MODEL DATA PENGAMBILAN KEPUTUSAN UNTUK ANALISIS DATA TINDAK KRIMINAL

Penelitian ini menghasilkan model arsitektur dan model data yang dapat mendukung aplikasi cerdas pengambilan keputusan yang berkaitan dengan analisis data tindak kejahatan. Aplikasi cerdas yang dimaksud adalah suatu aplikasi yang mampu melakukan analisis prediktif terhadap pola kriminal (crime pattern) dengan algoritma-algoritma data mining, tampilan multidimensional analisis dengan Online Analytical Analysis (OLAP), visualisasi dashboard yang mengacu pada Key Performance Indicator (KPI). Model arsitektur yang dihasilkan adalah model arsitektur yang mengintegrasikan banyak sumber data untuk analisis dan model data adalah hasil ekstraksi entitas dan atribut yang relevan dengan analisis yang yang dibutuhkan. Rancangan Data warehouse model yang dihasilkan menggunakan metode bottom-up Kimball. Metode pengumpulan data dengan cara survey ke lapangan yaitu ke pusat data kriminalisme dari instansi pemerintah dan pihak yang terkait (data sekunder), dan melalui interview terhadap pihak yang terkait (data primer). Pemodelan data menghasilkan star schema dengan tiga table fakta dan 13 tabel dimensi. Tabel fakta (fact table )yang dihasilkan yaitu: fact table case_analysis, fact table arrest_analysis dan fact table summon_analysis, sedangkan table dimensi yang dihasilkan terdiri dari: dim case, dim crime_scene, dim time, dim position, dim modus, dim DPO, dim visum, dim witness, dim police_officer, dim crime, dim convey, dim suspect, dim physical, dim iklim, dim demografi. Model schema yang dihasilkan digunakan untuk mendukung aplikasi cerdas dalam hal optimasi query untuk data yang besar dan tampilan multidimensional analisis

MODEL DATA PENGAMBILAN KEPUTUSAN UNTUK ANALISIS DATA TINDAK KRIMINAL

Penelitian ini menghasilkan model arsitektur dan model data yang dapat mendukung aplikasi cerdas pengambilan keputusan yang berkaitan dengan analisis data tindak kejahatan. Aplikasi cerdas yang dimaksud adalah suatu aplikasi yang mampu melakukan analisis prediktif terhadap pola kriminal (crime pattern) dengan algoritma-algoritma data mining, tampilan multidimensional analisis dengan Online Analytical Analysis (OLAP), visualisasi dashboard yang mengacu pada Key Performance Indicator (KPI). Model arsitektur yang dihasilkan adalah model arsitektur yang mengintegrasikan banyak sumber data untuk analisis dan model data adalah hasil ekstraksi entitas dan atribut yang relevan dengan analisis yang yang dibutuhkan. Rancangan Data warehouse model yang dihasilkan menggunakan metode bottom-up Kimball. Metode pengumpulan data dengan cara survey ke lapangan yaitu ke pusat data kriminalisme dari instansi pemerintah dan pihak yang terkait (data sekunder), dan melalui interview terhadap pihak yang terkait (data primer). Pemodelan data menghasilkan star schema dengan tiga table fakta dan 13 tabel dimensi. Tabel fakta (fact table )yang dihasilkan yaitu: fact table case_analysis, fact table arrest_analysis dan fact table summon_analysis, sedangkan table dimensi yang dihasilkan terdiri dari: dim case, dim crime_scene, dim time, dim position, dim modus, dim DPO, dim visum, dim witness, dim police_officer, dim crime, dim convey, dim suspect, dim physical, dim iklim, dim demografi. Model schema yang dihasilkan digunakan untuk mendukung aplikasi cerdas dalam hal optimasi query untuk data yang besar dan tampilan multidimensional analisis

The classification performance of Bayesian Networks Classifiers: a case study of detecting Denial of Service (DoS) attacks in cloud computing environments

In this research we propose a Bayesian networks approach as a promissory classification technique for detecting malicious traffic due to Denial of Service (DoS) attacks. Bayesian networks have been applied in numerous fields fraught with uncertainty and they have been proved to be successful. They have excelled tremendously in classification tasks i.e. text analysis, medical diagnoses and environmental modeling and management. The detection of DoS attacks has received tremendous attention in the field of network security. DoS attacks have proved to be detrimental and are the bane of cloud computing environments. Large business enterprises have been/or are still unwilling to outsource their businesses to the cloud due to the intrusive tendencies that the cloud platforms are prone too. To make use of Bayesian networks it is imperative to understand the ―ecosystem‖ of factors that are external to modeling the Bayesian algorithm itself. Understanding these factors have proven to result in comparable improvement in classification performance beyond the augmentation of the existing algorithms. Literature provides discussions pertaining to the factors that impact the classification capability, however it was noticed that the effects of the factors are not universal, they tend to be unique for each domain problem. This study investigates the effects of modeling parameters on the classification performance of Bayesian network classifiers in detecting DoS attacks in cloud platforms. We analyzed how structural complexity, training sample size, the choice of discretization method and lastly the score function both individually and collectively impact the performance of classifying between normal and DoS attacks on the cloud. To study the aforementioned factors, we conducted a series of experiments in detecting live DoS attacks launched against a deployed cloud and thereafter examined the classification performance in terms of accuracy of different classes of Bayesian networks. NSL-KDD dataset was used as our training set. We used ownCloud software to deploy our cloud platform. To launch DoS attacks, we used hping3 hacker friendly utility. A live packet capture was used as our test set. WEKA version 3.7.12 was used for our experiments. Our results show that the progression in model complexity improves the classification performance. This is attributed to the increase in the number of attribute correlations. Also the size of the training sample size proved to improve classification ability. Our findings noted that the choice of discretization algorithm does matter in the quest for optimal classification performance. Furthermore, our results indicate that the choice of scoring function does not affect the classification performance of Bayesian networks. Conclusions drawn from this research are prescriptive particularly for a novice machine learning researcher with valuable recommendations that ensure optimal classification performance of Bayesian networks classifiers

A Survey of Social Network Forensics

Social networks in any form, specifically online social networks (OSNs), are becoming a part of our everyday life in this new millennium especially with the advanced and simple communication technologies through easily accessible devices such as smartphones and tablets. The data generated through the use of these technologies need to be analyzed for forensic purposes when criminal and terrorist activities are involved. In order to deal with the forensic implications of social networks, current research on both digital forensics and social networks need to be incorporated and understood. This will help digital forensics investigators to predict, detect and even prevent any criminal activities in different forms. It will also help researchers to develop new models / techniques in the future. This paper provides literature review of the social network forensics methods, models, and techniques in order to provide an overview to the researchers for their future works as well as the law enforcement investigators for their investigations when crimes are committed in the cyber space. It also provides awareness and defense methods for OSN users in order to protect them against to social attacks

A Gender and Race Theoretical and Probabilistic Analysis of the Recent Title IX Policy Changes

On May 6th, 2020, after extensive public comment and review, the Department of Education published the final rule for the new Title IX regulations, which took effect in schools on August 14th. Title IX is the nearly fifty year old piece of the Education Amendments that prohibits sexual discrimination in federally funded schools. Several of these changes, such as the inclusion of live hearings and cross examination of witnesses, have been widely criticized by victims’ rights advocates for potentially retraumatizing victims of sexual assault and discouraging students from pursuing a Title IX claim. While the impact of the new regulations will not be known for certain any time soon, some of the consequences can be predicted using existing data and probability theory. This thesis discusses some of the common policy debates within Title IX as well as the racial dynamics of Title IX in order to frame an evaluation of these recent changes. We analyze some of the important issues in Title IX through both theoretical discussion as well as data based probability theory. We find that Title IX still centers the needs of accused students above victims of sexual violence, as demonstrated in some of the recent changes. The later parts of this thesis include an introduction to Bayesian networks, as well as an analysis of a Title IX data set through a Bayesian network we created. Finally, we hypothesize on what data is needed to properly analyze the recent changes to Title IX, and what the future of Title IX may look like under President Biden

Geospatial-based data and knowledge driven approaches for burglary crime susceptibility mapping in urban areas

The Damansara-Penchala region in Malaysia, is well-known for its high frequency of burglary crime and monetary loss based on the 2011-2016 geospatial burglary data provided by the Polis Diraja Malaysia (PDRM). As such, in order to have a better understanding of the components which influenced the burglary crime incidences in this area, this research aims at developing a geospatial-based burglary crime susceptibility mapping in this urban area. The spatial indicator maps was developed from the burglary data, census data and building footprint data. The initial phase of research focused on the development of the spatial indicators that influence the susceptibility of building towards the burglary crime. The indicators that formed the variable of susceptibility were first enlisted from the literature review. They were later narrowed down to the 18 indicators that were marked as important via the interview sessions with police officers and burglars. The burglary susceptibility mapping was done based on data-driven and knowledge-driven approaches. The data-driven burglary susceptibility maps were developed using bivariate statistics approach of Information Value Modelling (IVM), machine learning approach of Support Vector Machine (SVM) and Artificial Neural Network (ANN). Meanwhile, the knowledge-driven burglary susceptibility maps were developed using Relative Vulnerability Index (RVI) based on the input from experts. In order to obtain the best results, different parameter settings and indicators manipulation were established in the susceptibility modelling process. Both susceptibility modelling approaches were compared and validated with the same independent validation dataset using several accuracy assessment approaches of Area Under Curve - Receiver Operator Characteristic (AUC-ROC curve) and correlation matrix of True Positive and True Negative. The matrix is used to calculate the sensitivity, specificity and accuracy of the models. The performance of ANN and SVM were found to be close to one another with a sensitivity of 91.74% and 88.46%, respectively. However, in terms of specificity, SVM had a higher percentage than ANN at 57.59% and 40.46% respectively. In addition, the error term in classifying high frequency burglary building was also included as part of the measurements in order to decide on the best method. By comparing both classification results with the validation data, it was found that the ANN method has successfully classified buildings with high frequency of burglary cases to the high susceptibility class with no error at all, thus, proving it to be the best method. Meanwhile, the output from IVM had a very moderate percentage of sensitivity and specificity at 54.56% and 46.42% respectively. On the contrary, the knowledge-driven susceptibility map had a high percentage of sensitivity (86.51%) but a very low percentage of specificity (16.4%) which making it the least accurate model as it was not able to classify the high susceptible area correctly as compared to other modelling approaches. In conclusion, the results have indicated that the 18 indicators used in this research could be employed to successfully map the burglary susceptibility in the study area. Furthermore, it was also found that residential areas within the vicinity of Brickfields, Bangsar Baru, Hartamas and Bukit Pantai are consistent to be classified as high susceptible areas, meanwhile areas of Jalan Duta and Taman Tunku are both identified as the least susceptible areas across the modelling methods

Redefining Risk: Constructing and Validating a Bayesian Tool for Reducing Recidivism in the Era of Mass Incarceration

The actuarial assessment of recidivism risk is the foremost way that criminal justice systems balance managing caseloads, prioritizing rehabilitative services, and protecting public safety in the modern era of punishment. As such, many researchers have worked to develop new and better methods for more accurately classifying offender risk, but this has often happened at the expense of making theoretical contributions to the field of criminology. Furthermore, researchers have ignored some innovative methods that hold great promise for improving the accuracy of risk predictions. In this dissertation, I use Washington State Department of Corrections data to develop and validate the first recidivism risk assessment instrument to use Bayesian statistics, which I argue are particularly well suited to the task of prediction on a theoretical level. By comparing my results to those of a similar instrument developed using more common, frequentist regression models, I demonstrate the utility of Bayesian statistics for reducing model uncertainty and classification error. Findings also show that repeat property and drug offenders exhibit patterns of behavior that suggest a great deal of specialization in offending, whereas violent criminals display more versatile criminal behaviors, and highlight a troublesome tendency for risk assessments of all types to systematically overclassify recidivism risk among black men. Implications for life course, racial formation, and intersectional theories are discussed, along with recommendations for practice

Ubiquitous intelligence for smart cities: a public safety approach

Citizen-centered safety enhancement is an integral component of public safety and a top priority for decision makers in a smart city development. However, public safety agencies are constantly faced with the challenge of deterring crime. While most smart city initiatives have placed emphasis on the use of modern technology for fighting crime, this may not be sufficient to achieve a sustainable safe and smart city in a resource constrained environment, such as in Africa. In particular, crime series which is a set of crimes considered to have been committed by the same offender is currently less explored in developing nations and has great potential in helping to fight against crime and promoting safety in smart cities. This research focuses on detecting the situation of crime through data mining approaches that can be used to promote citizens' safety, and assist security agencies in knowledge-driven decision support, such as crime series identification. While much research has been conducted on crime hotspots, not enough has been done in the area of identifying crime series. This thesis presents a novel crime clustering model, CriClust, for crime series pattern (CSP) detection and mapping to derive useful knowledge from a crime dataset, drawing on sound scientific and mathematical principles, as well as assumptions from theories of environmental criminology. The analysis is augmented using a dual-threshold model, and pattern prevalence information is encoded in similarity graphs. Clusters are identified by finding highly-connected subgraphs using adaptive graph size and Monte-Carlo heuristics in the Karger-Stein mincut algorithm. We introduce two new interest measures: (i) Proportion Difference Evaluation (PDE), which reveals the propagation effect of a series and dominant series; and (ii) Pattern Space Enumeration (PSE), which reveals underlying strong correlations and defining features for a series. Our findings on experimental quasi-real data set, generated based on expert knowledge recommendation, reveal that identifying CSP and statistically interpretable patterns could contribute significantly to strengthening public safety service delivery in a smart city development. Evaluation was conducted to investigate: (i) the reliability of the model in identifying all inherent series in a crime dataset; (ii) the scalability of the model with varying crime records volume; and (iii) unique features of the model compared to competing baseline algorithms and related research. It was found that Monte Carlo technique and adaptive graph size mechanism for crime similarity clustering yield substantial improvement. The study also found that proportion estimation (PDE) and PSE of series clusters can provide valuable insight into crime deterrence strategies. Furthermore, visual enhancement of clusters using graphical approaches to organising information and presenting a unified viable view promotes a prompt identification of important areas demanding attention. Our model particularly attempts to preserve desirable and robust statistical properties. This research presents considerable empirical evidence that the proposed crime cluster (CriClust) model is promising and can assist in deriving useful crime pattern knowledge, contributing knowledge services for public safety authorities and intelligence gathering organisations in developing nations, thereby promoting a sustainable "safe and smart" city

Nonspeciesist criminology and the war against nonhuman animals

Purpose Drawing on nonspeciesist criminology (Beirne, 1999) and debates about the legal personhood status of nonhuman animals, this paper argues that we are currently engaged in a species war against nonhuman animals. It adopts a Clausewitzean interpretation of war to argue that War Crimes and Crimes against Humanity are being committed against nonhuman animals within the animal industrial complex. Design To analyze the war against nonhuman animals the paper draws on data from the Global Animal Protection Index; the Food and Agriculture Organization of the United Nations; and reports on Concentrated Animal Feeding Operations, as well as animal husbandry practices. Findings/interpretations The war against nonhuman animals is biopolitical. It involves the ‘industrialized reproduction’ of nonhuman animals (Wadiwel, 2015). Within this war, nonhuman animals are reduced to the status of property and their reproductive bodies are commodified within the food industry. This paper makes the case for extending Flynn and Hall’s (2017) proposal for a victimology of nonhuman animals to include victimology during war. To achieve this, nonhuman animals need to be recognized as sentient beings and afforded legal personhood status. This acknowledgement moves us closer towards developing a non-speciesist criminology of war