Figure - available from: Journal of Ambient Intelligence and Humanized Computing
This content is subject to copyright. Terms and conditions apply.
Source publication
In today’s cyber warfare realm, every stakeholder in cyberspace is becoming more potent by developing advanced cyber weapons. They have equipped with the most advanced malware and maintain a hidden attribution. The precocious cyber weapons, targeted and motivated with some specific intention are called as Advanced Persistent Threats (APT). Developi...
Similar publications
Botnets continue to evolve despite many efforts by law enforcement agencies and security researchers. As a result, there is an increase in the number of cybercrimes. This has led to a greater research focus on botnet detection. Among the reasons for growth in botnet and cybercrimes despite greater research focus are that significant number of the p...
Citations
... We consider the auditing system to be uncompromised, serving as a trusted computing base (TCB), and tamper-proof, ensuring the integrity and reliability of the collected system logs [3]. Furthermore, we assume that the provenance graphs constructed from these logs capture all relevant system entities and their interactions with sufficient granularity for effective APT detection and tracing, done in previous studies [32,9,18,16,26,13]. ...
Advanced persistent threats (APTs) pose significant challenges for organizations, leading to data breaches, financial losses, and reputational damage. Existing provenance-based approaches for APT detection often struggle with high false positive rates, a lack of interpretability, and an inability to adapt to evolving system behavior. We introduce RAPID, a novel deep learning-based method for robust APT detection and investigation, leveraging context-aware anomaly detection and alert tracing. By utilizing self-supervised sequence learning and iteratively learned embeddings, our approach effectively adapts to dynamic system behavior. The use of provenance tracing both enriches the alerts and enhances the detection capabilities of our approach. Our extensive evaluation demonstrates RAPID's effectiveness and computational efficiency in real-world scenarios. In addition, RAPID achieves higher precision and recall than state-of-the-art methods, significantly reducing false positives. RAPID integrates contextual information and facilitates a smooth transition from detection to investigation, providing security teams with detailed insights to efficiently address APT threats.
... Reviewing recent reports on large scale security breaches and APT campaigns revealed that APT groups have expanded their focus to encompass a broad array of industries and governmental entities [5,6]. An APT attack terminates either upon detection or upon the attackers' successful attainment of their objectives. ...
... In both instances, the targeted organization experiences substantial consequences, frequently encom-passing irreparable harm [5]. The authors in [6] mentioned that APT attacks target survivability, availability, confidentiality, and/or integrity of organizations. As a result, the severity of the consequences of an APT attack is heightened when it remains undetected until the attackers accomplish their predefined objectives. ...
... Step 6 The fake best master clock approach effectively stops the real time sources and their synchronization messages, creating a scenario similar to a DoS attack. The next step for the attacker is to maintain this situation for a longer period to confuse the devices. ...
Advanced Persistent Threats (APTs) are stealthy, multi-step attacks tailored to a specific target. Often described as ’low and slow’, APTs remain undetected until the consequences of the cyber-attack become evident, usually in the form of damage to the physical world, as seen with the Stuxnet attack, or manipulation of an industrial process, as was the case in the Ukraine Power Grid attacks. Given the increasing sophistication and targeted nature of cyber-attacks, especially APTs, this paper delves into the substantial threats APTs pose to critical infrastructures, focusing on power grid substations. Through a detailed case study, we present and explore a 2-stage APT attack on an IEC 61850 power grid substation, employing a Hardware-in-the-Loop (HIL) testbed to simulate real-world conditions. More specifically, this paper discusses two significant experiments conducted to assess vulnerabilities in the control protocols used in IEC 61850 substations: IEC 60870-5-104 and IEC 61850. The integration of findings from these experiments revealed a number of previously undiscussed potential threats to power grid infrastructure that could arise from attacking one or more substations. To better address these potential threats, the paper proposes an extension to the Industrial Control System (ICS) kill chain that explicitly accounts for the consequences of attacks on the physical aspects of Cyber-Physical Systems (CPSs).
... Simultaneously, the exploitation of zero-day vulnerabilities became a common tactic, compounding the difficulties faced by defenders in predicting and preventing attacks [15,2,16]. Ransomware campaigns were increasingly orchestrated using legitimate administrative tools, complicating the differentiation between benign and malicious activities on infected networks [17,18]. The role of Cybercrime-as-a-Service platforms grew, providing even lowskilled attackers with sophisticated tools for ransomware deployment, and they expanded the ransomware threat landscape by lowering entry barriers for potential cybercriminals and facilitating the rapid dissemination of ransomware techniques [19]. ...
Ransomware remains an alarming threat in the cybersecurity landscape, presenting complex challenges that demand innovative solutions. As the frequency and sophistication of ransomware attacks increase, understanding the dynamics of these malicious endeavors has become crucial for developing effective defense mechanisms. The comprehensive analysis provided here explores various facets of ransomware activity, particularly its impact on MacOS environments, a less commonly discussed target compared to Windows systems. Through an examination of attack vectors, the study highlights the role of user behavior, system vulnerabilities, and the lack of robust cybersecurity measures as primary facilitators of ransomware breaches. Technical mitigation strategies such as regular software updates, stringent access controls, and advanced threat detection systems are evaluated for their effectiveness in thwarting attacks. Additionally, the research delves into policy measures and best practices that can supplement technical defenses, emphasizing the need for continuous education and strategic response planning. Looking ahead, the study suggests avenues for future research, including the potential of artificial intelligence in predictive threat modeling and the importance of cross-sector collaboration in enhancing collective security postures. These insights not only refine current understandings of ransomware defense but also offer a blueprint for advancing cybersecurity resilience in the face of evolving digital threats.
... involving behavioral analytics and anomaly detection is anticipated. Future defense strategies may prioritize continuous monitoring of user behavior, enabling the rapid identification of abnormal activities and potential security threats [6]. ...
With the pervasive use of mobile devices in today's digital landscape, the threat of mobile malware continues to evolve, presenting challenges for effective defense strategies. This paper proposes advanced defense adaptation techniques to counter dynamic mobile malware threats by navigating evolving strategies. Through a comprehensive analysis of the mobile threat landscape, including emerging attack vectors and sophisticated evasion tactics employed by malware, this research explores innovative approaches for bolstering mobile security defenses. Leveraging proactive measures such as behavior analysis, anomaly detection, and machine learning algorithms, organizations can enhance their ability to detect and mitigate mobile malware in real-time. Additionally, this paper discusses the importance of continuous monitoring, threat intelligence sharing, and collaboration among stakeholders to stay ahead of evolving threats. By adopting a multi-layered defense approach and staying vigilant against emerging attack techniques, organizations can effectively protect their mobile ecosystems from the growing menace of mobile malware.
... Identification of APT components The research provides a detailed examination of the growth of complex malware design paradigms, the APT attack vector and its architecture, the APT attack tactics, techniques, and procedures (TTP), and specific case studies on open-ended APT attacks [5]. ...
Advanced persistent threats (APTs) present a significant cybersecurity challenge, necessitating innovative detection methods. This study stands out by integrating advanced data preparation with strategies for handling data imbalances, tailored for the SCVIC-APT-2021 dataset. We employ a mix of resampling, cost-sensitive learning, and ensemble methods, alongside machine learning and deep learning models like XGBoost, LightGBM, and ANNs, to enhance APT detection. Our strategy, which draws from the MITRE ATT&CK framework, concentrates on each stage of APT attacks, which significantly increases detection accuracy. Notably, we achieved a Macro F1-score of 95.20% with XGBoost and 96.67% with LightGBM, and significant enhancements in the area under the precision–recall curve for both. Our study’s exploration of the SCVIC-APT-2021 dataset marks a progressive step in APT detection research, with vital implications for future cybersecurity developments.
... This section emphasizes the importance of intuitive and user-friendly security interfaces, transparent permission requests, and clear security notifications. It explores the concept of security by design and the integration of user experience principles to foster a positive security culture [11], [12]. ...
The proliferation of mobile devices has led to an increase in sophisticated malware threats targeting these platforms. To effectively combat this evolving landscape, advanced defense strategies are essential. This abstract explores various techniques for navigating the dynamic mobile malware environment. First, proactive measures such as continuous monitoring and threat intelligence integration are emphasized to identify emerging threats early. Next, the importance of implementing multi-layered defense mechanisms, including robust endpoint security solutions and secure coding practices, is highlighted. Additionally, user education and awareness programs are advocated to mitigate the risks associated with social engineering attacks. Furthermore, the abstract discusses the significance of collaboration between security professionals, researchers, and industry stakeholders to share insights and develop effective countermeasures. By adopting these advanced strategies, organizations can enhance their resilience against mobile malware threats and safeguard sensitive data and resources.
... In the ever-evolving cyber domain, security professionals are continually seeking innovative strategies to prepare for and reduce the impact of complex threats. Cyber threats can take various forms, including malware, phishing, ransomware, DDoS attacks, social engineering, zero-day exploits, insider threats, and advanced persistent threats [1,2]. Adapting to these many threat types is critical for protecting organizations and digital assets in an increasingly linked world [3]. ...
This study proposes a novel approach leveraging Large Language Models (LLMs) to generate dynamic and complex adaptable cybersecu-rity exercise scenarios. Motivated by Turing’s sem-inal exploration into machine cognition, which questions the ability of machines to mimic human thought and intelligence. By exploiting the generative potential of LLMs, our methodology simulates a wide range of cyber threats, both known and novel, thereby enhancing cybersecurity training and awareness. This approach transforms the potential for ’hal-lucination’ inherent in LLMs into a potential advantage , enabling the creation of complex exercise scenarios that push the boundaries of traditional cybersecurity training. The innovation lies in the sophisticated application of AI, aiming to advance the preparedness of security professionals against diverse cyber threats. The scenarios generated through this method were subject to meticulous testing and a rigorous evaluation process involving GPT models and expert review to ensure their realism and applicability. Furthermore, the prompts provided to the LLMs were meticulously designed to adopt a Retrieval-Augmented Generation (RAG) approach, enriching the complexity and relevance of the scenarios. This incorporation of RAG, alongside the inspiration drawn from Tur-ing’s exploration of machine intelligence, showcases an advanced application of AI in cybersecurity training , reflecting a deep understanding of how machines can augment our capabilities to anticipate and mitigate cyber threats.
... APTs involve prolonged and targeted attacks by sophisticated adversaries aiming to gain unauthorized access and maintain a persistent presence within a network. These attacks pose a significant and evolving threat to the financial services sector, characterized by highly sophisticated and targeted cyberattacks that aim to compromise sensitive information and systems over an extended period [110]- [114]. Figure 9 shows a typical lifecycle for APTs. ...
As the financial service sector rapidly evolves with the integration of cutting-edge technologies, the intersection of security and privacy becomes paramount. This paper delves into the intricate landscape of security and privacy issues within the financial service sector, offering a comprehensive analysis of the challenges and opportunities presented by emerging technologies. From blockchain to artificial intelligence, the paper explores the vulnerabilities inherent in these innovations and the consequential threats to sensitive financial data. Through an examination of recent case studies, regulatory frameworks, and technological advancements, this work aims to provide a nuanced understanding of the evolving threat landscape. Additionally, the paper proposes strategic solutions and best practices to fortify the security and privacy architecture surrounding financial technologies, fostering a resilient and trustworthy ecosystem. This research contributes to the ongoing dialogue surrounding the imperative of safeguarding financial systems, ensuring that innovation aligns seamlessly with the imperatives of confidentiality, integrity, and availability in an era where financial services and technological advancements are inextricably linked.
... Ransomware is a type of malware that encrypts a victim's data and demands a ransom in exchange for the decryption key. It attacks have become increasingly prevalent and disruptive, targeting individuals, businesses, and even critical infrastructure [45,46]. ...
... Advanced attackers may use techniques such as polymorphic malware, zeroday exploits, and advanced persistent threats (APTs) to bypass conventional security measures. Advanced threat detection and defense mechanisms solutions are designed to detect these subtle and evasive tactics [45,48]. ...
... Advanced threat detection encompasses both network and endpoint security. With cyber threats targeting various entry points, a comprehensive approach that covers both the network and individual devices is crucial for effective defense [45,55]. ...
The cybersecurity concerns get increasingly intricate as the digital world progresses. In light of the increasing complexity of cyber threats, it is imperative to develop and implement advanced and flexible security strategies. Machine Learning (ML) has become a potent tool in strengthening cybersecurity, providing the capacity to scrutinise extensive information, recognise trends, and improve threat detection and defence methods. This paper examines the significance of ML in the field of cybersecurity, with a special emphasis on the identification of threats and the implementation of protective measures. By incorporating ML algorithms into cybersecurity frameworks, organisations may automate decision-making processes, facilitating prompt responses to ever-changing threats. The initial segment explores the terrain of cyber threats, highlighting the necessity for dynamic and aggressive security methods. Conventional solutions that rely on signatures are frequently inadequate when it comes to handling sophisticated, shape-shifting attacks. ML algorithms, in contrast, have exceptional proficiency in identifying nuanced patterns and irregularities within extensive datasets, therefore offering a more efficient method of detecting potential threats. The second section delves into several ML methodologies utilised in cybersecurity, including supervised and unsupervised learning, deep learning, and reinforcement learning. Every approach is assessed based on its suitability for threat detection, demonstrating its advantages and constraints. Furthermore, the relevance of feature engineering and data pretreatment in improving machine learning models for cybersecurity applications. The versatility of ML algorithms allows them to grow with emerging threats, making them a useful tool in the ever-changing arena of cyber warfare. The final segment focuses on real-world applications of machine learning in cybersecurity, presenting successful use cases across sectors. From anomaly detection to behavior analysis, ML algorithms contribute to the discovery of dangerous activity, lowering false positives and strengthening the overall security posture. Lastly, the paper covers the obstacles and ethical issues related to the adoption of ML in cybersecurity. Issues like as adversarial assaults, skewed datasets, and the interpretability of ML models are examined, highlighting the necessity for a holistic strategy that integrates modern technology with ethical considerations. The fusion of human expertise and machine intelligence offers a formidable defense against evolving cyber threats, paving the way for a more resilient and secure digital future.
... Issue: APTs involve prolonged and targeted attacks by sophisticated adversaries aiming to gain unauthorized access and maintain a persistent presence within a network. These attacks pose a significant and evolving threat to the financial services sector, characterized by highly sophisticated and targeted cyberattacks that aim to compromise sensitive information and systems over an extended period [110]- [114]. Adversaries behind APTs often employ advanced techniques, including social engineering, spear-phishing, and zero-day exploits, to gain unauthorized access to financial institutions' networks. ...
As the financial service sector rapidly evolves with the integration of cutting-edge technologies, the intersection of security and privacy becomes paramount. This manuscript delves into the intricate landscape of security and privacy issues within the financial service sector, offering a comprehensive analysis of the challenges and opportunities presented by emerging technologies. From blockchain to artificial intelligence, the manuscript explores the vulnerabilities inherent in these innovations and the consequential threats to sensitive financial data. Through an examination of recent case studies, regulatory frameworks, and technological advancements, this work aims to provide a nuanced understanding of the evolving threat landscape. Additionally, the manuscript proposes strategic solutions and best practices to fortify the security and privacy architecture surrounding financial technologies, fostering a resilient and trustworthy ecosystem. This research contributes to the ongoing dialogue surrounding the imperative of safeguarding financial systems, ensuring that innovation aligns seamlessly with the imperatives of confidentiality, integrity, and availability in an era where financial services and technological advancements are inextricably linked.
