A framework for implementing a Distributed Intrusion Detection System (DIDS) with interoperabilty and information analysis

Computer Intrusion Detection Systems (IDS) are primarily designed to protect availability, condentiality and integrity of critical information infrastructures. A Distributed IDS (DIDS) consists of several IDS over a large network(s), all of which communicate with each other, with a central server or with a cluster of servers that facilitates advanced network monitoring. In a distributed environment, DIDS are implemented using cooperative intelligent sensors distributed across the network(s). A significant challenge remains for IDS designers to combine data and information from numerous heterogeneous distributed agents into a coherent process which can be used to evaluate the security of the system. Multisensor data sensing, or distributed sensing, is a discipline used to combine data from multiple and diverse sensors and sources in order to make inferences about events, activities and situations. Today, common environments consists in large networks of high bandwidth. In these scenarios the amount of data produced by the sensors is extremely large so the efficient processing becomes a critical factor. In this article we propose a framework that aims to achieve the interoperability of the diverse heterogeneous agents that compose the typical infrastructure of a DIDS. Also, we address the alert aggregation and correlation problem proposing an alert processing software pipeline.Presentado en el XI Workshop Procesamiento Distribuido y Paralelo (WPDP)Red de Universidades con Carreras en Informática (RedUNCI

A framework for implementing a Distributed Intrusion Detection System (DIDS) with interoperabilty and information analysis

Computer Intrusion Detection Systems (IDS) are primarily designed to protect availability, condentiality and integrity of critical information infrastructures. A Distributed IDS (DIDS) consists of several IDS over a large network(s), all of which communicate with each other, with a central server or with a cluster of servers that facilitates advanced network monitoring. In a distributed environment, DIDS are implemented using cooperative intelligent sensors distributed across the network(s). A significant challenge remains for IDS designers to combine data and information from numerous heterogeneous distributed agents into a coherent process which can be used to evaluate the security of the system. Multisensor data sensing, or distributed sensing, is a discipline used to combine data from multiple and diverse sensors and sources in order to make inferences about events, activities and situations. Today, common environments consists in large networks of high bandwidth. In these scenarios the amount of data produced by the sensors is extremely large so the efficient processing becomes a critical factor. In this article we propose a framework that aims to achieve the interoperability of the diverse heterogeneous agents that compose the typical infrastructure of a DIDS. Also, we address the alert aggregation and correlation problem proposing an alert processing software pipeline.Presentado en el XI Workshop Procesamiento Distribuido y Paralelo (WPDP)Red de Universidades con Carreras en Informática (RedUNCI

A Survey of Distributed Intrusion Detection Approaches

Distributed intrustion detection systems detect attacks on computer systems by analyzing data aggregated from distributed sources. The distributed nature of the data sources allows patterns in the data to be seen that might not be detectable if each of the sources were examined individually. This paper describes the various approaches that have been developed to share and analyze data in such systems, and discusses some issues that must be addressed before fully decentralized distributed intrusion detection systems can be made viable

Analyzing audit trails in a distributed and hybrid intrusion detection platform

Efforts have been made over the last decades in order to design and perfect Intrusion Detection Systems (IDS). In addition to the widespread use of Intrusion Prevention Systems (IPS) as perimeter defense devices in systems and networks, various IDS solutions are used together as elements of holistic approaches to cyber security incident detection and prevention, including Network-Intrusion Detection Systems (NIDS) and Host-Intrusion Detection Systems (HIDS). Nevertheless, specific IDS and IPS technology face several effectiveness challenges to respond to the increasing scale and complexity of information systems and sophistication of attacks. The use of isolated IDS components, focused on one-dimensional approaches, strongly limits a common analysis based on evidence correlation. Today, most organizations’ cyber-security operations centers still rely on conventional SIEM (Security Information and Event Management) technology. However, SIEM platforms also have significant drawbacks in dealing with heterogeneous and specialized security event-sources, lacking the support for flexible and uniform multi-level analysis of security audit-trails involving distributed and heterogeneous systems. In this thesis, we propose an auditing solution that leverages on different intrusion detection components and synergistically combines them in a Distributed and Hybrid IDS (DHIDS) platform, taking advantage of their benefits while overcoming the effectiveness drawbacks of each one. In this approach, security events are detected by multiple probes forming a pervasive, heterogeneous and distributed monitoring environment spread over the network, integrating NIDS, HIDS and specialized Honeypot probing systems. Events from those heterogeneous sources are converted to a canonical representation format, and then conveyed through a Publish-Subscribe middleware to a dedicated logging and auditing system, built on top of an elastic and scalable document-oriented storage system. The aggregated events can then be queried and matched against suspicious attack signature patterns, by means of a proposed declarative query-language that provides event-correlation semantics

Towards Configured Intrusion Detection Systems

This paper studies the challenges in the current intrusion detection system and comparatively analyzes the active and passive response systems. The paper studies the existing IDS and their usefulness in detecting and preventing attacks in any type of network and control traffic with the performance of the system to be improved. The study also evaluates the emerging avenues in Intrusion Detection System and explores the possible future avenues in intrusion detection scheme. It is observed that the detection-based systems have started to gain popularity in the IT security domain. The paper highlights the need to implement an appropriately configured IDS since an optimally configured IDS deters hackers, thus, reducing the need for investigation by security experts for security violations

An Agent-Based Intrusion Detection System for Local Area Networks

Since it is impossible to predict and identify all the vulnerabilities of a network beforehand, and penetration into a system by malicious intruders cannot always be prevented, intrusion detection systems (IDSs) are essential entities to ensure the security of a networked system. To be effective in carrying out their functions, the IDSs need to be accurate, adaptive, and extensible. Given these stringent requirements and the high level of vulnerabilities of the current days' networks, the design of an IDS has become a very challenging task. Although, an extensive research has been done on intrusion detection in a distributed environment, distributed IDSs suffer from a number of drawbacks e.g., high rates of false positives, low detection efficiency etc. In this paper, the design of a distributed IDS is proposed that consists of a group of autonomous and cooperating agents. In addition to its ability to detect attacks, the system is capable of identifying and isolating compromised nodes in the network thereby introducing fault-tolerance in its operations. The experiments conducted on the system have shown that it has a high detection efficiency and low false positives compared to some of the currently existing systems.Comment: 13 pages, 5 figures, 2 table

An Historical Analysis of Factors Contributing to the Emergence of the Intrusion Detection Discipline and its Role in Information Assurance

In 2003, Gartner, Inc., predicted the inevitable demise of the intrusion detection (ID) market, a major player in the computer security technology industry. In light of this prediction, IT executives need to know if intrusion detection technologies serve a strategic purpose within the framework of information assurance (IA). This research investigated the historical background and circumstances that led to the birth of the intrusion detection field and explored the evolution of the discipline through current research in order to identify appropriate roles for IDS technology within an information assurance framework. The research identified factors contributing to the birth of ID including increased procurement and employment of resource-sharing computer systems in the DoD, a growing need to operate in an open computing environment while maintaining security and the unmanageable volume of audit data produced as a result of security requirements. The research also uncovered six trends that could be used to describe the evolution of the ID discipline encompassing passive to active response mechanisms, centralized to distributed management platforms, centralized to distributed/agent-based detection, single to multiple detection approaches within a system, host-based to network to hybrid analysis and software-based to hardware-based/in-line devices. Finally, the research outlined three roles suitable for IDS to fulfill within the IA framework including employing IDS as a stimulus to incident response mechanisms, as a forensic tool for gathering evidence of computer misuse and as a vulnerability assessment or policy enforcement facility

Distributed Network Anomaly Detection on an Event Processing Framework

Network Intrusion Detection Systems (NIDS) are an integral part of modern data centres to ensure high availability and compliance with Service Level Agreements (SLAs). Currently, NIDS are deployed on high-performance, high-cost middleboxes that are responsible for monitoring a limited section of the network. The fast increasing size and aggregate throughput of modern data centre networks have come to challenge the current approach to anomaly detection to satisfy the fast growing compute demand. In this paper, we propose a novel approach to distributed intrusion detection systems based on the architecture of recently proposed event processing frameworks. We have designed and implemented a prototype system using Apache Storm to show the benefits of the proposed approach as well as the architectural differences with traditional systems. Our system distributes modules across the available devices within the network fabric and uses a centralised controller for orchestration, management and correlation. Following the Software Defined Networking (SDN) paradigm, the controller maintains a complete view of the network but distributes the processing logic for quick event processing while performing complex event correlation centrally. We have evaluated the proposed system using publicly available data centre traces and demonstrated that the system can scale with the network topology while providing high performance and minimal impact on packet latency