detecting
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arXiv:2503.09302v1 Announce Type: new Abstract: This paper investigates the critical issue of data poisoning attacks on AI models, a growing concern in the ever-evolving landscape of artificial intelligence and cybersecurity. As advanced technology systems become increasingly prevalent across various sectors, the need for robust defence mechanisms against adversarial attacks becomes paramount. The study aims to…
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In November 2024, U.S. authorities charged multiple individuals for conducting cyberattacks on telecom and financial firms. They allegedly used phishing to steal credentials, breach networks, and exfiltrate data, leading to major security and financial losses. This incident highlights the escalating sophistication of cyber threats and the critical need for advanced defense mechanisms. Traditional security measures…
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arXiv:2503.02986v1 Announce Type: new Abstract: Adversarial attacks remain a significant threat that can jeopardize the integrity of Machine Learning (ML) models. In particular, query-based black-box attacks can generate malicious noise without having access to the victim model’s architecture, making them practical in real-world contexts. The community has proposed several defenses against adversarial attacks, only to…
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DySec: A Machine Learning-based Dynamic Analysis for Detecting Malicious Packages in PyPI Ecosystem
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arXiv:2503.00324v1 Announce Type: new Abstract: Malicious Python packages make software supply chains vulnerable by exploiting trust in open-source repositories like Python Package Index (PyPI). Lack of real-time behavioral monitoring makes metadata inspection and static code analysis inadequate against advanced attack strategies such as typosquatting, covert remote access activation, and dynamic payload generation. To address these…
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arXiv:2503.00416v1 Announce Type: new Abstract: Large Language Models (LLMs) have significantly advanced text understanding and generation, becoming integral to applications across education, software development, healthcare, entertainment, and legal services. Despite considerable progress in improving model reliability, latency remains under-explored, particularly through recurrent generation, where models repeatedly produce similar or identical outputs, causing increased latency and…
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arXiv:2502.14726v1 Announce Type: cross Abstract: Audio deepfakes are increasingly in-differentiable from organic speech, often fooling both authentication systems and human listeners. While many techniques use low-level audio features or optimization black-box model training, focusing on the features that humans use to recognize speech will likely be a more long-term robust approach to detection. We explore…
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Photo by Jefferson Santos on UnsplashYou’re working on your laptop, completely unaware that someone is watching you, not through a camera, but through your own internet connection.Every few minutes, your computer secretly reaches out to an unknown server, sending tiny packets of data.No security alerts go off. No antivirus detects anything suspicious.But in the background, an attacker…
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arXiv:2502.10110v1 Announce Type: new Abstract: With the rise of sophisticated scam websites that exploit human psychological vulnerabilities, distinguishing between legitimate and scam websites has become increasingly challenging. This paper presents ScamFerret, an innovative agent system employing a large language model (LLM) to autonomously collect and analyze data from a given URL to determine whether it…
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arXiv:2502.08921v1 Announce Type: new Abstract: The task of text-to-image generation has achieved tremendous success in practice, with emerging concept generation models capable of producing highly personalized and customized content. Fervor for concept generation is increasing rapidly among users, and platforms for concept sharing have sprung up. The concept owners may upload malicious concepts and disguise…
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arXiv:2502.07207v1 Announce Type: new Abstract: Advanced Persistent Threats (APTs) pose a significant security risk to organizations and industries. These attacks often lead to severe data breaches and compromise the system for a long time. Mitigating these sophisticated attacks is highly challenging due to the stealthy and persistent nature of APTs. Machine learning models are often…
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arXiv:2502.05367v1 Announce Type: new Abstract: Advanced Persistent Threats (APTs) are among the most sophisticated threats facing critical organizations worldwide. APTs employ specific tactics, techniques, and procedures (TTPs) which make them difficult to detect in comparison to frequent and aggressive attacks. In fact, current network intrusion detection systems struggle to detect APTs communications, allowing such threats…
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arXiv:2410.07588v2 Announce Type: replace Abstract: In Android apps, their developers frequently place app promotion ads, namely advertisements to promote other apps. Unfortunately, the inadequate vetting of ad content allows malicious developers to exploit app promotion ads as a new distribution channel for malware. To help detect malware distributed via app promotion ads, in this paper,…
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Have you ever encountered situations where you identified a malicious insider? How were you able to detect them, and what were the consequences for the insider? What advice can you offer on detecting malicious insiders, and how can organizations effectively organize monitoring for such activity? submitted by /u/athanielx [link] [comments]
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arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
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GUEST ESSAY: The key role static code analyzers play in detecting coding errors, eliminating flaws
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By Andrey Karpov In the modern world of software development, code quality is becoming a critical factor that determines a project success. Errors in code can entail severe consequences.
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arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
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MalBot November 19, 2024, 6:50am 1 Hello from Singapore where I’m with Johannes and Yee! This week, I’m teaching FOR710[1]. I spotted another Python script that looked interesting because, amongst the classic detection of virtualized environments, it also tries to detect the presence of a debugger. The script has been developed to target both environments: Windows & Linux.