Financial Malware Detect With Job Anomaly

It is well-known that financial frauds, such as money laundering, also facilitate terrorism or other illegal activity. A lot of this kind of this kind of illicit dealings entails a complicated trading and financial exchange, and that makes it impossible to uncover the frauds. Additionally, dynamic financial networks and features can be leveraged for trading. The trading network shows the relationship between organizations, thereby allowing investigators to identify fraudulent activity; while entity features filter out fraudulent behavior. Thus, the characteristics of the network and characteristics include knowledge that has the ability to enhance fraud identification. However, most of the current approaches operate on either networks or content. In this study, we propose a novel approach, dubbed CoDetect, that capitalizes on network and feature details. Another excellent aspect of the CoDetect is that it is able to simultaneously track both financial transactions and patterns of fraud. Extensive laboratory testing on both synthetic evidence and actual cases demonstrates the framework's capacity to tackle financial fraud

Intrusion detection through knowledge sharing

The financial losses caused by computer crimes have increased by more than $100 million every year since 1999. The combination of financial losses and high profile events such as the spread of the Code Red worm has sparked public interest in computer crime. With the increasing public awareness of the need for better computer security, companies are beginning to rely heavily on intrusion detection systems. Currently, security companies focus on the creation of complete, comprehensive intrusion detection products. So far no single product has been able to dominate the intrusion detection market. As a result, computer networks use multiple intrusion detection systems functioning independently of each other. There exists the possibility of better intrusion detection by linking the independent components into a knowledge-sharing system. With cooperative detection methods in mind, an outline for a knowledge-sharing protocol is developed. For this experiment the control is a hybrid intrusion detection system that is unable to share knowledge of previously detected attacks, and whose performance is effectively the sum of its components. The test IDS is the control system modified to take advantage of knowledge sharing. The experiment shows that better results can be achieved through the cooperation of the components of existing intrusion detection systems

Impact Of Artificial Intelligence And Big Data On The Oil And Gas Industry In Nigeria

This paper examines the concept of Artificial intelligence and Big Data as a field of study and its Impact on the oil and gas industry. Artificial Intelligence refers to the concept having of Computer systems that can perform tasks that would typically require human intelligence. Some such tasks are visual perception, speech recognition, decision-making and translation between languages, amongst others. “Big data” or Big Data analytics is a term often used to describe a huge or somewhat overwhelming data size that exceeds the capacity of both humans and the traditional software to process within an acceptable time and value. There is a big interface between the two concepts. AI does not stand alone; it requires big data for efficiency. AI and Big Data have brought about great impact across different industries and organizations. In the oil and gas industry, there have been an increasing installation of data recording sensors, hence data acquisition in exploration, drilling and production aspects of the industry. The industry is gradually making use of this huge data set by processing them using AI enabled tools and software to arrive at smart decisions that bring efficiency to operations in the industry. Some of such areas are analysis of seismic and micro-seismic data, improvement in reservoir characterization and simulation, reduction in drilling time and increasing drilling safety, optimization of pump performance, amongst others. Some of the solutions listed above have been successfully implemented in Nigeria, mostly by the international oil companies and some additional areas have also been impacted: managing asset integrity, tubular tally for drilling operations using RFID and the licensing and permit system by DPR. The industry has fully embraced the AI and Big Data concept, the future is very bright for more innovative solutions. However, there are still a few challenges especially in Nigeria. Some of these challenges include lack of local skilled manpower, poor data culture, security challenges in the industry’s operating areas, limited availability of good quality data, and understanding the complexity of the concept

Machine learning effects on the norwegian oil and gas industry

The downturn in the Norwegian oil industry in recent years has led to a revaluation of the sector. Out of this turmoil, a new surge of innovation appeared. This paper explores the innovation effects machine learning (ML) technology has brought to the Norwegian oil and gas industry (NOGI) using a qualitative approach through conducting semi-structured qualitative interviews. These interviews focus on five unique perspectives within the industry. These perspectives represent the unique interplay between private and public actors on the Norwegian continental shelf (NCS). The interviews discuss the value of big data, the use of ML in optimizing extraction processes and finding more sustainable approaches to detecting oil and gas. After presenting the five perspectives in the analysis, similarities and differences are discussed in light of the role the actors i.e. the companies play on the NCS. Interviewees expressed their enthusiasm and aversions about using new technologies to secure competitive advantages, despite most companies developing similar uses of ML. Throughout the analysis, background information from website searches and analyses are used to provide context for the interview data. The results show that the use of data, advanced analytics and various forms of ML create opportunities to fundamentally reimagine how and where work gets done and that there are possibilities of finding safer, more cost efficient and more sustainable approaches to the work currently being done through ML in the NOGI. The study shows that ML has brought disruptive innovation to the NOGI that enhances competitive advantages

External servers security

Romero Barrero, D. (2010). External servers security. http://hdl.handle.net/10251/9111.Archivo delegad

Anti-Money Laundering Alert Optimization Using Machine Learning with Graphs

Money laundering is a global problem that concerns legitimizing proceeds from serious felonies (1.7-4 trillion euros annually) such as drug dealing, human trafficking, or corruption. The anti-money laundering systems deployed by financial institutions typically comprise rules aligned with regulatory frameworks. Human investigators review the alerts and report suspicious cases. Such systems suffer from high false-positive rates, undermining their effectiveness and resulting in high operational costs. We propose a machine learning triage model, which complements the rule-based system and learns to predict the risk of an alert accurately. Our model uses both entity-centric engineered features and attributes characterizing inter-entity relations in the form of graph-based features. We leverage time windows to construct the dynamic graph, optimizing for time and space efficiency. We validate our model on a real-world banking dataset and show how the triage model can reduce the number of false positives by 80% while detecting over 90% of true positives. In this way, our model can significantly improve anti-money laundering operations.Comment: 8 pages, 5 figure