Error propagation metrics from XMI

This work describes the production of an application Error Propagation Metrics from XMI which can extract process and display software design metrics from XMI files. The tool archives these design metrics in a standard XML format defined by a metric document type definition.;XMI is a flavour of XML allowing the description of UML models. As such, the XMI representation of a software design will include information from which a variety of software design metrics can be extracted. These metrics are potentially useful in improving the software design process, either throughout the early stages of design if a suitable XMI-enabled modelling tool is deployed, or to enable the comparison of completed software projects, by extracting design metrics from UML models reverse engineered from the implemented source code.;The tool is able to derive the error propagation of metrics from test XMI files created from UML sequence and state diagrams and from reverse engineered Java source code. However, variation was observed between the XMI representations generated by different software design tools, limiting the ability of the tool to process XMI from all sources. Furthermore, it was noted that subtle differences between UML design representations might have a marked effect on the quality of metrics derived.;In conclusion in order to validate the usefulness of these metrics that can be extracted from XMI files it would be useful to follow well-documented design projects throughout the total design and implementation process. Alternatively, the tool might be used to compare metrics from well-matched design implementations. In either case design metrics will only be of true value to software engineers if they can be associated empirically with a validated measure of system quality

Disaster Recovery Management with PowerShell PSDRM

Securing information and infrastructure is at the top of every organization’s priority. Security solutions are necessary and when properly implemented can minimize the exposure of an organization’s risk to compromise. Implementation can be costly and standardization is challenging. There are many cybersecurity solutions available ranging from open source to premium level coverages that can include deployment, monitoring, detection, and response. As threats evolve, the impacts of exploits become more difficult to thwart and in cases of ransomware the affects can immobilize a company and lead to lasting economic reprisal. Disaster Recovery provides an aspect of Cybersecurity and the most fundamental requirement for an organization to maintain continuity. When an organization publicly acknowledges compromise of their infrastructure regardless of the nature of the attack, the outfall is loss of confidence which inevitably impacts both internal and external stakeholders. This in turn leads to further efficiency loss to the businesses profitability as the primary resources are allocated to investigative and resolution matters. What this research\u27s primary goal is to focus on Disaster Recovery and provide an executable with PowerShell at the backend to perform a selective approach to automating Disaster Recovery within Virtualization infrastructures. This research shows methods on which an administrator could build their project using native tools such as PowerShell, to provide their own customized automated Disaster Recovery solutions designed for Virtualized environments by initiating a backup, test, restore and conserve volatile state. Too often does an organization lack the necessary skillsets needed to bring an organization back to service after an attack as much has seen in the effect of Ransomware attacks. Providing these means for organizations gives those with less than a financial advantage a fighting chance against unanticipated attacks. We accomplish this by standardizing a method for the roles responsible in the organization for ensuring security measures are maintained using PowerShell

Walk This Way: Footwear Recognition Using Images & Neural Networks

Footwear prints are one of the most commonly recovered in criminal investigations. They can be used to discover a criminal's identity and to connect various crimes. Nowadays, footwear recognition techniques take time to be processed due to the use of current methods to extract the shoe print layout such as platter castings, gel lifting, and 3D-imaging techniques. Traditional techniques are prone to human error and waste valuable investigative time, which can be a problem for timely investigations. In terms of 3D-imaging techniques, one of the issues is that footwear prints can be blurred or missing, which renders their recognition and comparison inaccurate by completely automated approaches. Hence, this research investigates a footwear recognition model based on camera RGB images of the shoe print taken directly from the investigation site to reduce the time and cost required for the investigative process. First, the model extracts the layout information of the evidence shoe print using known image processing techniques. The layout information is then sent to a hierarchical network of neural networks. Each layer of this network is examined in an attempt to process and recognize footwear features to eliminate and narrow down the possible matches until returning the final result to the investigator

Browser Forensic Investigations of Instagram Utilizing IndexedDB Persistent Storage

Social media usage is increasing at a rapid rate. Everyday users are leaving a substantial amount of data as artifacts in these applications. As the size and velocity of data increase, innovative technologies such as Web Storage and IndexedDB are emerging. Consequently, forensic investigators are facing challenges to adapt to the emerging technologies to establish reliable techniques for extracting and analyzing suspect information. This paper investigates the convenience and efficacy of performing forensic investigations with a time frame and social network connection analysis on IndexedDB technology. It focuses on artifacts from prevalently used social networking site Instagram on the Mozilla Firefox browser. A single case pretest–posttest quasi-experiment is designed and executed over Instagram web application to produce artifacts that are later extracted, processed, characterized, and presented in forms of information suited to forensic investigation. The artifacts obtained from Mozilla Firefox are crossed-checked with artifacts of Google Chrome for verification. In the end, the efficacy of using these artifacts in forensic investigations is shown with a demonstration through a proof-of-concept tool. The results indicate that Instagram artifacts stored in IndexedDB technology can be utilized efficiently for forensic investigations, with a large variety of information ranging from fully constructed user data to time and location indicators

Herpetofauna of the vicinity of Aksehir and Eber (Konya, Afyon), Turkey

WOS: 000329919500014In this research, 29 species of 11 amphibian and reptile families were detected in the endorheic basin of Aksehir and Eber. Of these species, 5 are anurans, 1 is a tortoise, 1 is a turtle, 9 are lizards, and 13 are snakes. In addition, the chorotype classification of the species recorded in the study area and their distributions depending on plants are also provided. It was established that specimens of subspecies Ophisops elegans macrodactylus and O.e. centralanatolia were sympatrically found in the vicinity of Ortakoy and Tuzlukcu. Moreover, in addition to the species determined in previous studies, Platyceps najadum and Hemorrhois nummifer were first detected in this region

A new record of macrovipera lebetina obtusa (viperidae) from south-eastern anatolia

The viper Macrovipera lebetina obtusa is recorded from Nusaybin, Mardin province of Turkey. Information on morphological features and the biology of this subspecies is given. © 2002 Taylor & Francis Group, LLC

Detecting Unprotected SIP-Based Voice over IP Traffic

The use of Voice over IP (VoIP) applications has dramatically increased in recent years. Large, medium, and small organizations, as well as individuals, are reducing the cost of their phone calls using their data infrastructure or a broadband Internet service to transmit phone calls over IP networks. Like data networks, VoIP networks are also vulnerable to security threats such as Denial-of-Service (DoS) attacks, interception of private communications, registration hijacking, spam, and message tampering. Security mechanisms, such as encryption and authentication, may be used to reduce the potential impact of some of these security threats. However, in reality, VoIP providers may not supply adequate security, or otherwise they are adopting and implementing these countermeasures at very slow rates without informing users whether their phone calls are protected. Given the fact that the interception of private communications is one of the most commonly seen attacks in VoIP, we present a solution to detect unprotected SIP-based VoIP packets. Upon positive detection, alerts may be sent to users informing them about the unprotected VoIP calls, thus potentially preventing identity theft and improving security awareness. Our testing results show that our solution provides accurate detection with zero false detection rate of unprotected SIP-based VoIP traffic