To search, Click below search items. |
| All Published Papers Search Service |
|
|
|
Title |
Performance Enhancement for Intrusion Detection Systems |
|
Author |
Abdullah Baz, Samah Abuayeid, Hosam Alhakami, Tahani Alsubait |
| Citation |
Vol. 20 No. 4 pp. 229-239 |
|
Abstract |
Due to the rapid improvements in the networking and communication area, the internet becomes the primary connection and influence in people’s life. Besides, many organizations store, manipulate, and transfer their secure data via the internet. However, this increases the system’s vulnerabilities making it prone to different kinds of security threats. An efficient information system must achieve the goal of a security triangle by protecting system confidentiality, integrity, and availability. A particular practice to meet the security requirements in the modern organization’s information systems is to establish an intrusion detection system (IDS). IDS is considered an effective network technology to monitor and detect security attacks. Recently, IDS has addressed many problems related to detection accuracy, such as false-positive and false-negative alarm. In this paper, we introduce the primary concerns and challenges encountered continuously by IDS with a review of the current studies and research in the IDS area that solve and enhance these issues. Moreover, we propose a unified framework that utilizes a combination of IDS and machine learning techniques to address any potential impact on IDS performance. |
|
Keywords |
Intrusion Detection System Information security Network Security attacks Malware. |
|
URL |
