1,158 research outputs found
Harnessing the Speed and Accuracy of Machine Learning to Advance Cybersecurity
As cyber attacks continue to increase in frequency and sophistication,
detecting malware has become a critical task for maintaining the security of
computer systems. Traditional signature-based methods of malware detection have
limitations in detecting complex and evolving threats. In recent years, machine
learning (ML) has emerged as a promising solution to detect malware
effectively. ML algorithms are capable of analyzing large datasets and
identifying patterns that are difficult for humans to identify. This paper
presents a comprehensive review of the state-of-the-art ML techniques used in
malware detection, including supervised and unsupervised learning, deep
learning, and reinforcement learning. We also examine the challenges and
limitations of ML-based malware detection, such as the potential for
adversarial attacks and the need for large amounts of labeled data.
Furthermore, we discuss future directions in ML-based malware detection,
including the integration of multiple ML algorithms and the use of explainable
AI techniques to enhance the interpret ability of ML-based detection systems.
Our research highlights the potential of ML-based techniques to improve the
speed and accuracy of malware detection, and contribute to enhancing
cybersecurit
The Role of Deep Learning in Advancing Proactive Cybersecurity Measures for Smart Grid Networks: A Survey
As smart grids (SG) increasingly rely on advanced technologies like sensors
and communication systems for efficient energy generation, distribution, and
consumption, they become enticing targets for sophisticated cyberattacks. These
evolving threats demand robust security measures to maintain the stability and
resilience of modern energy systems. While extensive research has been
conducted, a comprehensive exploration of proactive cyber defense strategies
utilizing Deep Learning (DL) in {SG} remains scarce in the literature. This
survey bridges this gap, studying the latest DL techniques for proactive cyber
defense. The survey begins with an overview of related works and our distinct
contributions, followed by an examination of SG infrastructure. Next, we
classify various cyber defense techniques into reactive and proactive
categories. A significant focus is placed on DL-enabled proactive defenses,
where we provide a comprehensive taxonomy of DL approaches, highlighting their
roles and relevance in the proactive security of SG. Subsequently, we analyze
the most significant DL-based methods currently in use. Further, we explore
Moving Target Defense, a proactive defense strategy, and its interactions with
DL methodologies. We then provide an overview of benchmark datasets used in
this domain to substantiate the discourse.{ This is followed by a critical
discussion on their practical implications and broader impact on cybersecurity
in Smart Grids.} The survey finally lists the challenges associated with
deploying DL-based security systems within SG, followed by an outlook on future
developments in this key field.Comment: To appear in the IEEE internet of Things journa
Artificial intelligence in the cyber domain: Offense and defense
Artificial intelligence techniques have grown rapidly in recent years, and their applications in practice can be seen in many fields, ranging from facial recognition to image analysis. In the cybersecurity domain, AI-based techniques can provide better cyber defense tools and help adversaries improve methods of attack. However, malicious actors are aware of the new prospects too and will probably attempt to use them for nefarious purposes. This survey paper aims at providing an overview of how artificial intelligence can be used in the context of cybersecurity in both offense and defense.Web of Science123art. no. 41
Digital Deception: Generative Artificial Intelligence in Social Engineering and Phishing
The advancement of Artificial Intelligence (AI) and Machine Learning (ML) has
profound implications for both the utility and security of our digital
interactions. This paper investigates the transformative role of Generative AI
in Social Engineering (SE) attacks. We conduct a systematic review of social
engineering and AI capabilities and use a theory of social engineering to
identify three pillars where Generative AI amplifies the impact of SE attacks:
Realistic Content Creation, Advanced Targeting and Personalization, and
Automated Attack Infrastructure. We integrate these elements into a conceptual
model designed to investigate the complex nature of AI-driven SE attacks - the
Generative AI Social Engineering Framework. We further explore human
implications and potential countermeasures to mitigate these risks. Our study
aims to foster a deeper understanding of the risks, human implications, and
countermeasures associated with this emerging paradigm, thereby contributing to
a more secure and trustworthy human-computer interaction.Comment: Submitted to CHI 202
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