Bayu Adhi Tama

University of Maryland, Baltimore County (UMBC) · iHARP

Intrusion detection systems (IDSs) are intrinsically linked to a comprehensive solution of cyberattacks prevention instruments. To achieve a higher detection rate, the ability to design an improved detection framework is sought after, particularly when utilizing ensemble learners. Designing an ensemble often lies in two main challenges such as the...

A rising communication between modern industrial control infrastructure and the external Internet worldwide has led to a critical need to secure the network from multifarious cyberattacks. An intrusion detection system (IDS) is a preventive mechanism where new sorts of hazardous threats and malicious activities could be detected before harming the...

Vibration measurement and monitoring are essential in a wide variety of applications. Vibration measurements are critical for diagnosing industrial machinery malfunctions because they provide information about the condition of the rotating equipment. Vibration analysis is considered the most effective method for predictive maintenance because it is...

The mission of an intrusion detection system (IDS) is to monitor network activities and assess whether or not they are malevolent. Specifically, anomaly-based IDS can discover irregular activities by discriminating between normal and anomalous deviations. Nonetheless, existing strategies for detecting anomalies generally rely on single classificati...

Intrusion detection systems (IDS) play a pivotal role in computer security by discovering and repealing malicious activities in computer networks. Anomaly-based IDS, in particular, rely on classification models trained using historical data to discover such malicious activities. In this paper, an improved IDS based on hybrid feature selection and t...

The melting of ice sheets significantly contributes to sea level rise, highlighting the crucial need to comprehend the structure of ice for climate benefits. The stratigraphy of ice sheets revealed through ice layer radargrams gives us a window into historical depth-age correlations and accumulation rates. Harnessing this knowledge is not only cruc...

This paper focuses on the discovery of unusual spatiotemporal associations across multiple phenomena from distinct application domains in a spatial neighborhood where each phenomenon is represented by anomalies from the domain. Such an approach can facilitate the discovery of interesting links between distinct domains, such as links between traffic...

Ice-penetrating radar surveys have been conducted across the Greenland Ice Sheet since the 1960s, producing radargrams that measure ice thickness and detect the ice sheet's radios-tratigraphy. However, these radargrams are relatively under-explored and not yet fully annotated, mapped, or interpreted glaciologically. We aim to move towards automatic...

The clinical application of a real-time artificial intelligence (AI) image processing system to diagnose upper gastrointestinal (GI) malignancies remains an experimental research and engineering problem. Understanding these commonly used technical techniques is required to appreciate the scientific quality and novelty of AI studies. Clinicians freq...

Culture is a collection of connected and potentially interactive patterns that characterize a social group or a passed-on idea that people acquire as members of society. While offline activities can provide a better picture of the geographical association of cultural traits than online activities, gathering such data on a large scale has been chall...

As a system capable of monitoring and evaluating illegitimate network access, an intrusion detection system (IDS) profoundly impacts information security research. Since machine learning techniques constitute the backbone of IDS, it has been challenging to develop an accurate detection mechanism. This study aims to enhance the detection performance...

Given their escalating number and variety, combating malware is becoming increasingly strenuous. Machine learning techniques are often used in the literature to automatically discover the models and patterns behind such challenges and create solutions that can maintain the rapid pace at which malware evolves. This article compares various tree-base...

Outcome-based predictive process monitoring concerns predicting the outcome of a running process case using historical events stored as so-called process event logs. This prediction problem has been approached using different learning models in the literature. Ensemble learners have been shown to be particularly effective in outcome-based business...

Detecting cardiac abnormalities between 14 and 28 weeks of gestation with an apical four-chamber view is a difficult undertaking. Several unfavorable factors can prevent such detection, such as the fetal heart’s relatively small size, unclear appearances in anatomical structures (e.g., shadows), and incomplete tissue boundaries. Cardiac defects wit...

The transfer fees of sports players have become astronomical. This is because bringing players of great future value to the club is essential for their survival. We present a case study on the key factors affecting the world's top soccer players' transfer fees based on the FIFA data analysis. To predict each player's market value, we propose an imp...

Gradient boosting ensembles have been used in the cyber-security area for many years; nonetheless, their efficacy and accuracy for intrusion detection systems (IDSs) remain questionable, particularly when dealing with problems involving imbalanced data. This article fills the void in the existing body of knowledge by evaluating the performance of g...

Detecting and preventing industrial machine failures are significant in the modern manufacturing industry because machine failures substantially increase both maintenance and manufacturing costs. Recently, state-of-the-art deep learning techniques that use acoustic signals have been widely applied to solve industrial machine malfunction detection p...

Classifier ensembles have been utilized in the industrial cybersecurity sector for many years. However, their efficacy and reliability for intrusion detection systems remain questionable in current research, owing to the particularly imbalanced data issue. The purpose of this article is to address a gap in the literature by illustrating the benefit...

This short communication provides a discourse emerged after reading “Stacking ensemble based deep neural networks modeling for effective epileptic seizure detection, Expert Systems with Applications, 148, 113239, 2020.” The discussed paper proposes a novel application of stacking-based ensemble for seizure detection, where several deep neural netwo...

An anomaly-based intrusion detection system (A-IDS) provides a critical aspect in a modern computing infrastructure since new types of attacks can be discovered. It prevalently utilizes several machine learning algorithms (ML) for detecting and classifying network traffic. To date, lots of algorithms have been proposed to improve the detection perf...

Classification algorithms are widely taken into account for clinical decision support systems. However, it is not always straightforward to understand the behavior of such algorithms on a multiple disease prediction task. When a new classifier is introduced, we, in most cases, will ask ourselves whether the classifier performs well on a particular...

A robust authentication system is seen as a solution to ensure security within IoT devices since such devices are vulnerable to attack and data insecurity. In addition, the system could potentially bring some unpleasant experience to the user. Especially, if it is implemented in an everyday-used device. As the ability of the IoT devices to capture...

As a specialized sub-domain of machine learning, deep learning has secured enormous implications in various real-world applications, and in noise and vibration engineering field is no exception. Deep learning has shown a promising result with near-to-human-level accuracy. The objective of this paper is to give an overview of the state-of-the-art de...

There is a growing need for empirical benchmarks that support researchers and practitioners in selecting the best machine learning technique for given prediction tasks. In this article, we consider the next event prediction task in business process predictive monitoring, and we extend our previously published benchmark by studying the impact on the...

There is a growing need for empirical benchmarks that support researchers and practitioners in selecting the best machine learning technique for given prediction tasks. In this paper, we consider the next event prediction task in business process predictive monitoring and we extend our previously published benchmark by studying the impact on the pe...

In the era of the fourth industrial revolution (Industry 4.0) and the Internet of Things (IoT), real-time data is enormously collected and analyzed from mechanical equipment. By classifying and characterizing the measured signals, the fault condition of mechanical components could be identified. However, most current health monitoring techniques ut...

Background Although great advances in artificial intelligence for interpreting small bowel capsule endoscopy (SBCE) images have been made in recent years, its practical use is still limited. The aim of this study was to develop a more practical convolutional neural network (CNN) algorithm for the automatic detection of various small bowel lesions....

Objectives: To present an up-to-date survey of the use of artificial intelligence (AI) in the field of otorhinolaryngology, with respect to opportunities, research challenges, and research directions. Methods: We searched PubMed, the Cochrane Central Register of Controlled Trials, Embase, and the Web of Science. We initially retrieved 458 articl...

Artificial intelligence has become the primary issue in the era of Industry 4.0, accelerating the realization of a self-driven smart factory. It is transforming various manufacturing sectors including the assembly line for a camera lens module. The recent development of bezel-less smartphones necessitates a large-scale production of the camera lens...

Coronary heart disease (CHD) is one of the severe health issues and is one of the most common types of heart diseases. It is the most frequent cause of mortality across the globe due to the lack of a healthy lifestyle. Owing to the fact that a heart attack occurs without any apparent symptoms, an intelligent detection method is inescapable. In this...

Cloud computing supports many unprecedented cloud-based vehicular applications. To improve connectivity and bandwidth through programmable networking architectures, Software-Defined (SD) Vehicular Network (SDVN) is introduced. SDVN architecture enables vehicles to be equipped with SDN OpenFlow switch on which the routing rules are updated from a SD...

A Web attack protection system is extremely essential in today’s information age. Classifier ensembles have been considered for anomaly-based intrusion detection in Web traffic. However, they suffer from an unsatisfactory performance due to a poor ensemble design. This paper proposes a stacked ensemble for anomaly-based intrusion detection systems...

Continuous casting is the procedure of the successive casting for solidification of the steel, which contains several cooling processes along the caster to coagulate the molten steel. It is such a rule of thumb that strand surface quality and casting productivity is highly dependent on temperature control. A finite-difference method is one of estim...

Basic safety message (BSM) are messages that contain core elements of a vehicle such as vehicle’s size, position, speed, acceleration and others. BSM are lightweight messages that can be regularly broadcast by the vehicles to enable a variety of applications. On the other hand, event-driven message (EDM) are messages generated at the time of occurr...

Steel defect diagnostics is considerably important for a steel-manufacturing industry as it is strongly related to the product quality and production efficiency. Product quality control suffers from a real-time diagnostic capability since it is less-automatic and is not reliable in detecting steel surface defects. In this study, we propose a relati...

Predictive analytics is an essential capability in business process management to forecast future status and performance of business processes. In this paper, we focus on one particular predictive monitoring task that is solved using classification techniques, i.e. predicting the next event in a case. Several different classifiers have been recentl...

Continuous casting is the process of concretion of hot molten liquid in a continuous groundwork. Owing to the process of the secondary cooling possesses a critical impact on strand surface quality and casting productivity, the temperature control has always been a major issue in steel industry. The paper deals with a hybrid model of convolutional n...

Detection and diagnosis of material degradation is of a complex and challenging task since it is presently hand-operated by human. Therefore, it leads to misinterpretation and avoids correct classification and diagnosis. In this paper, we develop a computer-assisted detection method of material failure by utilizing a deep learning approach. A deep...

This paper proposes an improved detection performance of anomaly-based intrusion detection system (IDS) using gradient boosted machine (GBM). The best parameters of GBM are obtained by performing grid search. The performance of GBM is then compared with the four renowned classifiers, i.e. random forest, deep neural network, support vector machine,...

Diabetes is a lifestyle-driven disease which has become a critical health issue worldwide. In this paper, we conduct an exploratory study about early detection method of diabetes mellitus using various ensemble learning techniques. Eight tree-based machine learning algorithms, i.e. classification and regression tree, decision tree (C4.5), reduced e...

Lifestyle-driven disease such as diabetes mellitus has become a serious health problem worldwide. We propose the fusion of neural network-based classifiers, i.e., neural network and support vector machine to assist in early detection of diabetes mellitus. These classifiers are combined to produce the final prediction. However, when considering a nu...

Prediction of undesirable learner’s behaviors is an important issue in the distance learning system as well as the conventional university. This paper is devoted to benchmark ensemble of weak classifiers (decision tree, random forest, logistic regression, and CART) against single classifier models to predict inactive student. Two real-world dataset...

Phishing website has become a crucial concern in cyber security applications. It is performed by fraudulently deceiving users with the aim of obtaining their sensitive information such as bank account information, credit card, username, and password. The threat has led to huge losses to online retailers, e-business platform, financial institutions,...

The most challenging research topic in the field of intrusion detection system (IDS) is anomaly detection. It is able to repeal any peculiar activities in the network by contrasting them with normal patterns. This paper proposes an efficient random forest (RF) model with particle swarm optimization (PSO)-based feature selection for IDS. The perform...

The emerging greenhouse technology in agriculture based on Internet of Things (IoT) used for remote monitoring and automation has been rapidly developed. But it still has major concern about security and privacy, due to the large scale of disseminating nature of its network. To overcome these security challenges, we use blockchain which allows the...

Lifestyle-driven disease such as diabetes mellitus has become a serious health problem worldwide. We propose the fusion of neural network-based classifiers, i.e., neural network and support vector machine to assist in early detection of diabetes mellitus. These classifiers are combined to produce the final prediction. However, when considering a nu...

A variety of attacks in the transportation layer of IoT network seeks for a detection and prevention mechanism such as intrusion detection systems (IDSs). Anomaly detection is one of the most demanding task in IDSs. It requires a robust classifier model which is able to detect different kinds of attacks intelligently. This paper addresses deep neur...

A variety of attacks in the transportation layer of IoT network seeks for a detection and preventionmechanism such as intrusion detection systems (IDSs). Anomaly detection is one of the most demandingtask in IDSs. It requires a robust classifier model which is able to detect different kinds ofattacks intelligently. This paper addresses deep neural...

Intrusion detection systems (IDSs) are crucial in this overwhelming increase of attacks on the computing infrastructure. It intelligently detects malicious and predicts future attack patterns based on the classification analysis using machine learning and data mining techniques. This paper is devoted to thoroughly evaluate classifier ensembles for...

Network infrastructures are in jeopardy of suffering nowadays since a number of attacks have been developed and grown up enormously. In order to get rid of such security threats, a defense mechanism is much sought-after. This paper proposes an improved model of intrusion detection by using two-level classifier ensemble. The proposed model is made u...

Anomaly detection is one approach in intrusion detection systems (IDSs) which aims at capturing any deviation from the profiles of normal network activities. However, it suffers from high false alarm rate since it has impediment to distinguish the boundaries between normal and attack profiles. In this paper, we propose an effective anomaly detectio...

Blockchain technology has been known as a digital currency platform since the emergence of Bitcoin, the first and the largest of the cryptocurrencies. Hitherto, it is used for the decentralization of markets more generally, not exclusively for the decentralization of money and payments. The decentralized transaction ledger of blockchain could be em...

Anomaly detection is very crucial in an intrusion detection task since it has capability to discover new types of attacks. The major challenges of anomaly detection are how to maximize the accuracy while maintaining low positive rate. In this paper, we propose new approach on anomaly detection using multi-level classifier ensembles. We employ an en...

The goal of intrusion detection system (IDS) is to detect various types of malicious against computer networks. Unlike conventional firewall, IDS identifies attacks intelligently using analytical approaches, i.e. data mining and machine learning techniques. Specifically, a current focus of research in machine learning is the combination of multiple...

An intrusion detection system (IDS) plays a critical role in computer protection systems. Numerous approaches such as machine learning, data mining, and statistical techniques have been examined for IDS task. Recent studies reveal that combining multiple classifiers, i.e., classifiers ensemble, may possess better performance compared to single clas...

Web attack protection system is extremely essential in today's information deluge. We propose an improved detection approach against anomalous request in a web application using gradient boosted machine (GBM). The performance of GBM is evaluated in terms of detection and false alarm rate. Based on the experimental result using 10-cross validation,...

This paper is devoted to discover the appropriate base classifier algorithms while employing Rotation Forest as an ensemble learning method for intrusion detection system (IDS) in wireless network. Twenty different classification algorithms are involved in the experiment and their detection performances are assessed using the value of area under re...

DoS attacks become a serious attack so as resource protection against this kind of attack is a compulsory task. The major challenge on designing detection scheme using machine learning technique is how to maximize detection rate with lower false alarm. In this paper, we employ and analyze the performance of multiple classifier system (MCS) to detec...

This paper attempts to classify papers concerning DoS/DDoS attack detection using data mining techniques. Thirty five papers were selected and carefully reviewed by authors from two online journal databases. Each of selected paper was classified based on the function of data mining such as association, classification, clustering, and hybrid methods...

Vehicle safety related applications which are drawing increasing attention in public have attracted extensive research both in academic and industry areas. However, it is assumed that one service is offered by one provider, thus forcing vehicle drivers to subscribe to several service providers within limited computation capabilities. In this paper,...

The inactive student rate is becoming a major problem in most open universities worldwide. In Indonesia, roughly 36% of students were found to be inactive, in 2005. Data mining had been successfully employed to solve problems in many domains, such as for educational purposes. We are proposing a method for preventing inactive students by mining know...

Cross-selling plays important roles in the field of business and marketing. Cross-selling offers valuable marketing strategy through increasing sales order size from single-product to multiple-product. This paper proposes a cross-selling strategy based on association rules and sequential pattern obtained by mining transaction data in the enterprise...

Managing customer satisfaction has become a crucial issue in fast-food industry. This study aims at identifying determinant factor related to customer satisfaction in fast-food restaurant. Customer data are analyzed by using data mining method with two classification techniques such as decision tree and neural network. Classification models are dev...

Due to the numerous attacks over the Internet, several early detection systems have been developed to prevent the network from huge losses. Data mining, soft computing, and machine learning are employed to classify historical network traffic whether anomaly or normal. This paper presents the experimental result of network anomaly detection using pa...

Hepatitis is chronic disease that becomes major problem in developing countries. Health experts estimate that more than 185 billion people have chronic hepatitis worldwide. This paper attempts to detect major disease such as hepatitis in public hospital using ensemble methods. Several ensemble techniques were applied to acquire knowledge from patie...

Diabetes is a chronic disease and major problem of morbidity and mortality in developing countries. The International Diabetes Federation estimates that 285 million people around the world have diabetes. This total is expected to rise to 438 million within 20 years. Type-2 diabetes mellitus (T2DM) is the most common type of diabetes and accounts fo...

Diabetes is a chronic disease and a major problem of morbidity and mortality in developing countries. The International Diabetes Federation (IDF) estimates that 285 million people around the world have diabetes. This total is expected to rise to 438 million within 20 years. Type 2 diabetes (TTD) is the most common type of diabetes and accounts for...

Cross-selling's product determination achieves through out an analysis of purchasing data by using business intelligence tools such as data mining. Data mining is part of Analytical CRM used to discover data pattern, while market basket analysis method with association rules technique are a data mining task used to discover the candidate of cross-s...