Mixed Integer Program for e-Wallet Security Based on Suspicious Transaction Detection

<p>Accessing and retrieving users’ browser and network information is a common practice used by advertisers and many online services to deliver targeted ads and explicit improved services to users belonging to a particular group. They provide a great deal of information about a user’s geographical location, ethnicity, language, culture and general interests. However, in the same way these techniques have proven effective in advertising services, they can be used by attackers to launch targeted attacks against specific user groups. Targeted attacks have been proven more effective against user groups than their blind untargeted counterparts (e.g.spam, phishing). Their detection is more challenging as the detection tools need to be located within the targeted user group. This is one of the challenges faced by security researchers and organisations involved in the detection of new malware and exploits, using client honeypots. Client honeypots are detection systems used in the identification of malicious web sites. The client honeypot needs to mimic users in a pre-defined location, system, network and personality for which the malware is intended. The case is amplified by the use of Browser Exploit Packs/kits (BEPs), supporting these features. BEPs provide simplicity in deployment of targeted malicious web sites. They allow attackers to utilise specific geographical locations, network information, visit patterns or browser header information obtained from a visiting user to determine if a user should be subjected to an attack. Malicious web sites that operate based on targeted techniques can disguise themselves as legitimate web sites and bypass detection. Benign content is delivered to attacker-specified users while avoiding delivery to suspicious systems such as well-known or possible subnets that may host client honeypots. A client honeypot deployed in a single location with a single IP address will fail to detect an attack targeted at users in different demographic and network subnets. Failure in detection of such attacks results in high rates of false negatives which affect all honeypots regardless of detection technique or interaction level. BEPs are hugely popular and most include tracking features. The number of malicious web sites that utilise these features is currently unknown. There are very few studies that have addressed identifying the rate and number of malicious web sites utilising these techniques and no available client honeypot system is currently able to detect them. Any failure to detect these web sites will result in unknown numbers of users being exploited and infected with malware. The false negatives resulting from failing to detect these web sites can incorrectly be interpreted as a decline in the number of attacks. In this work, a study of information that can potentially expose users to targeted attack through a browser is examined through experimental analysis. Concrete approaches by attackers to obtain user-specific information in the deployment of targeted attacks through browsers are discussed and analysed. We propose a framework for designing a client honeypot capable of detecting geolocation attacks. Our framework relies on HAZard and OPerability (HAZOP) studies to identify components of the client honeypot, its processes and attributes of the experimental setup which could potentially introduce bias into our study. Any potential bias neglected, would affect the results of our real-world experiments and undermine our analysis through deviation from the intent of the study. To facilitate in our experiments, we developed a low interaction client honeypoy (YALIH) and performed real-world experiments on large selection of web sites. We determined the popularity of targeted malicious attacks based on likely attributes of a visiting user’s system. Our approach relies on previous research performed in the area of online spam detection which has similar attributes to malicious web sites. Our experiments show that referer, via, X-Forwarded-For and browser language attributes of HTTP protocol header, retrieval behaviour (i.e. IP tracking) and geographical location of a visitor identified by an IP address can be used in a targeted attack. These attributes can have significant effect on the number of detected malicious web sites in a study and should therefore be reliably controlled in an experimental setup. This findings in this research can potentially reduce false negative rates in all types of client honeypots, measurement studies of malicious malicious web sites and help researchers and malware analysts capture and analyse new malware and exploit samples from malicious web sites.</p>

In this paper, we consider an optimization problem solving for model predictive control (MPC) of a modular multilevel converter (MMC). An MMC consists of a large number of submodules. The objective of the MPC is to determine the best switching sequences for the submodules in the MMC to track the phase current references for T time horizons. The MPC is formulated as a nonlinear mixed-integer programming (MIP) problem with the on/off status of submodules as binary decision variables and MMC dynamic states such as phase currents, circulating currents and submodule capacitor voltages as continuous decision variables. With a large number of submodules and a large number of time horizons, the dimension of the nonlinear MIP problem becomes difficult to handle. Our contribution is to formulate this problem and solve this problem using Bender's decomposition. An example 5-level single-phase MMC is demonstrated for the proposed algorithm.

This study investigates the effect of transaction costs and opportunistic behavior on the sustainability of Micro, Small, and Medium Enterprises (MSMEs) in Indonesia, with operational efficiency serving as a mediating variable. A quantitative research approach was employed, involving 185 MSME respondents selected through purposive sampling. Data were collected using a structured questionnaire measured on a Likert scale and analyzed using Structural Equation Modeling–Partial Least Squares (SEM-PLS 3). The results indicate that transaction costs and opportunistic behavior have significant negative effects on both operational efficiency and MSME sustainability. Conversely, operational efficiency demonstrates a significant positive effect on sustainability and serves as a partial mediator in the relationship between transaction costs, opportunistic behavior, and MSME sustainability. These findings suggest that high transaction costs and opportunistic practices hinder business performance, while efficient operational management enhances long-term viability. The study contributes to the literature by integrating economic and behavioral perspectives within MSME sustainability research and offers practical implications for policymakers and business practitioners to improve operational performance, reduce inefficiencies, and strengthen the resilience of MSMEs in Indonesia.

"Test and Standard Precautions"-Is It Enough to Protect Us From False-Negative Severe Acute Respiratory Syndrome Coronavirus 2 Test Results?

A cutting plane algorithm for the site layout planning problem with travel barriers

Finding an energy efficient path for plug-in electric vehicles with speed optimization and travel time restrictions

In this paper, we ask whether it is possible to build an IP address to geographic location mapping service for Internet hosts. Such a service would enable a large and interesting class of location-aware applications. This is a challenging problem because an IP address does not inherently contain an indication of location.We present and evaluate three distinct techniques, collectively referred to as IP2Geo, for determining the geographic location of Internet hosts. The first technique, Geo Track, infers location based on the DNS names of the target host or other nearby network nodes. The second technique, GeoPing, uses network delay measurements from geographically distributed locations to deduce the coordinates of the target host. The third technique, GeoCluster, combines partial (and possibly inaccurate) host-to-location mapping information and BGP prefix information to infer the location of the target host. Using extensive and varied data sets, we evaluate the performance of these techniques and identify fundamental challenges in deducing geographic location from the IP address of an Internet host.

Portfolio rebalancing with an investment horizon and transaction costs

Drawing on the Transaction Cost Economics (TCE) and taking the perspective of a developing economy, the study examined the impacts of governance mechanisms (GM-formal control and social control) on supply chain (SC) performance (operational efficiency and service & market performance). Data were collected using questionnaires from 152 firms operating in the most industrialized regions of Ghana. Structural equation modelling technique (using LISREL 8.5) was employed in estimating the study’s model. The study finds significant positive associations between formal control and operational efficiency; and between social control and service & market performance. The study also finds that within the research context, social control is antecedent of (adherence to) formal control; and that the effect of social control on operational efficiency is fully channelled via formal control. These findings imply that firms’ ability to institute and implement GMs would present them opportunities to enhance the performance of their SCs. Theoretically, the results suggest the potential mediating role of formal control in the link between social control and SC performance; which means that having in place formal controls is necessary for firms in this part of the world (who mostly rely on social control) to better experience benefits arising out of social relationships.

Credit card fraud has emerged as a significant issue within the digital payment framework due to an increase in online payments. This paper provides a fraud detection mechanism using ML and DL methods focusing on a hybrid model based on CNN and ANN. For the purpose of the research project, this study utilized the European credit card transaction dataset. They also utilized SMOTE for resolving any data imbalance which aided in efficient model fit, reliability and sensitive fraud detection. The proposed CNN-ANN model achieved an accuracy of 95.8%, which significantly improved on the baseline models of logistic regression and decision trees. The results show the hybrid deep learning method can reduce false negatives and ultimately improve the identification of fraudulent transactions in real time.

Abstract: The rapid expansion of digital transactions has intensified the need for real-time fraud detection systems capable of identifying anomalous patterns with high accuracy. Traditional rule-based and classical machine learning approaches often fall short in handling the complex, high-dimensional, and sequential nature of transaction data. This study investigates the application of deep learning models—specifically Convolutional Neural Networks (CNNs) and Long Short-Term Memory Recurrent Neural Networks (LSTM-RNNs)—to detect fraudulent behavior in real-time transaction networks. Using the IEEE-CIS Fraud Detection dataset, comprising over 500,000 records, the models were evaluated based on accuracy, precision, recall, and F1-score. Data preprocessing included normalization, categorical encoding, and sequence transformation. The LSTM-RNN model demonstrated superior performance, achieving 96% accuracy and an F1-score of 0.945, significantly outperforming baseline models such as logistic regression and random forest. Regression analysis further validated the stability of predictions across various transaction features. The results highlight the effectiveness of sequence-aware deep learning models in capturing subtle fraud indicators and reducing false negatives. The study underscores the importance of combining high model accuracy with low latency and interpretability for deployment in financial security systems. Keywords: deep learning, fraud detection, real-time transactions, LSTM-RNN, CNN, financial security, anomaly detection, predictive modeling, sequential data, IEEE-CIS dataset

This paper tackles a complex logistics challenge of disaster management, encompassing warehouse location, pre-disaster inventory planning, routing, and post-disaster relief supply delivery. We establish an iterative process for optimizing relief distribution to shelters. Adaptable warehouse inventory reallocation responds to fluctuating demands, guided by a two-phase mathematical programming approach. In the first phase, a two-stage stochastic programming (TSSP) model determines optimal warehouse and shelter locations and inventory levels. In the subsequent phase, we introduce a mixed-integer programming (MIP) model to minimize the overall delivery time by making routing decisions. To streamline the process, we introduce a novel enumeration algorithm that trims down route options by considering unavailable links, effectively transforming the MIP model into an assignment-based model. This innovation results in a noticeable 74% reduction in solution time. Further efficiency is achieved by developing a branch-and-cut algorithm for swift MIP resolution. A real-world case study confirms the practicality of our approach.

Given an IP address, it is a challenging task to obtain its geographic location. Besides approaches which associate coordinates with IP addresses in a predominantly static way, there are also measurement based approaches that exploit the correlation between the propagation delay of signals and round trip times of probe packets. We analyze multiple approaches solely based on delay measurements, i. e. without the use of third-party knowledge, and obtain mean errors of just under 100 km. In this paper, we propose a new model for IP geolocation which combines the strengths of different previous techniques and reduces IP geolocation to the problem of finding equilibrium points in a spring system. Our approach, called Spring-Based Geolocation (SBG), is able to reduce the mean error to less than 75km in our experiments without adding significant complexity. In fact, our model allows for additional data sources in a natural way, which has the potential to further improve results.

Identification, Segmentation, and Image Property Study of Acute Infarcts in Diffusion-Weighted Images by Using a Probabilistic Neural Network and Adaptive Gaussian Mixture Model

Large-scale phosphoproteomics studies are of great interest due to their potential for the dissection of signaling pathways controlled by protein kinases. Recent advances in mass spectrometry (MS)-based phosphoproteomic techniques offer new opportunities to profile protein kinase activities in a comprehensive manner. However, this increasingly used approach still poses many analytical challenges. On one hand, high stringency criteria for phosphopeptide identification based on MS/MS data are needed in order to avoid false positives; however, on the other hand, these stringent criteria also result in the introduction of many false negatives. In the current report, we employ different mass spectrometric techniques for large-scale phosphoproteomics in order to reduce the presence of false negatives and enhance data confidence. A LTQ-Orbitrap LC-MS/MS platform identified approximately 3 times more phosphopeptides than Q-TOF LC-MS/MS instrumentation (4308 versus 1485 identifications, respectively). In both cases, collision induced dissociation (CID) was used to fragment peptides. Interestingly, the two platforms produced complementary data as many of the low scoring phosphopeptide ions identified by LTQ-Orbitrap MS/MS gave rise to high score identifications by Q-TOF MS/MS analysis, and vice versa. In fact, approximately 450 phosphopeptides identified by the Q-TOF instrument were not identified by the LTQ-Orbitrap. Further data comparison revealed the extent of the problem: in one experiment, the estimated number of false negatives (1066) was close to the number of identified phosphopeptides (1485). This work demonstrates that by using standard procedures for phosphopeptide identification the number of false negatives can be even greater than the number of false positives. We propose using historical phosphoproteomic data and spectral matching algorithms in order to efficiently minimize false negative rates.