Biblio

Ransomware attack is posting a serious threat against Android devices and stored data that could be locked or/and encrypted by such attack. Existing solutions attempt to detect and prevent such attack by studying different features and applying various analysis mechanisms including static, dynamic or both. In this paper, recent ransomware detection solutions were investigated and compared. Moreover, a deep analysis of android permissions was conducted to identify significant android permissions that can discriminate ransomware with high accuracy before harming users' devices. Consequently, based on the outcome of this analysis, a permissions-based ransomware detection system is proposed. Different classifiers were tested to build the prediction model of this detection system. After the evaluation of the ransomware detection service, the results revealed high detection rate that reached 96.9%. Additionally, the newly permission-based android dataset constructed in this research will be made available to researchers and developers for future work.

Artificial Intelligence also often referred to as machine learning is being labelled to as the future has been into light since more than a decade. Artificial Intelligence designated by the acronym AI has a vast scope of development and the developers have been working on with it constantly. AI is being associated with the existing objects in the world as well as with the ones that are about to arrive to improve them and make them more reliable. AI as it states in its name is intelligence, intelligence shown by the machines to work similar to humans and work on achieving the goals they are being provided with. Another application of AI could be to provide defenses against the present cyber threats, vehicle overrides etc. Also, AI might be intelligence but, in the end, it's still a bunch of codes, hence it is prone to be corrupted or misused by the world. To prevent the misuse of the technologies, it is necessary to deploy them with a sustainable defensive system as well. Obviously, there is going to be a default defense system but it is prone to be corrupted by the hackers or malfunctioning of the intelligence in certain scenarios which can result disastrous especially in case of Robotics. A proposal referred to as the “Guard Masking” has been offered in the following paper, to provide an alternative for securing Artificial Intelligence.

In this paper we propose a solution to support iOS developers in creating better applications, to use static analysis to investigate source code and detect secure coding issues while simultaneously pointing out good practices and/or secure APIs they should use.

The notion of edge computing introduces new computing functions away from centralized locations and closer to the network edge and thus facilitating new applications and services. This enhanced computing paradigm is provides new opportunities to applications developers, not available otherwise. In this talk, I will discuss why placing computation functions at the extreme edge of our network infrastructure, i.e., in wireless Access Points and home set-top boxes, is particularly beneficial for a large class of emerging applications. I will discuss a specific approach, called ParaDrop, to implement such edge computing functionalities, and use examples from different domains – smarter homes, sustainability, and intelligent transportation – to illustrate the new opportunities around this concept.

Computed Tomography (CT) Image Reconstruction is an important technique used in a wide range of applications, ranging from explosive detection, medical imaging to scientific imaging. Among available reconstruction methods, Model Based Iterative Reconstruction (MBIR) produces higher quality images and allows for the use of more general CT scanner geometries than is possible with more commonly used methods. The high computational cost of MBIR, however, often makes it impractical in applications for which it would otherwise be ideal. This paper describes a new MBIR implementation that significantly reduces the computational cost of MBIR while retaining its benefits. It describes a novel organization of the scanner data into super-voxels (SV) that, combined with a super-voxel buffer (SVB), dramatically increase locality and prefetching, enable parallelism across SVs and lead to an average speedup of 187 on 20 cores.

Cameras have become nearly ubiquitous with the rise of smartphones and laptops. New wearable devices, such as Google Glass, focus directly on using live video data to enable augmented reality and contextually enabled services. However, granting applications full access to video data exposes more information than is necessary for their functionality, introducing privacy risks. We propose a privilege-separation architecture for visual recognizer applications that encourages modularization and least privilege–-separating the recognizer logic, sandboxing it to restrict filesystem and network access, and restricting what it can extract from the raw video data. We designed and implemented a prototype that separates the recognizer and application modules and evaluated our architecture on a set of 17 computer-vision applications. Our experiments show that our prototype incurs low overhead for each of these applications, reduces some of the privacy risks associated with these applications, and in some cases can actually increase the performance due to increased parallelism and concurrency.

Cloud computing paradigm is being used because of its low up-front cost. In recent years, even mobile phone users store their data at Cloud. Customer information stored at Cloud needs to be protected against potential intruders as well as cloud service provider. There is threat to the data in transit and data at cloud due to different possible attacks. Organizations are transferring important information to the Cloud that increases concern over security of data. Cryptography is common approach to protect the sensitive information in Cloud. Cryptography involves managing encryption and decryption keys. In this paper, we compare key management methods, apply key management methods to various cloud environments and analyze symmetric key cryptography algorithms.