Learning-based malware detection systems (LB-MDS) play a crucial role in defending computer systems from malicious attacks. Nevertheless, these systems can be vulnerable to various attacks, which can have significant consequences. Software obfuscation techniques can be used to modify the features of malware, thereby avoiding its classification as malicious by LB-MDS. However, existing portable executable (PE) malware datasets primarily use a single obfuscation technique, which LB-MDS has already learned, leading to a loss of their robustness evaluation ability. Therefore, creating a dataset with diverse features that were not observed during LB-MDS training has become the main challenge in evaluating the robustness of LB-MDS.

We propose a obfuscation dataset ERMDS that solves the problem of evaluating the robustness of LB-MDS by generating malwares with diverse features. When designing this dataset, we created three types of obfuscation spaces, corresponding to binary obfuscation, source code obfuscation, and packing obfuscation. Each obfuscation space has multiple obfuscation techniques, each with different parameters. The obfuscation techniques in these three obfuscation spaces can be used in combination and can be reused. This enables us to theoretically obtain an infinite number of obfuscation combinations, thereby creating malwares with a diverse range of features that have not been captured by LB-MDS.

To assess the effectiveness of the ERMDS obfuscation dataset, we create an instance of the obfuscation dataset called ERMDS-X. By utilizing this dataset, we conducted an evaluation of the robustness of two LB-MDS models, namely MalConv and EMBER, as well as six commercial antivirus software products, which are anonymized as AV1-AV6. The results of our experiments showed that ERMDS-X effectively reveals the limitations in the robustness of existing LB-MDS models, leading to an average accuracy reduction of 20% in LB-MDS and 32% in commercial antivirus software. We conducted a comprehensive analysis of the factors that contributed to the observed accuracy decline in both LB-MDS and commercial antivirus software. We have released the ERMDS-X dataset as an open-source resource, available on GitHub at https://github.com/lcjia94/ERMDS.

Spiking Neural Networks (SNNs) show great potential for solving Artificial Intelligence (AI) applications. At the preliminary stage of SNNs, benchmarks are essential for evaluating and optimizing SNN algorithms, software, and hardware toward AI scenarios. However, a majority of SNN benchmarks focus on evaluating SNN for brain science, which has distinct neural network architectures and targets. Even though there have several benchmarks evaluating SNN for AI, they only focus on a single stage of training and inference or a processing fragment of a whole stage without accuracy information. Thus, the existing SNN benchmarks lack an end-to-end perspective that not only covers both training and inference but also provides a whole training process to a target accuracy level.

This paper presents SNNBench—the first end-to-end AI-oriented SNN benchmark covering the processing stages of training and inference and containing the accuracy information. Focusing on two typical AI applications: image classification and speech recognition, we provide nine workloads that consider the typical characteristics of SNN, i.e., the dynamics of spiking neurons, and AI, i.e., learning paradigms including supervised and unsupervised learning, learning rules like backpropagation, connection types like fully connected, and accuracy. The evaluations of SNNBench on both CPU and GPU show its effectiveness. The specifications, source code, and results will be publicly available from https://www.benchcouncil.org/SNNBench.

Modern web services such as social media, online messaging, and web search support billions of users, requiring data centers that scale to hundreds of thousands of servers, i.e., hyperscale. The key challenge in enabling hyperscale web services arise from (1) an unprecedented growth in data, users, and service functionality and (2) a decline in hardware performance scaling. We highlight a dissertation’s contributions in bridging the software and hardware worlds to realize more efficient hyperscale services despite these challenges.

Generative Pretrained Transformer, often known as GPT, is an innovative kind of Artificial Intelligence (AI) which can produce writing that seems to have been written by a person. OpenAI created this AI language model called ChatGPT. It is built using the GPT architecture and is trained on a large corpus of text data to respond to natural language inquiries that resemble a person’s requirements. This technology has lots of applications in healthcare. The need for accurate and current data is one of the major obstacles to adopting ChatGPT in healthcare. GPT must have access to precise and up-to-date medical data to provide trustworthy suggestions and treatment options. It might be accomplished by ensuring that the data used by GPT is received from reliable sources and that the data is updated regularly. Since sensitive medical information would be involved, it will also be crucial to consider privacy and security issues while utilising GPT in the healthcare industry. This paper briefs about ChatGPT and its need for healthcare, its significant Work Flow Dimensions and typical features of ChatGPT for the Healthcare domain. Finally, it identified and discussed significant applications of ChatGPT for healthcare. ChatGPT can comprehend the conversational context and provide contextually appropriate replies. Its effectiveness as a conversational AI tool makes it useful for chatbots, virtual assistants, and other applications. However, we see many limitations in medical ethics, data interpretation, accountability and other issues related to the privacy. Regarding specialised tasks like text creation, language translation, text categorisation, text summarisation, and creating conversation systems, ChatGPT has been pre-trained on a large corpus of text data, and somewhat satisfactory results can be expected. Moreover, it can also be utilised for various Natural Language Processing (NLP) activities, including sentiment analysis, part-of-speech tagging, and named entity identification.

The Reserve Bank of India (RBI) has recently launched the country’s first pilot project for the digital currency known as the digital rupee or e-Rupee (e₹). The launch of the digital rupee represents a significant advancement in the “Digital India” revolution. It will be a fantastic opportunity for India since it might make conducting business easier while enhancing the security and resilience of the overall payments system. Digital currency attempts to rapidly progress monetary policy to disrupt physical money, lower the cost of financial transactions, and reshape how the money will circulate. Although the effects of digital currency cannot be foreseen, it is extremely important to thoroughly research digital currency and its effects on the operational stage. The development of a digital currency infrastructure has some challenges in terms of performance, scalability, and different usage scenarios. The article clarifies what e₹ is. How does it work? What makes it different from cryptocurrencies? What are the major challenges and prospects for it in India?