Prevent Cyber Attacks Research Articles

Design of Metaheuristic Optimization Algorithms for Deep Learning Model for Secure IoT Environment

Because of the rise in the number of cyberattacks, the devices that make up the Internet of Things (IoT) environment are experiencing increased levels of security risks. In recent years, a significant number of centralized systems have been developed to identify intrusions into the IoT environment. However, due to diverse requirements of IoT devices such as dispersion, scalability, resource restrictions, and decreased latency, these strategies were unable to achieve notable outcomes. The present paper introduces two novel metaheuristic optimization algorithms for optimizing the weights of deep learning (DL) models, use of DL may help in the detection and prevention of cyberattacks of this nature. Furthermore, two hybrid DL classifiers, i.e., convolutional neural network (CNN) + deep belief network (DBN) and bidirectional long short-term memory (Bi-LSTM) + gated recurrent network (GRU), were designed and tuned using the already proposed optimization algorithms, which results in ads to improved model accuracy. The results are evaluated against the recent approaches in the relevant field along with the hybrid DL classifier. Model performance metrics such as accuracy, rand index, f-measure, and MCC are used to draw conclusions about the model’s validity by employing two distinct datasets. Regarding all performance metrics, the proposed approach outperforms both conventional and cutting-edge methods.

Hackers of critical infrastructure: expectations and limits of the principle of target distinction

ABSTRACT I explore reasons why existing defense has failed to prevent cyber attacks on critical infrastructure. I study one of the least studied notions of cyberspace behavior known as target distinction. Drawn from customary international law, the principle posits that states should tell their wartime targets between combatants and noncombatants and use force only toward military objects. States should not target critical infrastructure, like gas pipelines, because to do so harms civilian populations who use it. I investigate four issues that keeps the principle from preventing attacks on critical infrastructure. The first is its inability to capture the networked nature of critical infrastructure beyond the simple dual-use (military and cyber) purposes. The second defect is the interpretive confusion that the principle generates over the rules of engagement. The third problem is the omission from its coverage of actors other than nation states. By design, the principle condones cyber attacks by nonstate actors on infrastructure, or by those whose linkage to state sponsors cannot be legally established. Finally, the principle is prone to fail when hackers lack proper understanding of what it does and does not allow.

Preventing cyber attacks using artificial intelligence

This paper explores the utilization of artificial intelligence (AI) in preventing cyber attacks across various domains of cybersecurity. It discusses different applications of AI in cybersecurity, including intrusion detection and prevention, threat hunting, vulnerability assessment, and fraud detection. Various AI techniques, such as anomaly detection, machine learning, natural language processing, and predictive analysis, are highlighted within each application area. The paper examines specific AI-driven cyber-attack methods such as phishing attacks, voice impersonation, and the use of chatbots. The study concludes with a comparative analysis of intrusion detection methods, highlighting the effectiveness of AI-driven approaches in identifying intruders and instances of attacks, especially those with unique characteristics. The paper underscores the importance of using AI in cybersecurity defense strategies to combat evolving cyber threats effectively.

Cyber security and artificial intelligence

Cyber security is one of the key challenges in the age of information technology, which is particularly important in the financial sector, where security is key both for customers and institutions. Data protection, fighting fraud and preventing cyber attacks are areas in which artificial intelligence and automated systems can provide significant assistance. The use of AI and machine learning for cybersecurity allows systems to be recovered quickly and effectively after a cyber attack. Using AI algorithms, experts can immediately assess damage and respond to cyber incidents with AI. In the paper the support of cyber protection with artificial intelligence is presented in the finance sector. There are major overlaps with infrastructural protection, individual security levels and proper data protection.

Вызовы в реализации систем глубокого анализа сетевого трафика методом полного протокольного декодирования

This paper presents a summary of experience in developing the deep packet inspection system using full protocol decoding. The paper reviews the challenges encountered during implementation and provides a high-level overview of the solutions to these issues. The challenges can be grouped into two groups. The first group is related to the fundamental tasks which must be addressed when implementing full protocol decoding systems. This includes ensuring correct protocol parsing, which involves identifying and interpreting protocol headers and fields correctly. Moreover, it is necessary to ensure the processing of fragmented packets and the assembly of fragments into the original message. Additionally, the processing and analysis of encrypted traffic is a crucial task that may require the use of specialized algorithms and tools. The second group of problems is related to optimizing the process of full protocol decoding to ensure high-speed traffic processing, as well as supporting new protocols and the ability to add user-defined extensions. While there are open-source systems that address some of the primary issues associated with full protocol decoding, there may be a need for additional effort and specialized solutions to efficiently operate and expand the functionality of such systems. Although implementing deep network traffic analysis tools using full protocol decoding requires the use of advanced hardware and software technologies, the benefits of such analysis are significant. This approach provides a more complete understanding of network traffic patterns and enables more effective detection and prevention of cyber-attacks. It also allows for more accurate monitoring of network performance and the identification of potential bottlenecks or other issues that may impact network efficiency. In this article, we also emphasize the importance of system architecture development and implementation to ensure the successful deployment of deep network traffic analysis tools using full protocol decoding. At last, we conducted an experiment where several advanced optimizations were implemented in the system that had already solved primary issues. These optimizations related to working with memory, based on the features of the traffic processing scheme. By results, we evaluated significant performance improvement in solving secondary tasks, described in this work.

A Systematic Evaluation of Artificial Intelligence's Impact on the Landscape of Threat Modeling

This article systematically evaluates the influence of Artificial Intelligence (AI) on the landscape of Threat modelingin cybersecurity. The study involves an extensive review of relevant journal articles, books, and conference papers to comprehensively assess the current state of the field. By synthesizing existing literature, we identify and analyze the ways in which AItechnologies are applied in Threat modelingmethodologies. The evaluation explores the strengths and limitations of these applications, shedding light on the advancements that have significantly enhancedThreat modeling.Furthermore, the research includes a meticulous gap analysis within the existing literature, revealing areas where further investigation is warranted. Identified gaps in the current research landscape serve as a foundation for proposing future research directions in AI-enhanced Threat modeling. The current literature related to the current landscape of Artificial Intelligence research in cybersecurity predominantly focuses on articles and active studies pertaining to the detection and prevention of cyber-attacks. However, there is a noticeable gap in the existing literature when it comes to leveraging Artificial Intelligence to enhance Threat modeling. While numerous innovative methods have been proposed in recent articles, these predominantly concentrate on Threat modeling within specific domains, such as unmanned vehicles, Cyber-Physical Systems, and Healthcare. There is a lack of generalization in applying these findings to improve Threat modeling practices more broadly. A promising avenue for further research lies in the automation of Threat modeling. Existing literature predominantly emphasizes the study of Threat generation areas, leaving other crucial aspects, such as Architecture representation and Model validation,in need of more comprehensive exploration and analysis

Digital Forensics Techniques and Trends: A Review

The research work presented in this paper aims to review Digital Forensics (DF) techniques and trends. As computer technology advances day by day, the chances of data being misused and tampered with are also growing daily. The advancement in technology results in various cyber-attacks on computers and mobile devices. DF plays a vital role in the investigation and prevention of cyber-attacks. DF can be used to find the shreds of evidence and prevent attacks from happening in the future. Earlier presented reviews highlighted specific issues in DF only. This paper explores deeply DF issues by highlighting domain-specific issues and possible helpful areas for DF. This article highlights the investigation process framework and related approaches for the digital investigation process. The cognitive and human factors that affect the DF process are also presented to strengthen the investigation process. Nowadays, many DF tools are available in the industry that helps in DF investigation. A comparative analysis of the four DF tools is also presented. Finally DF performance is discussed. The submitted work may help the researchers go deeper into DF and apply the best tools and models according to their requirements

Methods of Anomaly Detection for the Prevention and Detection of Cyber Attacks

Methods of Anomaly Detection for the Prevention and Detection of Cyber Attacks

Methods of anomaly detection for the prevention and detection of cyber attacks

Methods of anomaly detection for the prevention and detection of cyber attacks

Access Control Application Prevention and Mitigation of Cyber Attacks

In the rapidly evolving landscape of cyber threats, safeguarding digital assets has become paramount for organizations worldwide. This paper presents a comprehensive analysis of access control applications as a frontline defense against cyber intrusions. We delve into the mechanics of contemporary access control systems, highlighting their role in preventing unauthorized access and ensuring data integrity. Drawing on real-world case studies, we demonstrate the efficacy of robust access control measures in thwarting cyber-attacks and mitigating their potential impacts. Further, we explore the challenges faced in implementing these systems, such as ensuring scalability, managing user privileges, and maintaining system resilience. Our findings underscore the indispensable nature of access control applications in today’s digital age. By offering actionable insights and best practices, this paper aims to guide organizations in fortifying their digital infrastructures against the ever-present threat of cyberattacks. To ensure the security of data, it’s essential to update automation processes periodically. This not only safeguards the transfer of information, ensuring its authenticity and confidentiality, but also prevents tampering or misinformation. Incorporating access control into the security strategy can address this concern. Access control restricts data access to designated individuals within the organization. However, there’s an inherent risk: permissions could inadvertently be granted to unintended parties. To mitigate this, we recommend several solutions, with multifactor authentication being a prominent option for future implementation.

Some Comments on the Legal Regulation on Misinformation and Cyber Attacks Conducted Through Online Platforms

What are the tools to effectively manage the new threats to the free flow of opinions to protect this essential precondition for a pluralist social and political system? As a basis for action at the community level, how can we protect users who use internet platforms to inform themselves on issues of public interest from disinformation attacks through cyberspace? Effective action against cyber-attacks that adversely affect certain fundamental rights requires a combination of instruments, creating the technological, economic, human and legal conditions for meaningful counter-measures. In legal terms, the guarantees that platform providers must offer each user to prevent cyber attacks and illegal content should be laid down, and legal instruments should be put in place to ensure they are always available. In addition, in the event of misusing any content shared on the platform or of personal data made available to the operator, clear responsibilities should be established, and the extent to which the responsibility for protection lies with the platform operator or the user should be clarified. In our study, we outline regulatory options to address these challenges.

Content Disarm and Reconstruction of PDF Files

Content Disarm and Reconstruction (CDR) is a zero-trust file methodology that proactively extracts threat attack vectors from documents and media files. While extensive literature on CDR emphasizes its importance, a detailed discussion of how the CDR process works, its effectiveness, and its drawbacks is not presented. Therefore, this paper presents PdfCDR, the first PDF CDR system in which the validation, the prevention rate, and the received visual similarity effect of disarming and reconstruction are presented and measured. Furthermore, PdfCDR suggests for the first time a novel method dealing with new emerging exploits by automatically converting detection rules to disarm and reconstruction rules. As a result, PdfCDR can prevent evasive attacks without any software upgrades and utilize the cyber security community knowledge to prevent cyber attacks as soon as they are advertised. The effectiveness of the novel PdfCDR against well-known PDF datasets shows that it disarmed not only the malicious components, but the reconstructed file is also usable and functional. However, since CDR relies on understanding the file format, any CDR solution should handle each supported file type separately due to the vast difference in each file format. Hence, this paper focuses on the Portable Document Format (PDF) file type that attackers commonly exploit. The results indicate that PdfCDR successfully CDR 90% of the malicious files while the remaining 10% were encrypted or had abnormal structures compared to the standard and were quarantined.

Cyber Kill Chain Analysis of Five Major US Data Breaches

Data breaches are a major concern for both US and global corporations. With more companies allowing their employees to be working remote, providing them a secure work environment has been a priority for employers. The Interpol 2020 report on cyber breaches mentions that the number of cyber-attacks has multiplied in the last year. The IBM Data Breach Report of 2021 notes that data breach costs rose from USD 3.86 million to USD 4.24 million, while the average cost was USD 1.07 Mil higher in breaches where remote work was a factor in causing the breach. Given this environment of increased cyber breaches, it is important to learn from previous major data breaches to understand the root cause which led to the compromise of information security and the steps which could have effectively prevented the same. This paper evaluates five major data breaches in US history using Lockheed's Cyber Kill Chain Analysis, since the details of these breaches have never been documented for research and also proposes an eight-step cyber-attack prevention plan.

Cybersecurity Enhancement of Smart Grid: Attacks, Methods, and Prospects

Smart grid is an emerging system providing many benefits in digitizing the traditional power distribution systems. However, the added benefits of digitization and the use of the Internet of Things (IoT) technologies in smart grids also poses threats to its reliable continuous operation due to cyberattacks. Cyber–physical smart grid systems must be secured against increasing security threats and attacks. The most widely studied attacks in smart grids are false data injection attacks (FDIA), denial of service, distributed denial of service (DDoS), and spoofing attacks. These cyberattacks can jeopardize the smooth operation of a smart grid and result in considerable economic losses, equipment damages, and malicious control. This paper focuses on providing an extensive survey on defense mechanisms that can be used to detect these types of cyberattacks and mitigate the associated risks. The future research directions are also provided in the paper for efficient detection and prevention of such cyberattacks.

Detection and Prevention of Cyber Attacks on Cloud-Based Data Centers using Machine Learning

Detection and Prevention of Cyber Attacks on Cloud-Based Data Centers using Machine Learning

From black to white: the regulation of ethical hacking in Spain

ABSTRACT Cyber-attacks are exponentially growing, and their impact on systems, people, and organizations increases. Among other challenges, cyber-attacks prevention must tackle the fact that many software systems are marketed with security vulnerabilities due to the companies’ need to reduce time-to-market. One strategy to reduce security vulnerabilities is ethical hacking. However, while ethical hacking can bring many advantages, it also comes with many challenges. This paper introduces a comprehensive study of the possibilities and limitations of ethical hacking in Spain, both empirical and normative. On the empirical side, the paper presents the results of a Delphi study with cyber security experts in Spain on their opinions about the regulation of ethical hacking. In the normative study, the paper critically reviews the possibilities open by the International, European and Spanish law for regulating ethical hacking. The conclusions of this paper offer a roadmap for harnessing ethical hacking to improve cyber security.

Online Attacks Types of Data Breach and Cyber-attack Prevention Methods

Online Attacks Types of Data Breach and Cyber-attack Prevention Methods

Online Attacks Types of Data Breach and Cyber-attack Prevention Methods

Online Attacks Types of Data Breach and Cyber-attack Prevention Methods

Distributed event-triggered secondary frequency control of islanded AC microgrids under cyber attacks with input time delay

This article proposes a distributed event-triggered secondary frequency control considering cyber attacks and disturbances subject to input delay for islanded AC microgrids (MGs). The reliance on communication networks exposes MGs to cyber attacks. Therefore, detection and prevention of cyber attacks is one of the most important issues for islanded MGs. The purpose of this article is the analysis of the impacts of cyber attacks, such as false data injection (FDI) and denial-of-service (DoS) on communication links and local controllers (LCs), in the presence of time delay in communication links. Our method conserves communication resources by reducing the number of transmissions of sampled data. Also, each distributed generation (DG) can quickly detect and isolate corrupted links and controllers. Moreover, we have analyzed the stability of our proposed strategy by the Lyapunov theory. The superior features of our proposed method, including cyber security, number of transmissions of sampled data, and communication time delay, have been investigated in MATLAB/SimPowersystems Toolbox.

Cyber attack awareness and prevention in network security

<span>This article aims to provide an overview of cyber attack awareness and prevention in network security. This article discussed the different types of cyber attacks, current trends of cyber attacks, how to prevent cyber attacks and uum students' awareness of cyber attacks. First, we will go over the different types of cyber attack, current trend, impact of cyber attack and the prevention. The approach entailed comparing and observing the outcomes of 13 different papers. The survey's findings would demonstrate the results obtained after analyzing the data collection which are the questionnaire filled out by respondents after watching the cyber attack awareness video to improve awareness of students through the cyber attack. Depending on the outcome of this survey, we will have a better understanding of current students' knowledge and awareness of cyber attacks, allowing us to improve students' understanding of cyber threats and the necessity of cyber security.</span>