In this paper we examine the behavior of Ad Hoc networks through simulations, using different routing protocols and various topologies. We examine the difference in performance, using CBR application, with packets of different size through a variety of topologies, showing the impact node placement has on networks performance. We show that the choice of routing protocol plays an important role on network’s performance. We also quantify node mobility effects, by looking into both static and fully mobile configurations. Our paper presents a systematic analysis of a variety of different ad hoc network topologies in terms of node placement, node mobility and routing protocols through several simulated scenarios.

IJCSI Publicity Board 2010

This paper presents methods for analyzing the topology of a Bayesian belief network created to qualify and quantify the strengths of investigative hypotheses and their supporting digital evidence. The methods, which enable investigators to systematically establish, demonstrate and challenge a Bayesian belief network, help provide a powerful framework for reasoning about digital evidence. The methods are applied to review a Bayesian belief network constructed for a criminal case involving BitTorrent file sharing, and explain the causal effects underlying the legal arguments.

/pdf/reasoning-about-evidence-using-bayesian-networks-2pifdwkrj4.pdf

Reasoning About Evidence Using Bayesian Networks

On a graph's security number

Security number of grid-like graphs

http://univ-bejaia.dz/staff/photo/pubs/200-984-4_Alliances_in_graphs-a_survey_1-HAL.pdf

Alliances in graphs: Parameters, properties and applications—A survey

Bounds on a graph's security number

Computer (digital) forensic examiners typically write a report to document the examination process, including tools used, major processing steps, summary of the findings, and a detailed listing of relevant evidence (files, artifacts) exported to external media (CD, DVD, hard copy) for the case investigator or attorney. However, proper interpretation of the significance of extracted evidence often requires additional consultation with the examiner. This paper proposes a practical methodology for transforming the findings in typical forensic reports to a graphical representation using Bayesian networks (BNs). BNs offer the following advantages: (1) Delineate the cause-effect relationship among relevant pieces of evidence described in the report; and (2) Use probability and established Bayesian inference rules to deal with uncertainty of digital evidence. A realistic forensic report is used to demonstrate this methodology.

From digital forensic report to Bayesian network representation

Past attempts to mine transactional databases for strongly correlated item pairs have been beset by difficulties. In an attempt to be efficient, some algorithms produce false positive and false negative results. In an attempt to be accurate and comprehensive, other algorithms sacrifice efficiency. We propose an efficient new algorithm that uses Jaccard's correlation coefficient, which is simply the ratio between the sizes of the intersection and the union of two sets, to generate a set of strongly correlated item pairs that is both accurate and comprehensive. The pruning of candidate item pairs based on an upper bound facilitates efficiency. Furthermore, there is no possibility of false positives or false negatives. Testing of our algorithm on datasets of various sizes shows its effectiveness in real-world application.

Efficient mining of strongly correlated item pairs

Outlier detection has become an important data mining problem in many applications, including customer management and fraud detection. In recent years, many algorithms have been developed for discovering outliers in large databases. However, to our knowledge, no algorithm exists for discovering outliers in interval data. In this paper, we propose an efficient algorithm to detect distance-based outliers in interval data. We perform empirical studies on real and simulated interval datasets to evaluate the effectiveness of our proposed algorithm in identifying meaningful outliers.

Detecting outliers in interval data

Detection of intrusion on network servers plays an ever more important role in network security. This paper investigates whether analysis of incoming connection behavior for properties of locality can be used to create a normal profile for network servers. Intrusions can then be detected due to their abnormal behavior. Experiments show that connections to a typical network server do in fact exhibit locality, and attacks can be detected through their violation of locality.

Robert Lee

Papers

Bounds on a graph's security number

From digital forensic report to Bayesian network representation

Efficient mining of strongly correlated item pairs

Detecting outliers in interval data

Locality-Based Server Profiling for Intrusion Detection