Authentication through electrocardiogram signals based on emotions-a step towards ATM security

Gupta, Shivangi; Chowdhary, Sunil Kumar

doi:10.1109/icrito.2017.8342467

Cited by 3 publications

(3 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ethical issues can be dealt with by writing appropriate code and testing issues properly. A hybrid system among AI and humans is an intelligent solution to implement machine intelligence [8]. Knowledge Engineering (KE) reviews the structure of a decision to recognize how a conclusion is attained.…”

Section: Disparate and Data-driven System Challenges Experienced By Internet Connected Systems (Ics) And Different Threat Intelligence Scmentioning

confidence: 99%

“…In spite of various prevention techniques such as installing antivirus, encryption, and firewalls, organizations are still experiencing intrusions at an alarming rate. The most troubled companies that require threat detection are commercial banks, credit card holders, telecommunication sector and many more [8]. Some of the common threats are:  The disruptive use of information technology services by attackers to fulfill their ideological agenda.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Cyber Security Threat Intelligence using Data Mining Techniques and Artificial Intelligence

Gupta*¹,

Sabitha²,

Punhani³

2019

IJRTE

View full text Add to dashboard Cite

Threat intelligence is the procurement of evidence-based knowledge about current or potential threats. The interest of threat intelligence comprises of advancement in efficiency and boosting effectiveness in terms of analytical and prevention capabilities. Cybersecurity represents serious interest for numerous organizations because maximum of them are using Internet-connected data devices which are opening doors for cyber attackers. Outstanding threat intelligence within the cyber sphere requests for the knowledge base of threat information and a thoughtful way to represent this knowledge. This study proposes a clear rationale of significant artificial intelligence (AI) techniques used for recognizing a cyber-attack. Data analysis can be formulated to guide industries and Internet-connected systems such as smartphones or robotic factories on what to do in the appearance of an incident. AI techniques will analyze past incidents and summarize knowledge from experts and will continue to adapt or reform new branches as it reviews from the new incidents. In addition, various data mining approaches used in boosting threat truthfulness in cybersecurity data are also studied. To conclude, we discussed that; AI will robotize the collation of machine-readable external threats and will improve the efficiency and accuracy of the data for each smart organization’s specific framework.

show abstract

Section: Disparate and Data-driven System Challenges Experienced By Internet Connected Systems (Ics) And Different Threat Intelligence Scmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cyber Security Threat Intelligence using Data Mining Techniques and Artificial Intelligence

Gupta*¹,

Sabitha²,

Punhani³

2019

IJRTE

View full text Add to dashboard Cite

show abstract

“…Notwithstanding the use of many preventative measures, such as antivirus software, encryption, and firewalls, enterprises persistently encounter a substantial amount of intrusions. Companies in the commercial banking, credit card, and communications sectors are particularly susceptible and need effective threat detection measures (Gupta & Chowdhary, 2017). The increase in cybersecurity threats in recent years has emphasized the need for automated threat analysis at every level of an organization or company (Sisiaridis & Markowitch, 2018).…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Applications in Cyber Threat Intelligence: A Comprehensive Review

Naseer

2024

ABBDM

View full text Add to dashboard Cite

Contemporary institutions are consistently confronted with fraudulent activities that exploit weaknesses in interconnected systems. Securing critical data against unauthorized access by hackers and other cybercriminals requires the application of robust cybersecurity protocols. As the number and complexity of cyber threats continue to grow, innovative prevention strategies are required. The objective of this study is to investigate the correlation between machine learning (ML) and cyber threat intelligence (CTI) to improve cybersecurity strategies. For the detection of anomalies, the analysis of malware, and the prediction of threats, ML techniques are indispensable in industries including retail, finance, healthcare, and cybersecurity. By employing critical threat information (CTI), security teams can gain a comprehensive understanding of adversary strategies and bolster defensive measures; thus, they play a pivotal role in proactive defense. Integration of ML and CTI facilitates exhaustive analysis by automating the acquisition, processing, and categorization of data. However, obstacles arise when confronted with issues such as risk assessment, the requirement for precise data, and the initial stages of machine learning implementation in business intelligence. In this paper, we present an extensive examination of the current literature concerning the visualization of Cyber Threat Intelligence (CTI) and the utilization of Machine Learning (ML. Therefore, the report concludes with an analysis of emergent threats, potential future applications of AI and ML in the field of cyber threat intelligence, and the critical contribution of machine learning to the improvement of cybersecurity.

show abstract

Enhancement of Feature Extraction in Image Quality

Patil¹,

Patil²

2019

2019 Third International Conference on I-Smac (IoT in Social, Mobile, Analytics and Cloud) (I-Smac)

View full text Add to dashboard Cite

Authentication through electrocardiogram signals based on emotions-a step towards ATM security

Cited by 3 publications

References 5 publications

Cyber Security Threat Intelligence using Data Mining Techniques and Artificial Intelligence

Cyber Security Threat Intelligence using Data Mining Techniques and Artificial Intelligence

Machine Learning Applications in Cyber Threat Intelligence: A Comprehensive Review

Enhancement of Feature Extraction in Image Quality

Contact Info

Product

Resources

About