Rola sztucznej inteligencji w wykrywaniu i neutralizacji zagrożeń cybernetycznych / The Role of Artificial Intelligence in Detecting and Neutralising Cyber Threats
Rola sztucznej inteligencji w wykrywaniu i neutralizacji zagrożeń cybernetycznych / The Role of Artificial Intelligence in Detecting and Neutralising Cyber Threats
Data
2025
Autorzy
Kania, Marek
Tytuł czasopisma
ISSN czasopisma
Tytuł tomu
Wydawca
Akademia Bialska im. Jana Pawła II
Streszczenie
Rozwój technologii sztucznej inteligencji (SI) w ostatnich latach znacząco zmienił sposób
identyfikowania, monitorowania i neutralizowania zagrożeń w cyberprzestrzeni. Dzięki
algorytmom uczenia maszynowego i głębokiego uczenia, systemy bezpieczeństwa są
w stanie analizować ogromne wolumeny danych w czasie rzeczywistym, wykrywając
anomalie i wzorce charakterystyczne dla działań cyberprzestępczych. Rozdział
przedstawia przegląd aktualnych zastosowań AI w cyberbezpieczeństwie, ze szczególnym
uwzględnieniem metod detekcji ataków typu phishing, ransomware oraz APT (Advanced
Persistent Threats). Omówione zostaną także wyzwania związane z implementacją SI,
w tym ryzyko błędnych alarmów, podatność algorytmów na manipulacje oraz kwestie
etyczne. Analiza obejmuje również studia przypadków zastosowań AI w infrastrukturze krytycznej oraz w sektorze prywatnym. Wnioski wskazują, że skuteczne wykorzystanie Ai
wymaga synergii technologii z czynnikiem ludzkim, ciągłego doskonalenia algorytmów
oraz integracji z polityką bezpieczeństwa organizacji.
The development of artificial intelligence (AI) technology in recent years has significantly altered the way cyber threats are identified, monitored, and neutralised. Thanks to machine learning and deep learning algorithms, security systems can analyse vast volumes of data in real time, detecting anomalies and patterns characteristic of cybercriminal activity. This chapter provides an overview of current AI applications in cybersecurity, with a particular focus on methods for detecting phishing, ransomware, and APT (Advanced Persistent Threats). The challenges associated with AI implementation, including the risk of false alarms, the vulnerability of algorithms to manipulation, and ethical issues, will also be discussed. The analysis also includes case studies of AI applications in critical infrastructure and the private sector. The conclusions suggest that the effective use of AI necessitates synergy between technology and the human factor, continuous algorithm improvement, and integration with the organisation’s security policy.
The development of artificial intelligence (AI) technology in recent years has significantly altered the way cyber threats are identified, monitored, and neutralised. Thanks to machine learning and deep learning algorithms, security systems can analyse vast volumes of data in real time, detecting anomalies and patterns characteristic of cybercriminal activity. This chapter provides an overview of current AI applications in cybersecurity, with a particular focus on methods for detecting phishing, ransomware, and APT (Advanced Persistent Threats). The challenges associated with AI implementation, including the risk of false alarms, the vulnerability of algorithms to manipulation, and ethical issues, will also be discussed. The analysis also includes case studies of AI applications in critical infrastructure and the private sector. The conclusions suggest that the effective use of AI necessitates synergy between technology and the human factor, continuous algorithm improvement, and integration with the organisation’s security policy.
The development of artificial intelligence (AI) technology in recent years has significantly altered the way cyber threats are identified, monitored, and neutralised. Thanks to machine learning and deep learning algorithms, security systems can analyse vast volumes of data in real time, detecting anomalies and patterns characteristic of cybercriminal activity. This chapter provides an overview of current AI applications in cybersecurity, with a particular focus on methods for detecting phishing, ransomware, and APT (Advanced Persistent Threats). The challenges associated with AI implementation, including the risk of false alarms, the vulnerability of algorithms to manipulation, and ethical issues, will also be discussed. The analysis also includes case studies of AI applications in critical infrastructure and the private sector. The conclusions suggest that the effective use of AI necessitates synergy between technology and the human factor, continuous algorithm improvement, and integration with the organisation’s security policy.
The development of artificial intelligence (AI) technology in recent years has significantly altered the way cyber threats are identified, monitored, and neutralised. Thanks to machine learning and deep learning algorithms, security systems can analyse vast volumes of data in real time, detecting anomalies and patterns characteristic of cybercriminal activity. This chapter provides an overview of current AI applications in cybersecurity, with a particular focus on methods for detecting phishing, ransomware, and APT (Advanced Persistent Threats). The challenges associated with AI implementation, including the risk of false alarms, the vulnerability of algorithms to manipulation, and ethical issues, will also be discussed. The analysis also includes case studies of AI applications in critical infrastructure and the private sector. The conclusions suggest that the effective use of AI necessitates synergy between technology and the human factor, continuous algorithm improvement, and integration with the organisation’s security policy.
Opis
Słowa kluczowe
sztuczna inteligencja,
cyberbezpieczeństwo,
wykrywanie zagrożeń,
neutralizacja cyberataków,
uczenie maszynowe,
artificial intelligence,
cybersecurity,
threat detection,
cyberattack neutralisation,
machine learning