The term intrusion refers to a series of behaviours that exposes computer networks and systems' security to compromises. Corrective action on the network cannot go on without intrusion detection. IDS and IDS is the framework used to detect network traffic intrusions, which is how the network control mechanism identifies potential intrusions. Security breaches are designed to undermine one or more of the network's three primary security goals: privacy, availability, and trust. To get access to a system, an attacker must follow a predetermined set of procedures. Once inside, they can begin gathering data such as the protocol being used and the network resources available. There are many ways for a hacker to find out what systems are available on the network and how vulnerable they are to attacks. The rapid advancement of network technology necessitated IDS to focus on the detection of assaults using contextual analysis from signature matching processes. Using machine learning to detect and prevent intrusions, the IDS is a critical part of protecting data systems. Network intrusion detection is the focus of this paper, which examines and shows various machine learning techniques.

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Patients included in the analysis all achieved CR1 or PR1 by CIBMTR definition prior to transplant. Patients who received tandem transplants, allogeneic transplants, or who were transplanted on clinical protocol were excluded. Disease status prior to transplant and disease status 100 days after transplant was recorded for both patients younger than 65 and 65 years of age and older. Data from transplants of 117 patients were analyzed. 32 patients (27%) were age 65 and older, and 85 patients (73%) were younger than age 65. Prior to transplant, 20/32 patients (63%) age 65 and above were in CR or VGPR compared to 23/85 (27%) of patients younger than age 65. At 100-day restaging after transplant, 25/32 patients (78%) age 65 and above achieved a CR or VGPR compared to 44/85 patients (52%) younger than 65. There was one transplant-related death in each age group corresponding to a transplant-related mortality of 3% and 1% in the older and younger age groups, respectively. Two patients who were both younger than age 65 had evidence of progressive disease at 100-day restaging. Based on our single-institution analysis, multiple myeloma patients age 65 and above have experienced similar outcomes compared to younger patients with respect to transplant-related mortality and disease status 100-days after transplant. Specifically both age groups experienced a consolidative benefit to high-dose therapy followed by autologous SCT. Prospective studies evaluating the impact of age on transplant outcome should be performed for further investigation.