-10 and Ensemble-CNN-LSTM. Ensemble-CNN-10 combines 10 CNN models developed from 10-fold cross-validation, whereas Ensemble-CNN-LSTM combines base CNN and LSTM models. This paper also presents feature importance for both anomaly detection and multi-attack classification. Overall, the proposed ensemble models performed well in both the 10-fold cross-validation and independent testing on our dataset. Together, these results suggest the robustness and effectiveness of the proposed IDSs based on ML and DL models on the CCD-IDSv1 intrusion detection dataset.

Deep Transfer Learning Based Intrusion Detection System for Electric Vehicular Networks.

The Controller Area Network (CAN) bus works as an important protocol in the real-time In-Vehicle Network (IVN) systems for its simple, suitable, and robust architecture. The risk of IVN devices has still been insecure and vulnerable due to the complex data-intensive architectures which greatly increase the accessibility to unauthorized networks and the possibility of various types of cyberattacks. Therefore, the detection of cyberattacks in IVN devices has become a growing interest. With the rapid development of IVNs and evolving threat types, the traditional machine learning-based IDS has to update to cope with the security requirements of the current environment. Nowadays, the progression of deep learning, deep transfer learning, and its impactful outcome in several areas has guided as an effective solution for network intrusion detection. This manuscript proposes a deep transfer learning-based IDS model for IVN along with improved performance in comparison to several other existing models. The unique contributions include effective attribute selection which is best suited to identify malicious CAN messages and accurately detect the normal and abnormal activities, designing a deep transfer learning-based LeNet model, and evaluating considering real-world data. To this end, an extensive experimental performance evaluation has been conducted. The architecture along with empirical analyses shows that the proposed IDS greatly improves the detection accuracy over the mainstream machine learning, deep learning, and benchmark deep transfer learning models and has demonstrated better performance for real-time IVN security.

An Effective Model for Fraud Risk Management in Mitigating Telecommunication Fraud Incidences

In the context of Malaysia, the most common types of cyber attacks are denial of service, intrusion attempts, spam, vulnerability report, fraud, malicious code, and content-related attacks as reported by Cyber Security Malaysia. However, since 2011, cyber attacks such as intrusion attempts, denial of service and spam are decreasing steadily as a result of security measures that have been taken by companies or internet users. Nevertheless, cyber attacks which are steadily increasing are cyber harassment, intrusion and fraud. Approximately 5,328 fraud incidences were reported in 2011 while in 2020, the number of cases rose to 7,593 cases. Out of these cyber attacks in Malaysia, the most proliferating ones have been identified as fraud incidences. Thus, it is highly needed to come up with and propose the best fraud risk management strategy to handle fraud incidences among internet users and internet providers through the utilization of business intelligence tools, and quality enhancement via quality systems in place, quality information, and quality users. With these tools in hand, it is hoped that the proposed model will serve as a framework to mitigate and/or prevent the occurrence of fraud incidences.

Artificial Intelligence (AI)-Empowered Intrusion Detection Architecture for the Internet of Vehicles

Recent advances in the Internet of Things (IoT) and the adoption of IoT in vehicular networks have led to a new and promising paradigm called the Internet of Vehicles (IoV). However, the mode of communication in IoV being wireless in nature poses serious cybersecurity challenges. With many vehicles being connected in the IoV network, the vehicular data is set to explode. Traditional intrusion detection techniques may not be suitable in these scenarios with an extremely large amount of vehicular data being generated at an unprecedented rate and with various types of cybersecurity attacks being launched. Thus, there is a need for the development of advanced intrusion detection techniques capable of handling possible cyberattacks in these networks. Toward this end, we present an artificial intelligence (AI)-based intrusion detection architecture comprising Deep Learning Engines (DLEs) for identification and classification of the vehicular traffic in the IoV networks into potential cyberattack types. Also, taking into consideration the mobility of the vehicles and the realtime requirements of the IoV networks, these DLEs will be deployed on Multi-access Edge Computing (MEC) servers instead of running on the remote cloud. Extensive experimental results using popular evaluation metrics and average prediction time on a MEC testbed demonstrate the effectiveness of the proposed scheme.

A Novel Machine Learning-Based Framework for Optimal and Secure Operation of Static VAR Compensators in EAFs

A static VAR compensator (SVC) is a critical component for reactive power compensation in electric arc furnaces (EAFs) that is used to relieve the flicker impacts and maintain the voltage level. A weak voltage profile can not only reduce the power-quality services, but can also result in system instability in severe cases. The cybersecurity of EAFs is becoming a significant concern due to their cyber-physical structure. The reliance of SVC controllers on reactive power measurement and network communications has resulted in a cyber-vulnerability point for unauthorized access to the EAF, which can affect its normal operation. This paper addresses concerns about cyber attacks on EAFs, which can cause network communication issues in measurement data for SVCs. Three significant and different types of cyber attacks that are launched on SVC controllers—a replay attack, delay attack, and false data injection attack (FDIA)—were simulated and investigated. In order to stop the activities of cyber attacks, a secured anomaly detection model (ADM) based on a prediction interval is proposed. The proposed model is dependent on a support vector regression and a new smooth cost function for constructing the optimal and symmetrical intervals. A modified algorithm based on teaching–learning-based optimization was developed to adapt the ADM’s parameters during training. The simulation’s outcomes on a genuine dataset showed the strong capability of the proposed model against cyber attacks in EAFs.

Entropy-Based Evaluation of DNS Activity for Threat Hunting

The paper documents, based mainly on published papers where a consistent mathematical description of cyberspace and various types of Cyber-Attacks and protection measures are presented, a mathematical approach for Cyber Threat Hunting using Domain Name System (DNS) observations. After referring to the various Advanced Persistent Threat (APT) hunting techniques we propose a high level, mainly, entropy-based technique for detecting the existence of various threat vectors in our networks, demystifying DNS Anomalies. Keywords: Domain Name System (DNS), Advanced Persistent Threat (APT) actors, Entropy, Anomaly Detection.

Secured Banking Authentication by Pixel Correlation for Online Shopping

This paper presents a secure way for bank transaction during online shopping with the help of graphical passwords that is image processing.The project's aim is to assist users in choosing and constructing a stronger password that cannot be easily compromised. This paper also talks about the clustering and recommendation of items that are bought by the user’s during online shopping. Item based recommendation is used so that unwanted items that are not relevant to the user’s shopping arenot recommended [Noise recommendation]. Multilevel clustering is used to cluster all the items and then group them. This project helps to avoid phishing attack.The attacker uses phishing e-mails to send different types of links or attachments that can be compromised, including credential details such as login details or account information of the user, in this type of cyber-attack.This method was introduced to overcome textual passwords, pins and other trivial passwords methods which were difficult to remember and prone to external attack. When a client forgets their password, they must create a new one that is both difficult to remember and easy to hack. A graphical password is easier to remember than a text password, and users can generate their own passwords using images. As a result, some researchers have proposed using a graphical password instead of a text-based one. As a result, several researchers have suggested graphical password as an alternative to text-based password. The user will select a particular part of the screen to confirm the process. We propose a framework having pixel correlation for user verification based on double hashing.

Distributed denial‐of‐service attack mitigation in network functions virtualization‐based 5G networks using management and orchestration

SummaryThe fifth generation (5G) technology is expected to allow connectivity to billions of devices, known as Internet of Things (IoT). However, IoT devices will inevitably be the main target of various cyberattack types. The most common one is known as distributed denial‐of‐service (DDoS) attack. In order to mitigate such attacks, network functions virtualization (NFV) has a great potential to provide the benefit of elasticity and low‐cost solutions for protecting 5G networks. In this context, this study proposes a new mechanism developed to mitigate DDoS attacks in 5G NFV networks. The proposed mechanism utilizes intrusion prevention system's (IPS) virtual machines (VMs) to intercept the queries. Based on the volume of DDoS traffic, IPS's VMs are dynamically deployed by means of management and orchestration (MANO) in order to balance the load. To evaluate the effectiveness of the mechanism, experiments are conducted in a real 5G NFV environment built by using 5G NFV environment tools. To our best knowledge, this is the first time that NFV‐based mechanism is experimentally tested in a real 5G NFV environment for mitigating DDoS attacks in 5G networks. The experimental results verify that the proposed mechanism can mitigate DDoS attacks effectively.

Robust and Resilient Distributed Optimal Frequency Control for Microgrids Against Cyber Attacks

The optimal frequency control of autonomous microgrids (MGs), i.e., to achieve fast frequency recovery and dynamic power adjustment of the distributed generators in proportion to predefined participation factors, can be achieved in a fully distributed way based on the subgradient consensus protocol. However, such a distributively controlled MG is susceptible to different types of cyber attacks infiltrated from different locations. In this article, a robust and resilient distributed optimal frequency control scheme is proposed to address the threat of cyber attacks. It is facilitated by introducing an auxiliary networked system interconnecting with the original cooperative control system. On condition that the cyber attacks are within certain ranges, the robust design can maintain the functionalities by significantly attenuating the impact. Otherwise, the cyber attacks can be easily detected, and resilient reactions can be taken to mitigate their influences via isolation. Simulation results in a modified IEEE 34-bus MG validate the effectiveness of the proposed approach.