A supervised learning approach for criminal identification using similarity measures and K-Medoids clustering

Bharathi, S. T.; Indrani, B.; Prabakar, M. Amutha

doi:10.1109/icicict1.2017.8342639

Cited by 6 publications

(4 citation statements)

References 8 publications

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“…With the proliferation of freely available crime data, there have been many AI and statistical algorithms applied to these datasets (Bharathi et al, 2018;Isafiade et al, 2015;Joshi et al, 2018;Keyvanpour et al, 2011;Shermila et al, 2018). The areas that offer the most promise are applications of big data and data mining to "traditional" problems of profiling, segmentation and prediction, for instance, for: real time crime forecasting; profiling (offender, threats/crime risk, gang membership, recidivism); predictive policing threats and opportunities; producing real-time accurate results from big data streams, and drift detection techniques to cope with changing environments.…”

Section: Theme 1 Profiling Segmentation Forecasting and Predictionmentioning

confidence: 99%

Themes in data mining, big data, and crime analytics

Oatley

2021

WIREs Data Min & Knowl

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This article examines the impact of new AI‐related technologies in data mining and big data on important research questions in crime analytics. Because the field is so broad, the review focuses on a selection of the most important topics. Challenges for information management, and in turn law and society, include: AI‐powered predictive policing; big data for legal and adversarial decisions; bias using big data and analytics in profiling and predicting criminality; forecasting crime risk and crime rates; and, regulating AI systems. This article is categorized under: Algorithmic Development > Spatial and Temporal Data Mining Fundamental Concepts of Data and Knowledge > Big Data Mining Technologies > Artificial Intelligence Application Areas > Data Mining Software Tools

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Section: Theme 1 Profiling Segmentation Forecasting and Predictionmentioning

confidence: 99%

Themes in data mining, big data, and crime analytics

Oatley

2021

WIREs Data Min & Knowl

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“…Despite its success in cyber defense, machine learning is becoming a growing concern in data security [3]. Cloud computing, networking, and evolutionary computation have grown rapidly as a result of remarkable advances in computing, storage, and computational technologies [4]. As the world becomes more digitalized, there is a growing requirement for comprehensive and advanced privacy and security issues, as well as tactics to combat security threats that are getting more complex [5].…”

Section: Introductionmentioning

confidence: 99%

Cyber-security attack prediction using cognitive spectral clustering technique based on simulated annealing search

Rajalakshmi,

E.,

Parameshwari

et al. 2023

ACE

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Data protection and security is a big challenging portion in a modern technical world against the cyber-attacks like; ransomware, man-in-the-middle, DDoS, etc. In order to overcome this scenario, there are lot of artificial intelligence framework have been introduced to detect and classify the cyber-attacks. In particular, neural networks, with their solid speculation execution ability, are capable to address an extensive variety of cyber-attacks. This article frames the training and testing of a neural network group such a way to deal with detection of cyber-attack using cognitive spectral clustering technique based on simulated annealing search method. The optimization of individual networks can be made by using adaptive memetic algorithm with simulated annealing search. It is used to enhance the neural network weights and hidden neurons respectively. This algorithm is a combination of both local and global search enhancement method and used to get rid of the premature convergence, and used to achieve the adaptive search output. The testing outcome of the proposed framework shows a better result 99.5% of overall accuracy, and effectively adaptive in terms of detecting the cyber-attacks.

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“…Other applications include detecting spam and network attacks [10], detecting phishing attempts against banks [11], and increasing sexual crimes on social media [12] Stock prediction [13], risk mapping [14], and www.ijacsa.thesai.org cyber profiling [15] are some of the sectors where these technologies have been used. Implementation areas include predicting crime trends and patterns [16], criminal identity detection [17], and crime prevention [18].…”

Section: Introductionmentioning

confidence: 99%

A Framework for Crime Detection and Diminution in Digital Forensics (CD3F)

Singh¹,

Singh²,

Vege³

et al. 2022

IJACSA

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Cyber-attacks have become one of the world's most serious issues. Every day, they wreak serious financial harm to governments and people. As cyber-attacks become more common, so does cyber-crime. Identifying cyber-crime perpetrators and understanding attack tactics are critical in the battle against crime and criminals. Cyber-attack detection and prevention are difficult undertakings. Researchers have lately developed security models and made forecasts using artificial intelligence technologies to solve these concerns. In the literature, the authors explained numerous ways of predicting crime. They, on the other hand, have a problem forecasting cyber-crime and cyber-attack strategies. Here, in this paper author proposed a digital forensic investigation procedure that deals with cybercrime. In this investigation, the process author explains digital forensics techniques for ensuring that digital evidence is located, collected, preserved, evaluated, and reported in such a way that the evidence's integrity is preserved. These sequential digital forensic stages affect a standard and accepted digital forensic investigation procedure, and each phase is influenced by sequential occurrences, with each event relying on tasks. Digital forensics investigation is a technique for ensuring that digital evidence is handled in such a way that the evidence's integrity is preserved. Sequential digital forensic stages affect a standard and accepted digital forensic investigation procedure, and each phase is influenced by sequential occurrences, with each event relying on tasks.

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A supervised learning approach for criminal identification using similarity measures and K-Medoids clustering

Cited by 6 publications

References 8 publications

Themes in data mining, big data, and crime analytics

Themes in data mining, big data, and crime analytics

Cyber-security attack prediction using cognitive spectral clustering technique based on simulated annealing search

A Framework for Crime Detection and Diminution in Digital Forensics (CD3F)

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