Watchword-Oriented and Time-Stamped Algorithms for Tamper-Proof Cloud Provenance Cognition

Provenance is derivative journal information about the origin and activities of system data and processes. For a highly dynamic system like the cloud, provenance can be accurately detected and securely used in cloud digital forensic investigation activities. This paper proposes watchword oriented provenance cognition algorithm for the cloud environment. Additionally time-stamp based buffer verifying algorithm is proposed for securing the access to the detected cloud provenance. Performance analysis of the novel algorithms proposed here yields a desirable detection rate of 89.33% and miss rate of 8.66%. The securing algorithm successfully rejects 64% of malicious requests, yielding a cumulative frequency of 21.43 for MR

Investigate how developers and managers view security design in software

Software security requirements have been traditionally considered as a non-functional attribute of the software. However, as more software started to provide services online, existing mechanisms of using firewalls and other hardware to secure software have lost their applicability. At the same time, under the current world circumstances, the increase of cyber-attacks on software is ever increasing. As a result, it is important to consider the security requirements of software during its design. To design security in the software, it is important to obtain the views of the developers and managers of the software. Also, it is important to evaluate if their viewpoints match or differ regarding the security. Conducting this communication through a specific model will enable the developers and managers to eliminate any doubts on security design and adopt an effective strategy to build security into the software. In this paper, we analyzed the viewpoints of developers and managers regarding their views on security design. We interviewed a team of 7 developers and 2 managers, who worked in two teams to build a real-life software product that was recently compromised by a cyber-attack. We obtained their views on the reasons for the successful attack by the malware and took their recommendations on the important aspects to consider regarding security. Based on their feedback, we coded their open-ended responses into 4 codes, which we recommended using for other real-life software as well

Analysis of variable VHE gamma-ray emission from the hard spectrum blazar 1ES 1218+304

This thesis is a study of the very high energy gamma-ray emission from the hard spectrum blazar 1ES 1218+304. The data were collected during the 2008/09 observing season by the VERITAS observatory, an array of four atmospheric Cherenkov telescopes in Southern Arizona. This work describes the development of a set of analysis tools suitable for the extraction of the energy spectra of astrophysical objects. Initially, the tools are applied to the Crab nebula data to optimize and calibrate the analysis. Afterwards, the analysis is applied to the high energy observations of the blazar 1ES 1218+304. We report an intense, day-scale flare observed on January 30, 2009. This marks the first detection of variability in gamma-ray emission from 1ES 1218+304. I also investigate the possibility of detecting a spectral feature in the observed energy spectra of blazar due to extragalactic background light. I demonstrate the presence of a spectral cut-off in the simulated multi-TeV energy spectra of blazars at around 1 TeV. This novel technique has a strong potential to discover the first observable signature of absorption of very high-energy photons due to the extragalactic background light

Qualitative analysis of the relationship between design smells and software engineering challenges

Software design debt aims to elucidate the rectification attempts of the present design flaws and studies the influence of those to the cost and time of the software. Design smells are a key cause of incurring design debt. Although the impact of design smells on design debt have been predominantly considered in current literature, how design smells are caused due to not following software engineering best practices require more exploration. This research provides a tool which is used for design smell detection in Java software by analyzing large volume of source codes. More specifically, 409,539 Lines of Code (LoC) and 17,760 class files of open source Java software are analyzed here. Obtained results show desirable precision values ranging from 81.01\% to 93.43\%. Based on the output of the tool, a study is conducted to relate the cause of the detected design smells to two software engineering challenges namely "irregular team meetings" and "scope creep". As a result, the gained information will provide insight to the software engineers to take necessary steps of design remediation actions.Comment: arXiv admin note: substantial text overlap with arXiv:1910.0542

URegM: a unified prediction model of resource consumption for refactoring software smells in open source cloud

The low cost and rapid provisioning capabilities have made the cloud a desirable platform to launch complex scientific applications. However, resource utilization optimization is a significant challenge for cloud service providers, since the earlier focus is provided on optimizing resources for the applications that run on the cloud, with a low emphasis being provided on optimizing resource utilization of the cloud computing internal processes. Code refactoring has been associated with improving the maintenance and understanding of software code. However, analyzing the impact of the refactoring source code of the cloud and studying its impact on cloud resource usage require further analysis. In this paper, we propose a framework called Unified Regression Modelling (URegM) which predicts the impact of code smell refactoring on cloud resource usage. We test our experiments in a real-life cloud environment using a complex scientific application as a workload. Results show that URegM is capable of accurately predicting resource consumption due to code smell refactoring. This will permit cloud service providers with advanced knowledge about the impact of refactoring code smells on resource consumption, thus allowing them to plan their resource provisioning and code refactoring more effectively

DDoS Attack Detection with Deep Learning Algorithm for SNMP, NetBISO, and DNS

Abstract: In this day and age of advanced technology, devices that are connected to the Internet and can think are a big part of both our everyday lives and the work we do in factories. The number of Internet of Things devices has been steadily increasing from one year to the next, and it is expected that by 2030, there will be 126 billion of them. On the other hand, the number of distributed denial of service, or DDoS, attacks on the internet's surface has gone up as the number of Internet of Things devices has grown. Because IoT devices are limited in what they can do, it's important to come up with some advanced security techniques to protect the DDoS surface. Because of this, people who want to take control of an Internet of Things device can attack it. This thesis uses the CICDoS2019 dataset to improve how bugs are handled and build a new taxonomy that can handle DDoS attacks better. In the end, this will make the defense against these kinds of attacks stronger. In this paper, the DNN and the LSTMs methods to find distributed denial of service threats (SNMP, NetBIOS, DNS). With our suggested method, accuracy rates of 99.99% have been reached. . Keywords SNMP, NetBIOS, DNS, LSTM, DDN, Deep Learnin