Unveiling the frontiers of deep learning: innovations shaping diverse domains

Deep learning (DL) enables the development of computer models that are capable of learning, visualizing, optimizing, refining, and predicting data. In recent years, DL has been applied in a range of fields, including audio-visual data processing, agriculture, transportation prediction, natural language, biomedicine, disaster management, bioinformatics, drug design, genomics, face recognition, and ecology. To explore the current state of deep learning, it is necessary to investigate the latest developments and applications of deep learning in these disciplines. However, the literature is lacking in exploring the applications of deep learning in all potential sectors. This paper thus extensively investigates the potential applications of deep learning across all major fields of study as well as the associated benefits and challenges. As evidenced in the literature, DL exhibits accuracy in prediction and analysis, makes it a powerful computational tool, and has the ability to articulate itself and optimize, making it effective in processing data with no prior training. Given its independence from training data, deep learning necessitates massive amounts of data for effective analysis and processing, much like data volume. To handle the challenge of compiling huge amounts of medical, scientific, healthcare, and environmental data for use in deep learning, gated architectures like LSTMs and GRUs can be utilized. For multimodal learning, shared neurons in the neural network for all activities and specialized neurons for particular tasks are necessary.Comment: 64 pages, 3 figures, 3 table

LightBox: Full-stack Protected Stateful Middlebox at Lightning Speed

Running off-site software middleboxes at third-party service providers has been a popular practice. However, routing large volumes of raw traffic, which may carry sensitive information, to a remote site for processing raises severe security concerns. Prior solutions often abstract away important factors pertinent to real-world deployment. In particular, they overlook the significance of metadata protection and stateful processing. Unprotected traffic metadata like low-level headers, size and count, can be exploited to learn supposedly encrypted application contents. Meanwhile, tracking the states of 100,000s of flows concurrently is often indispensable in production-level middleboxes deployed at real networks. We present LightBox, the first system that can drive off-site middleboxes at near-native speed with stateful processing and the most comprehensive protection to date. Built upon commodity trusted hardware, Intel SGX, LightBox is the product of our systematic investigation of how to overcome the inherent limitations of secure enclaves using domain knowledge and customization. First, we introduce an elegant virtual network interface that allows convenient access to fully protected packets at line rate without leaving the enclave, as if from the trusted source network. Second, we provide complete flow state management for efficient stateful processing, by tailoring a set of data structures and algorithms optimized for the highly constrained enclave space. Extensive evaluations demonstrate that LightBox, with all security benefits, can achieve 10Gbps packet I/O, and that with case studies on three stateful middleboxes, it can operate at near-native speed.Comment: Accepted at ACM CCS 201

Internet Censorship: An Integrative Review of Technologies Employed to Limit Access to the Internet, Monitor User Actions, and their Effects on Culture

The following conducts an integrative review of the current state of Internet Censorship in China, Iran, and Russia, highlights common circumvention technologies (CTs), and analyzes the effects Internet Censorship has on cultures. The author spends a large majority of the paper delineating China’s Internet infrastructure and prevalent Internet Censorship Technologies/Techniques (ICTs), paying particular attention to how the ICTs function at a technical level. The author further analyzes the state of Internet Censorship in both Iran and Russia from a broader perspective to give a better understanding of Internet Censorship around the globe. The author also highlights specific CTs, explaining how they function at a technical level. Findings indicate that among all three nation-states, state control of Internet Service Providers is the backbone of Internet Censorship. Specifically, within China, it is discovered that the infrastructure functions as an Intranet, thereby creating a closed system. Further, BGP Hijacking, DNS Poisoning, and TCP RST attacks are analyzed to understand their use-case within China. It is found that Iran functions much like a weaker version of China in regards to ICTs, with the state seemingly using the ICT of Bandwidth Throttling rather consistently. Russia’s approach to Internet censorship, in stark contrast to Iran and China, is found to rely mostly on the legislative system and fear to implement censorship, though their technical level of ICT implementation grows daily. TOR, VPNs, and Proxy Servers are all analyzed and found to be robust CTs. Drawing primarily from the examples given throughout the paper, the author highlights the various effects of Internet Censorship on culture – noting that at its core, Internet Censorship destroys democracy