Network Intrusion Detection Systems (NIDS) are computer systems which monitor a network with the aim of discerning malicious from benign activity on that network. While a wide range of approaches have met varying levels of success, most IDSs rely on having access to a database of known attack signatures which are written by security experts. Nowadays, in order to solve problems with false positive alerts, correlation algorithms are used to add additional structure to sequences of IDS alerts. However, such techniques are of no help in discovering novel attacks or variations of known attacks, something the human immune system (HIS) is capable of doing in its own specialised domain. This paper presents a novel immune algorithm for application to the IDS problem. The goal is to discover packets containing novel variations of attacks covered by an existing signature base.
University of Nottingham（诺丁汉大学）；University College London（伦敦大学学院）；HP Labs（惠普实验室）；King’s College London（伦敦国王学院）；University of the West of England（西英格兰大学）
University of New Mexico（新墨西哥大学）；Amirkabir University of Technology（阿米尔卡比尔理工大学）
We propose an artificial immune model for intrusion detection in distributed systems based on a relatively recent theory in immunology called Danger theory. Based on Danger theory, immune response in natural systems is a result of sensing corruption as well as sensing unknown substances. In contrast, traditional self-nonself discrimination theory states that immune response is only initiated by sensing nonself (unknown) patterns. Danger theory solves many problems that could only be partially explained by the traditional model. Although the traditional model is simpler, such problems result in…
Faculty of Computer Science and Engineering, Shahid Beheshti University（沙希德·贝赫什提大学计算机科学与工程学院）
UASs form a large part of the fighting ability of the advanced military forces. In particular, these systems that carry confidential information are subject to security attacks. Accordingly, an Intrusion Detection System (IDS) has been proposed in the proposed design to protect against the security problems using the human immune system (HIS). The IDSs are used to detect and respond to attempts to compromise the target system. Since the UASs operate in the real world, the testing and validation of these systems with a variety of sensors is confronted with problems. This design is inspired by H…
该模型的主要功能是访问UCI Wisconsin Breast Cancer数据集[1]，并将数据项分为正常和异常两类。这种分类可以被称为异常检测，其区分计算机系统中的异常行为与正常行为。异常检测的一种流行解决方案是免疫系统（AIS）。人工免疫系统是受理论免疫学和观察到的免疫功能、原理和模型的启发，应用于问题解决的自适应系统。树突细胞算法（DCA）[2]是一种AIS算法，专门为异常检测而开发。它已成功地应用于计算机安全领域的入侵检测。基于智能体的模型被认为是实现AIS的理想方法，因为智能体可以完美地代表AIS中的免疫实体。该模型评估了在基于代理的仿真环境中重新实现DCA的可行性，该环境称为AnyLogic，其中DCA中的免疫实体由智能代理表示。如果该模型能够成功实现，将为在基于Agent的仿真环境中实现更复杂、更适应性更强的AIS模型提供可能。
Few-Shot Network Intrusion Detection Using Online Triplet Mining
Department of Electronics and Electrical Engineering, University of Strathclyde（斯特拉斯克莱德大学电子与电气工程系）；Faculty of Computer and Information Sciences, Ain Shams University（爱思曼大学计算机与信息科学学院）；Faculty of Electrical Engineering, Czech Technical University（捷克技术大学电气工程学院）
