The effect of probe interval estimation on attack detection performance of a WLAN independent intrusion detection system

A new niche of densely populated, unprotected networks is becoming more prevalent in public areas such as Shopping Malls, defined here as independent open-access networks, which have attributes that make attack detection more challenging than in typical enterprise networks. To address these challenges, new detection systems which do not rely on knowledge of internal device state are investigated here. This paper shows that this lack of state information requires an additional metric (The exchange timeout window) for detection of WLAN Denial of Service Probe Flood attacks. Variability in this metric has a significant influence on the ability of a detection system to reliably detect the presence of attacks. A parameter selection method is proposed which is shown to provide reliability and repeatability in attack detection in WLANs. Results obtained from ongoing live trials are presented that demonstrate the importance of accurately estimating probe request and probe response timeouts in future Independent Intrusion Detection Systems.

INFO2009 Group 21 - A Poster and set of Resources on Computer Security

This contains a poster advertising the resources. The resource is a profile folder on five topics, as well as a website, a quiz, and an interactive game.

Design and evaluation of intelligent classifier based intrusion detection system

We have discovered a novel approach of intrusion detection system using an intelligent data classifier based on a self organizing map (SOM). We have surveyed all other unsupervised intrusion detection methods, different alternative SOM based techniques and KDD winner IDS methods. This paper provides a robust designed and implemented intelligent data classifier technique based on a single large size (30x30) self organizing map (SOM) having the capability to detect all types of attacks given in the DARPA Archive 1999 the lowest false positive rate being 0.04 % and higher detection rate being 99.73% tested using full KDD data sets and 89.54% comparable detection rate and 0.18% lowest false positive rate tested using corrected data sets.

A risk analysis model to reduce computer security risks among healthcare organizations

The paper describes the on-going development of a new computer-based security risk analysis methodology that may be used to determine the computer security requirements of medical computer systems. The methodology has been developed for use within healthcare, with particular emphasis placed upon protecting medical information systems. The paper goes on to describe some of the problems with existing automated risk analysis systems, and how the ODESSA system may overcome the majority of these problems. Examples of security scenarios are also presented.

Organisational security requirements : an agile approach to ubiquitous information security

This paper proposes to address the need for more innovation in organisational information security by adding a security requirement engineering focus. Based on the belief that any heavyweight security requirements process in organisational security will be doomed to fail, we developed a security requirement approach with three dimensions. The use of a simple security requirements process in the first dimension has been augmented by an agile security approach. However, introducing this second dimension of agile security does provide support for, but does not necessarily stimulate, innovation. A third dimension is, therefore, needed to ensure there is a proper focus in the organisation's efforts to identify potential new innovations in their security. To create this focus three common shortcomings in organisational information security have been identified. The resulting security approach that addresses these shortcomings is called Ubiquitous Information Security. This paper will demonstrate the potential of this new approach by briefly discussing its possible application in two areas: Ubiquitous Identity Management and Ubiquitous Wireless Security.

Computational immunology for anomaly detection

The thesis makes a significant contribution to the issue of anomaly detection by introducing a computational immunology approach. Immunity-based anomaly detection in high dimensional space is systematically investigated and the proposed hybrid method (combining data mining techniques and computational immunology) improves both accuracy and efficiency.

Towards designing privacy-preserving signature-based IDS as a service : a study and practice

With the advent of Cloud Computing, IDS as a service (IDSaaS) has been proposed as an alternative to protect a network (e.g., financial organization) from a wide range of network attacks by offloading the expensive operations such as the process of signature matching to the cloud. The IDSaaS can be roughly classified into two types: signature-based detection and anomaly-based detection. During the packet inspection, no party wants to disclose their own data especially sensitive information to others, even to the cloud provider, for privacy concerns. However, current solutions of IDSaaS have not much discussed this issue. In this work, focus on the signature-based IDSaaS, we begin by designing a promising privacy-preserving intrusion detection mechanism, the main feature of which is that the process of signature matching does not reveal any specific content of network packets by means of a fingerprint-based comparison. We further conduct a study to evaluate this mechanism under a cloud scenario and identify several open problems and issues for designing such a privacy-preserving mechanism for IDSaaS in a practical environment.

Semi-supervised learning with concept drift using particle dynamics applied to network intrusion detection data

Concept drift, which refers to non stationary learning problems over time, has increasing importance in machine learning and data mining. Many concept drift applications require fast response, which means an algorithm must always be (re)trained with the latest available data. But the process of data labeling is usually expensive and/or time consuming when compared to acquisition of unlabeled data, thus usually only a small fraction of the incoming data may be effectively labeled. Semi-supervised learning methods may help in this scenario, as they use both labeled and unlabeled data in the training process. However, most of them are based on assumptions that the data is static. Therefore, semi-supervised learning with concept drifts is still an open challenging task in machine learning. Recently, a particle competition and cooperation approach has been developed to realize graph-based semi-supervised learning from static data. We have extend that approach to handle data streams and concept drift. The result is a passive algorithm which uses a single classifier approach, naturally adapted to concept changes without any explicit drift detection mechanism. It has built-in mechanisms that provide a natural way of learning from new data, gradually "forgetting" older knowledge as older data items are no longer useful for the classification of newer data items. The proposed algorithm is applied to the KDD Cup 1999 Data of network intrusion, showing its effectiveness.

A Design Methodology for Computer Security Testing

The field of "computer security" is often considered something in between Art and Science. This is partly due to the lack of widely agreed and standardized methodologies to evaluate the degree of the security of a system. This dissertation intends to contribute to this area by investigating the most common security testing strategies applied nowadays and by proposing an enhanced methodology that may be effectively applied to different threat scenarios with the same degree of effectiveness. Security testing methodologies are the first step towards standardized security evaluation processes and understanding of how the security threats evolve over time. This dissertation analyzes some of the most used identifying differences and commonalities, useful to compare them and assess their quality. The dissertation then proposes a new enhanced methodology built by keeping the best of every analyzed methodology. The designed methodology is tested over different systems with very effective results, which is the main evidence that it could really be applied in practical cases. Most of the dissertation discusses and proves how the presented testing methodology could be applied to such different systems and even to evade security measures by inverting goals and scopes. Real cases are often hard to find in methodology' documents, in contrary this dissertation wants to show real and practical cases offering technical details about how to apply it. Electronic voting systems are the first field test considered, and Pvote and Scantegrity are the two tested electronic voting systems. The usability and effectiveness of the designed methodology for electronic voting systems is proved thanks to this field cases analysis. Furthermore reputation and anti virus engines have also be analyzed with similar results. The dissertation concludes by presenting some general guidelines to build a coordination-based approach of electronic voting systems to improve the security without decreasing the system modularity.

10th National Computer Security Conference : proceedings : 21-24 September, 1987.

AD-A219 100.

11th National Computer Security Conference : proceedings, 17-20 October, 1988.

AD-A219 099.

Computer security and the data encryption standard : proceedings of the Conference on Computer Security and the Data Encryption Standard held at the National Bureau of Standards in Gaithersburg, Maryland, on February 15, 1977 /

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