The New Frontier of Cybersecurity: Emerging Threats and Innovations

• URL: http://arxiv.org/abs/2311.02630v1
• Date: Sun, 5 Nov 2023 12:08:20 GMT
• Title: The New Frontier of Cybersecurity: Emerging Threats and Innovations
• Authors: Daksh Dave, Gauransh Sawhney, Pushkar Aggarwal, Nitish Silswal, Dhruv Khut
• Abstract summary: The research delves into the consequences of these threats on individuals, organizations, and society at large. The sophistication and diversity of these emerging threats necessitate a multi-layered approach to cybersecurity. This study emphasizes the importance of implementing effective measures to mitigate these threats.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In today's digitally interconnected world, cybersecurity threats have reached unprecedented levels, presenting a pressing concern for individuals, organizations, and governments. This study employs a qualitative research approach to comprehensively examine the diverse threats of cybersecurity and their impacts across various sectors. Four primary categories of threats are identified and analyzed, encompassing malware attacks, social engineering attacks, network vulnerabilities, and data breaches. The research delves into the consequences of these threats on individuals, organizations, and society at large. The findings reveal a range of key emerging threats in cybersecurity, including advanced persistent threats, ransomware attacks, Internet of Things (IoT) vulnerabilities, and social engineering exploits. Consequently, it is evident that emerging cybersecurity threats pose substantial risks to both organizations and individuals. The sophistication and diversity of these emerging threats necessitate a multi-layered approach to cybersecurity. This approach should include robust security measures, comprehensive employee training, and regular security audits. The implications of these emerging threats are extensive, with potential consequences such as financial loss, reputational damage, and compromised personal information. This study emphasizes the importance of implementing effective measures to mitigate these threats. It highlights the significance of using strong passwords, encryption methods, and regularly updating software to bolster cyber defenses.

Related papers

• Cyber Threats to Canadian Federal Election: Emerging Threats, Assessment, and Mitigation Strategies [2.04903126350824]
  Recent foreign interference in elections globally highlight the increasing sophistication of adversaries in exploiting technical and human vulnerabilities. To mitigate these vulnerabilities, a threat assessment is crucial to identify emerging threats, develop incident response capabilities, and build public trust and resilience against cyber threats. The research identifies three major threats: misinformation, disinformation, and malinformation (MDM) campaigns; attacks on critical infrastructure and election support systems; and espionage by malicious actors.
  arXiv  Detail & Related papers  (2024-10-07T23:40:40Z)
• Enhancing cybersecurity defenses: a multicriteria decision-making approach to MITRE ATT&CK mitigation strategy [0.0]
  This paper proposes a defense strategy for the presented security threats by determining and prioritizing which security control to put in place. This approach helps organizations achieve a more robust and resilient cybersecurity posture.
  arXiv  Detail & Related papers  (2024-07-27T09:47:26Z)
• The MESA Security Model 2.0: A Dynamic Framework for Mitigating Stealth Data Exfiltration [0.0]
  Stealth Data Exfiltration is a significant cyber threat characterized by covert infiltration, extended undetectability, and unauthorized dissemination of confidential data. Our findings reveal that conventional defense-in-depth strategies often fall short in combating these sophisticated threats. As we navigate this complex landscape, it is crucial to anticipate potential threats and continually update our defenses.
  arXiv  Detail & Related papers  (2024-05-17T16:14:45Z)
• PsySafe: A Comprehensive Framework for Psychological-based Attack, Defense, and Evaluation of Multi-agent System Safety [70.84902425123406]
  Multi-agent systems, when enhanced with Large Language Models (LLMs), exhibit profound capabilities in collective intelligence. However, the potential misuse of this intelligence for malicious purposes presents significant risks. We propose a framework (PsySafe) grounded in agent psychology, focusing on identifying how dark personality traits in agents can lead to risky behaviors. Our experiments reveal several intriguing phenomena, such as the collective dangerous behaviors among agents, agents' self-reflection when engaging in dangerous behavior, and the correlation between agents' psychological assessments and dangerous behaviors.
  arXiv  Detail & Related papers  (2024-01-22T12:11:55Z)
• Critical Analysis and Countermeasures Tactics, Techniques and Procedures (TTPs) that targeting civilians: A case study On Pegasus [0.0]
  This paper investigates the targeting of journalists and activists by the malware Pegasus. Examines the far-reaching consequences of these attacks for cybersecurity policy. Describes some of the most important tactics that businesses may use to reduce the danger of cyberattacks.
  arXiv  Detail & Related papers  (2023-10-01T19:28:03Z)
• On the Security Risks of Knowledge Graph Reasoning [71.64027889145261]
  We systematize the security threats to KGR according to the adversary's objectives, knowledge, and attack vectors. We present ROAR, a new class of attacks that instantiate a variety of such threats. We explore potential countermeasures against ROAR, including filtering of potentially poisoning knowledge and training with adversarially augmented queries.
  arXiv  Detail & Related papers  (2023-05-03T18:47:42Z)
• Graph Mining for Cybersecurity: A Survey [61.505995908021525]
  The explosive growth of cyber attacks nowadays, such as malware, spam, and intrusions, caused severe consequences on society. Traditional Machine Learning (ML) based methods are extensively used in detecting cyber threats, but they hardly model the correlations between real-world cyber entities. With the proliferation of graph mining techniques, many researchers investigated these techniques for capturing correlations between cyber entities and achieving high performance.
  arXiv  Detail & Related papers  (2023-04-02T08:43:03Z)
• ThreatKG: An AI-Powered System for Automated Open-Source Cyber Threat Intelligence Gathering and Management [65.0114141380651]
  ThreatKG is an automated system for OSCTI gathering and management. It efficiently collects a large number of OSCTI reports from multiple sources. It uses specialized AI-based techniques to extract high-quality knowledge about various threat entities.
  arXiv  Detail & Related papers  (2022-12-20T16:13:59Z)
• Defending against cybersecurity threats to the payments and banking system [0.0]
  The proliferation of cyber crimes is a huge concern for various stakeholders in the banking sector. To prevent risks of cyber-attacks on software systems, entities operating within cyberspace must be identified. This paper will examine various approaches that identify assets in cyberspace, classify the cyber threats, provide security defenses and map security measures to control types and functionalities.
  arXiv  Detail & Related papers  (2022-12-15T11:55:11Z)
• A System for Automated Open-Source Threat Intelligence Gathering and Management [53.65687495231605]
  SecurityKG is a system for automated OSCTI gathering and management. It uses a combination of AI and NLP techniques to extract high-fidelity knowledge about threat behaviors.
  arXiv  Detail & Related papers  (2021-01-19T18:31:35Z)
• Adversarial Machine Learning Attacks and Defense Methods in the Cyber Security Domain [58.30296637276011]
  This paper summarizes the latest research on adversarial attacks against security solutions based on machine learning techniques. It is the first to discuss the unique challenges of implementing end-to-end adversarial attacks in the cyber security domain.
  arXiv  Detail & Related papers  (2020-07-05T18:22:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.