Neural Networks Detecting Intrusions And Attacks On Computer Networks
Intrusion Detection Systems (IDS) can be defined as software or hardware systems that automate the process of monitoring the events occurring in a computer system or network, analysing them for signs of security problems. Today, they are an especially important component of computer network security architecture, given the increased exposure of business critical process, product, financial, customer and employee internal systems and data to the internet including via Cloud deployment, IoT (Internet of Things), mobile self-service applications, B2B (Business to Business) and B2C (Business to Consumer) integration.
With network firewalls, inbound network traffic is filtered according to a set of predefined rules, meaning malicious traffic can reach systems if no rules exist that match and flag such traffic. Similarly, signature based detection systems rely on an inventory of signature definitions to enable flagging of activity as a threat. In both cases, the technique is reactive, with the firewall rules and signature inventory updated only after an attack type is known.
Conversely, IDS is both proactive and able to deal with unknown threats and abnormal activity, providing an additional security layer by actively classifying internal and external traffic in real time as good or bad according to characteristics of the network data packets, and can respond appropriately by closing ports, blacklisting I.P. addresses or sending TCP reset packets to break connections.
This study implements Neural Networks as the basis of an IDS that classifies computer network activity as bad, covering intrusions and attacks, and good for normal connections. A dataset originally prepared by MIT Lincoln labs for the 1998 Defence Advanced Research Projects Agency (DARPA) Intrusion Detection Evaluation Program, simulating air force base LAN activity, is used.
Master’s Degree Intelligent Systems paper grade 80% | Source code
Neural Networks For Crop Prediction And Monitoring Ecosystems
With the environment under increasing pressure from climate change and human development, there appears to be real opportunities to benefit from an increase in smarter farming ecosystem management given today’s perfect technology storm of available time-series satellite data, accessible machine learning software, elastic and affordable cloud computing, connected IoT and more capable UAV platforms.
Knowing exactly when to harvest for maximum crop yield and planning for it throughout the complete supply chain brings significant benefit, as does knowing when to selectively apply herbicides or take action on newly identified deforestation or areas at risk of landslide.
This study reviews recent application of artificial neural networks (ANNs) for crop prediction, classification and monitoring of ecosystems. It focuses on the use of NDVI (Normalized Difference Vegetation Index) data, which measures the difference between near-infrared (which vegetation strongly reflects) and red light (which vegetation absorbs), sourced from satellites and UAVs.