RISK ASSESSMENT IN CYBER ATTACKS BASED ON INTERNET OF THINGS (IOT) Article Swipe
Shubham Kumar
,
Dayashankar Singh
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.55524/csistw.2024.12.1.66
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.55524/csistw.2024.12.1.66
The rapid proliferation of Internet of Things (IoT) devices has ushered in an era of unprecedented connectivity and convenience. However, it has also exposed these devices to a growing number of cyber threats. This study explores the evolving landscape of IoT cyber-attacks and investigates the role of artificial intelligence (AI) in enhancing the security of IoT ecosystems. IoT devices, ranging from smart thermostats and wearables to industrial sensors and autonomous vehicles, have become integral parts of our daily lives and critical infrastructures. With their increasing prevalence, they have become attractive targets for cybercriminals. Traditional security mechanisms often fall short in detecting and mitigating these attacks, making AI a promising solution. This research leverages AI techniques such as machine learning, deep learning, and anomaly detection to analyze patterns and trends in IoT cyber-attacks. By examining a vast dataset of real-world incidents, including botnet-driven DDoS attacks, device compromise, and data breaches, the study identifies common attack vectors and vulnerabilities inherent to IoT devices. Furthermore, it assesses the effectiveness of AI-driven intrusion detection and prevention systems in real-time threat identification.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- http://doi.org/10.55524/csistw.2024.12.1.66
- https://doi.org/10.55524/csistw.2024.12.1.66
- OA Status
- gold
- References
- 30
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4394895051
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4394895051Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.55524/csistw.2024.12.1.66Digital Object Identifier
- Title
-
RISK ASSESSMENT IN CYBER ATTACKS BASED ON INTERNET OF THINGS (IOT)Work title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2024Year of publication
- Publication date
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2024-03-15Full publication date if available
- Authors
-
Shubham Kumar, Dayashankar SinghList of authors in order
- Landing page
-
https://doi.org/10.55524/csistw.2024.12.1.66Publisher landing page
- PDF URL
-
https://doi.org/10.55524/csistw.2024.12.1.66Direct link to full text PDF
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.55524/csistw.2024.12.1.66Direct OA link when available
- Concepts
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Botnet, Computer security, Computer science, Internet of Things, Intrusion detection system, Denial-of-service attack, Anomaly detection, Wearable computer, Hacker, Cyber-attack, Honeypot, Identification (biology), The Internet, Artificial intelligence, World Wide Web, Embedded system, Biology, BotanyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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30Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.threats. | 32 |
| abstract_inverted_index.AI-driven | 167 |
| abstract_inverted_index.breaches, | 148 |
| abstract_inverted_index.detecting | 100 |
| abstract_inverted_index.detection | 123, 169 |
| abstract_inverted_index.enhancing | 51 |
| abstract_inverted_index.examining | 133 |
| abstract_inverted_index.including | 140 |
| abstract_inverted_index.intrusion | 168 |
| abstract_inverted_index.landscape | 38 |
| abstract_inverted_index.learning, | 118, 120 |
| abstract_inverted_index.leverages | 112 |
| abstract_inverted_index.promising | 108 |
| abstract_inverted_index.real-time | 174 |
| abstract_inverted_index.solution. | 109 |
| abstract_inverted_index.vehicles, | 70 |
| abstract_inverted_index.wearables | 64 |
| abstract_inverted_index.artificial | 47 |
| abstract_inverted_index.attractive | 89 |
| abstract_inverted_index.autonomous | 69 |
| abstract_inverted_index.identifies | 151 |
| abstract_inverted_index.incidents, | 139 |
| abstract_inverted_index.increasing | 84 |
| abstract_inverted_index.industrial | 66 |
| abstract_inverted_index.mechanisms | 95 |
| abstract_inverted_index.mitigating | 102 |
| abstract_inverted_index.prevention | 171 |
| abstract_inverted_index.real-world | 138 |
| abstract_inverted_index.techniques | 114 |
| abstract_inverted_index.Traditional | 93 |
| abstract_inverted_index.compromise, | 145 |
| abstract_inverted_index.ecosystems. | 56 |
| abstract_inverted_index.prevalence, | 85 |
| abstract_inverted_index.thermostats | 62 |
| abstract_inverted_index.Furthermore, | 161 |
| abstract_inverted_index.connectivity | 16 |
| abstract_inverted_index.convenience. | 18 |
| abstract_inverted_index.intelligence | 48 |
| abstract_inverted_index.investigates | 43 |
| abstract_inverted_index.botnet-driven | 141 |
| abstract_inverted_index.cyber-attacks | 41 |
| abstract_inverted_index.effectiveness | 165 |
| abstract_inverted_index.proliferation | 2 |
| abstract_inverted_index.unprecedented | 15 |
| abstract_inverted_index.cyber-attacks. | 131 |
| abstract_inverted_index.cybercriminals. | 92 |
| abstract_inverted_index.identification. | 176 |
| abstract_inverted_index.vulnerabilities | 156 |
| abstract_inverted_index.infrastructures. | 81 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5100755125 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| corresponding_institution_ids | https://openalex.org/I2799532815 |
| citation_normalized_percentile.value | 0.05497078 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |