Leveraging the Windows Amcache.hve File in Forensic Investigations

The Amcache.hve is a registry hive file that is created by Microsoft® Windows® to store the information related to execution of programs. This paper highlights the evidential potential of Amcache.hve file and its application in the area of user activity analysis. The study uncovers numerous artifacts retained in Amcache.hve file when a user performs certain actions such as running host-based applications, installation of new applications, or running portable applications from external devices. The results of experiments demonstrate that Amcache.hve file stores intriguing artifacts related to applications such as timestamps of creation and last modification of any application; name, description, publisher name and version of applications; execution file path, SHA-1 hash of executable files etc. These artifacts are found to persist even after the applications have been deleted from the system. Further experiments were conducted to evaluate forensic usefulness of the information stored in Amcache.hve and it was found that Amcache.hve information is propitious to trace the deleted applications, malware programs and applications run from external devices. Finally, comparison of information in Amcache.hve file with information in other similar sources (IconCache.db, SRUDB.dat and Prefetch files) is shown, in order to provide more useful information to forensic investigators

C&R Tree based Air Target Classification Using Kinematics

Since the improvement in Anti Radar Material technology and stealth technology grows, there are immense counter measures that have opened to deny such technologies for classification to the adversary. At the same time it is observed that radar is continuously tracking the air target. This track data represents the kinematics which can be efficiently manipulated for effective classification without being deceived. The present study uses decision tree based classifier, specifically Classification and Regression Tree (CRT) algorithm over certain significant feature vectors. It classifies the data set of an air target into a target class where feature vectors are derived from the Radar Track Data using Matlab code. The work presented here aims to assess the performance of CRT. Although the methods and results presented here are for Air Target Classification, they may give insight for other applications

Distinct Multiple Learner-Based Ensemble SMOTEBagging (ML-ESB) Method for Classification of Binary Class Imbalance Problems

Traditional classification algorithms often fail in learning from highly imbalanced datasets because the training involves most of the samples from majority class compared to the other existing minority class. In this paper, a Multiple Learners-based Ensemble SMOTEBagging (ML-ESB) technique is proposed. The ML-ESB algorithm is a modified SMOTEBagging technique in which the ensemble of multiple instances of the single learner is replaced by multiple distinct classifiers. The proposed ML-ESB is designed for handling only the binary class imbalance problem. In ML-ESB the ensembles of multiple distinct classifiers include Naïve Bays, Support Vector Machine, Logistic Regression and Decision Tree (C4.5) is used. The performance of ML-ESB is evaluated based on six binary imbalanced benchmark datasets using evaluation measures such as specificity, sensitivity, and area under receiver operating curve. The obtained results are compared with those of SMOTEBagging, SMOTEBoost, and cost-sensitive MCS algorithms with different imbalance ratios (IR). The ML-ESB algorithm outperformed other existing methods on four datasets with high dimensions and class IR, whereas it showed moderate performance on the remaining two low dimensions and small IR value datasets

Cold atoms in space: community workshop summary and proposed road-map

We summarise the discussions at a virtual Community Workshop on Cold Atoms in Space concerning the status of cold atom technologies, the prospective scientific and societal opportunities offered by their deployment in space, and the developments needed before cold atoms could be operated in space. The cold atom technologies discussed include atomic clocks, quantum gravimeters and accelerometers, and atom interferometers. Prospective applications include metrology, geodesy and measurement of terrestrial mass change due to, e.g., climate change, and fundamental science experiments such as tests of the equivalence principle, searches for dark matter, measurements of gravitational waves and tests of quantum mechanics. We review the current status of cold atom technologies and outline the requirements for their space qualification, including the development paths and the corresponding technical milestones, and identifying possible pathfinder missions to pave the way for missions to exploit the full potential of cold atoms in space. Finally, we present a first draft of a possible road-map for achieving these goals, that we propose for discussion by the interested cold atom, Earth Observation, fundamental physics and other prospective scientific user communities, together with the European Space Agency (ESA) and national space and research funding agencies

Cold atoms in space: community workshop summary and proposed road-map

We summarise the discussions at a virtual Community Workshop on Cold Atoms in Space concerning the status of cold atom technologies, the prospective scientific and societal opportunities offered by their deployment in space, and the developments needed before cold atoms could be operated in space. The cold atom technologies discussed include atomic clocks, quantum gravimeters and accelerometers, and atom interferometers. Prospective applications include metrology, geodesy and measurement of terrestrial mass change due to, e.g., climate change, and fundamental science experiments such as tests of the equivalence principle, searches for dark matter, measurements of gravitational waves and tests of quantum mechanics. We review the current status of cold atom technologies and outline the requirements for their space qualification, including the development paths and the corresponding technical milestones, and identifying possible pathfinder missions to pave the way for missions to exploit the full potential of cold atoms in space. Finally, we present a first draft of a possible road-map for achieving these goals, that we propose for discussion by the interested cold atom, Earth Observation, fundamental physics and other prospective scientific user communities, together with the European Space Agency (ESA) and national space and research funding agencies.publishedVersio

Cold atoms in space: community workshop summary and proposed road-map

We summarise the discussions at a virtual Community Workshop on Cold Atoms in Space concerning the status of cold atom technologies, the prospective scientific and societal opportunities offered by their deployment in space, and the developments needed before cold atoms could be operated in space. The cold atom technologies discussed include atomic clocks, quantum gravimeters and accelerometers, and atom interferometers. Prospective applications include metrology, geodesy and measurement of terrestrial mass change due to, e.g., climate change, and fundamental science experiments such as tests of the equivalence principle, searches for dark matter, measurements of gravitational waves and tests of quantum mechanics. We review the current status of cold atom technologies and outline the requirements for their space qualification, including the development paths and the corresponding technical milestones, and identifying possible pathfinder missions to pave the way for missions to exploit the full potential of cold atoms in space. Finally, we present a first draft of a possible road-map for achieving these goals, that we propose for discussion by the interested cold atom, Earth Observation, fundamental physics and other prospective scientific user communities, together with the European Space Agency (ESA) and national space and research funding agencies

Development of New Structure for Frequent Pattern Mining

In this paper, we develop a new novel data structure called SH-Struct (Soft-Hyperlinked Structure) which mines the complete frequent itemset using SH-Mine algorithm. This algorithm enables frequent pattern mining with different supports. SH-Struct is based on creating SH-Tree which extends the idea of H-Struct to improvise storage compression and allow very fast frequent pattern mining. The algorithm has been tested extensively with various datasets and the experimental analysis shows that it outperforms FP-growth (Frequent Pattern growth) algorithm in terms of space and time payoffs.</p

Efficient Temporal Pattern Mining for Humanoid Robot

Pattern mining in temporal databases is one of the challenging platform which holds attention when some ordered sequences are frequently occurred at different time instances in the dataset. We have found temporal patterns in humanoid robot dataset of HOAP-2 (Humanoid Open-Architecture Platform) which generates different motions through recurring sequences of various joint associations. For mining temporal patterns in that dataset we have proposed a method. This method uses FP-Temporal and SH(Soft-Hyperlinked)-Temporal mining algorithm as pattern growth methods for generating temporal association rules for various motion patterns of HOAP-2. Brief performance analysis shows that SH-Temporal is much efficient than FP-Temporal for such datasets and works significantly for mining sequentially associative temporal patterns in terms of temporal association rules.</p

SH-Struct: An Affirmative Advanced Method for Mining Frequent Patterns

Data Mining requires versatile computational techniques for analyzing patterns among large and diversified databases. One of the most influential and typically emerging research area is to develop impinging structures for valid frequent patterns. In this paper, we have formulated a novel data structure known as SH-Struct (Soft-Hyperlinked Structure) which mines the complete frequent itemset using SH-Mine algorithm. This algorithm enables frequent pattern mining with different supports. Fundamentally, SH-Struct is a tree structure which maintains H-Struct (Hyperlinked Structure) at each level of the tree called SH-Tree to improvise storage compression and reserves frequent patterns very fast using SH-Mine algorithm. To validate the effectiveness of our structure here we present the performance study of SH-mine and FP (Frequent Pattern)- growth algorithm highlighting space and time payoffs for two categories of databases: sparse and dense. The experimental results show the prominent behavior of proposed method and incite us to further deploy it in more dense and dynamic databases such as temporal databases for generating more prognostic outcomes. </p