Leveraging the Cloud for Software Security Services.

This thesis seeks to leverage the advances in cloud computing in order to address modern security threats, allowing for completely novel architectures that provide dramatic improvements and asymmetric gains beyond what is possible using current approaches. Indeed, many of the critical security problems facing the Internet and its users are inadequately addressed by current security technologies. Current security measures often are deployed in an exclusively network-based or host-based model, limiting their efficacy against modern threats. However, recent advancements in the past decade in cloud computing and high-speed networking have ushered in a new era of software services. Software services that were previously deployed on-premise in organizations and enterprises are now being outsourced to the cloud, leading to fundamentally new models in how software services are sold, consumed, and managed. This thesis focuses on how novel software security services can be deployed that leverage the cloud to scale elegantly in their capabilities, performance, and management. First, we introduce a novel architecture for malware detection in the cloud. Next, we propose a cloud service to protect modern mobile devices, an ever-increasing target for malicious attackers. Then, we discuss and demonstrate the ability for attackers to leverage the same benefits of cloud-centric services for malicious purposes. Next, we present new techniques for the large-scale analysis and classification of malicious software. Lastly, to demonstrate the benefits of cloud-centric architectures outside the realm of malicious software, we present a threshold signature scheme that leverages the cloud for robustness and resiliency.Ph.D.Computer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/91385/1/jonojono_1.pd

IoT-based Secure Data Transmission Prediction using Deep Learning Model in Cloud Computing

The security of Internet of Things (IoT) networks has become highly significant due to the growing number of IoT devices and the rise in data transfer across cloud networks. Here, we propose Generative Adversarial Networks (GANs) method for predicting secure data transmission in IoT-based systems using cloud computing. We evaluated our model’s attainment on the UNSW-NB15 dataset and contrasted it with other machine-learning (ML) methods, comprising decision trees (DT), random forests, and support vector machines (SVM). The outcomes demonstrate that our suggested GANs model performed better than expected in terms of precision, recall, F1 score, and area under the receiver operating characteristic curve (AUC-ROC). The GANs model generates a 98.07% accuracy rate for the testing dataset with a precision score of 98.45%, a recall score of 98.19%, an F1 score of 98.32%, and an AUC-ROC value of 0.998. These outcomes show how well our suggested GANs model predicts secure data transmission in cloud-based IoT-based systems, which is a crucial step in guaranteeing the confidentiality of IoT networks

A SYSTEMATIC ANALYSIS ON WORM DETECTION IN CLOUD BASED SYSTEMS

ABSTRACT An innovative breakthrough in computer science is cloud computing and involves several computers which are connected via the Internet or it is dispersed over a network. A large database, services, applications, software and resources are an integral part of this technology. It has the capability to operate a program or applications on numerous connected computers simultaneously and permits the users to enter applications and resources through a web browser or web service via the Internet anytime and anywhere. Current susceptibility in elementary technologies gravitates to expose doors for intrusions. Cloud computing offers enormous advantages such as cost reduction, dynamic virtualized resources, significant data storage and enhanced productivity. At the same time, numerous risks occur regarding security and intrusions, for example, worm can intercept cloud computing services, impair service, application or virtual in the cloud formation. Worm attacks are now more complex and resourceful making intruders more difficult to detect than previously. The motivation of this research is founded on ramifications presented by the worms. This paper presents different intrusion detection systems affecting cloud resources and service. Moreover, this paper illustrates how genetic algorithm can be integrated in detecting worm attacks in cloud computing more efficiently

Comprehensive Overview of Security Issues in the Internet and Mobile Applications

The popularity and advanced functionality of mobile devices have made them attractive targets for malicious and intrusive applications. Although strong internet security measures are in place for most mobile systems, the area where these systems often fail is the reliance on the user to make decisions that affect the security of a device. In our prime example, Android relies on users to understand the permissions requested by an application, on which depends its installation decision on the list of permissions. Previous research has shown that this reliance on users is ineffective, as most users do not understand or considerate permission information. Keywords: Internet Security, Mobile Applications, Mobile Security, Security Issue

CYBER SECURITY IN INDUSTRIAL CONTROL SYSTEMS (ICS): A SURVEY OF ROWHAMMER VULNERABILITY

Increasing dependence on Information and Communication Technologies (ICT) and especially on the Internet in Industrial Control Systems (ICS) has made these systems the primary target of cyber-attacks. As ICS are extensively used in Critical Infrastructures (CI), this makes CI more vulnerable to cyber-attacks and their protection becomes an important issue. On the other hand, cyberattacks can exploit not only software but also physics; that is, they can target the fundamental physical aspects of computation. The newly discovered RowHammer (RH) fault injection attack is a serious vulnerability targeting hardware on reliability and security of DRAM (Dynamic Random Access Memory). Studies on this vulnerability issue raise serious security concerns.  The purpose of this study was to overview the RH phenomenon in DRAMs and its possible security risks on ICSs and to discuss a few possible realistic RH attack scenarios for ICSs. The results of the study revealed that RH is a serious security threat to any computer-based system having DRAMs, and this also applies to ICS

A first look at the misuse and abuse of the IPv4 Transfer Market

The depletion of the unallocated address space in combination with the slow pace of IPv6 deployment have given rise to the IPv4 transfer market, namely the trading of allocated IPv4 prefixes between ASes. While RIRs have established detailed policies in an effort to regulate the IPv4 transfer market for malicious networks such as spammers and bulletproof ASes, IPv4 transfers pose an opportunity to bypass reputational penalties of abusive behaviour since they can obtain "clean" address space or offload blacklisted address space. Additionally, IP transfers create a window of uncertainty about legitimate ownership of prefixes, which adversaries to hijack parts of the transferred address space. In this paper, we provide the first detailed study of how transferred IPv4 prefixes are misused in the wild by synthesizing an array of longitudinal IP blacklists and lists of prefix hijacking incidents. Our findings yield evidence that the transferred network blocks are used by malicious networks to address botnets and fraudulent sites in much higher rates compared to non-transferred addresses, while the timing of the attacks indicates efforts to evade filtering mechanisms