CMP 450 Networking and Machine Learning Mid Term

AI and Machine Learning in Network Intrusion Detection

• Intelligence can be defined as the ability to make the correct decision from a list of potential decisions given a set of inputs.
• The Turing test was designed to determine if a computer could be considered intelligent by comparing its responses to a human's through a terminal.
• Games were used as early test-beds for AI methods because they provided bounded problems that could demonstrate the power of AI algorithms.
• Arthur Samuel set the bar for learning programs in the 1950s by having two copies of his program play against each other to learn from increasingly complex strategies.
• The LISP language was the first language developed specifically for AI, and Prolog was developed in the 1970s to support logic programming.
• Strong AI aims to match or exceed human general intelligence, while weak AI focuses on providing bounded intelligence in specific areas.
• The 1969 monograph "Perceptrons" by Marvin Minsky and Seymour Papert is often credited with leading to AI's Winter.
• Scruffy and Neat approaches refer to Weak AI and Strong AI, respectively.
• After AI's Winter, the re-emergence of AI focused on hidden applications rather than openly promoting AI solutions.
• The systems approach explores AI from the perspective of its application within an environment, considering inputs, outputs, and interactions with the environment.
• Intrusion Detection Systems (IDS) aim to protect networks from malware by monitoring traffic and network devices for anomalous activities.
• Host-based IDS (HIDS) and network-based IDS (NIDS) are two types of IDS that offer different advantages based on network size and configuration.