The proliferation of Internet of Things(IoT)technology has exponentially increased the number of devices interconnected over networks,thereby escalating the potential vectors for cybersecurity *** response,this study ...
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The proliferation of Internet of Things(IoT)technology has exponentially increased the number of devices interconnected over networks,thereby escalating the potential vectors for cybersecurity *** response,this study rigorously applies and evaluates deep learning models—namely Convolutional Neural Networks(CNN),Autoencoders,and Long Short-Term Memory(LSTM)networks—to engineer an advanced Intrusion Detection System(IDS)specifically designed for IoT *** the comprehensive UNSW-NB15 dataset,which encompasses 49 distinct features representing varied network traffic characteristics,our methodology focused on meticulous data preprocessing including cleaning,normalization,and strategic feature selection to enhance model performance.A robust comparative analysis highlights the CNN model’s outstanding performance,achieving an accuracy of 99.89%,precision of 99.90%,recall of 99.88%,and an F1 score of 99.89%in binary classification tasks,outperforming other evaluated models *** results not only confirm the superior detection capabilities of CNNs in distinguishing between benign and malicious network activities but also illustrate the model’s effectiveness in multiclass classification tasks,addressing various attack vectors prevalent in IoT *** empirical findings from this research demonstrate deep learning’s transformative potential in fortifying network security infrastructures against sophisticated cyber threats,providing a scalable,high-performance solution that enhances security measures across increasingly complex IoT *** study’s outcomes are critical for security practitioners and researchers focusing on the next generation of cyber defense mechanisms,offering a data-driven foundation for future advancements in IoT security strategies.
Foundation models(FMs) [1] have revolutionized software development and become the core components of large software systems. This paradigm shift, however, demands fundamental re-imagining of software engineering theo...
Foundation models(FMs) [1] have revolutionized software development and become the core components of large software systems. This paradigm shift, however, demands fundamental re-imagining of software engineering theories and methodologies [2]. Instead of replacing existing software modules implemented by symbolic logic, incorporating FMs' capabilities to build software systems requires entirely new modules that leverage the unique capabilities of ***, while FMs excel at handling uncertainty, recognizing patterns, and processing unstructured data, we need new engineering theories that support the paradigm shift from explicitly programming and maintaining user-defined symbolic logic to creating rich, expressive requirements that FMs can accurately perceive and implement.
Multiarmed bandit(MAB) models are widely used for sequential decision-making in uncertain environments, such as resource allocation in computer communication systems.A critical challenge in interactive multiagent syst...
Multiarmed bandit(MAB) models are widely used for sequential decision-making in uncertain environments, such as resource allocation in computer communication systems.A critical challenge in interactive multiagent systems with bandit feedback is to explore and understand the equilibrium state to ensure stable and tractable system performance.
Graph processing has been widely used in many scenarios,from scientific computing to artificial *** processing exhibits irregular computational parallelism and random memory accesses,unlike traditional ***,running gra...
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Graph processing has been widely used in many scenarios,from scientific computing to artificial *** processing exhibits irregular computational parallelism and random memory accesses,unlike traditional ***,running graph processing workloads on conventional architectures(e.g.,CPUs and GPUs)often shows a significantly low compute-memory ratio with few performance benefits,which can be,in many cases,even slower than a specialized single-thread graph *** domain-specific hardware designs are essential for graph processing,it is still challenging to transform the hardware capability to performance boost without coupled software *** article presents a graph processing ecosystem from hardware to *** start by introducing a series of hardware accelerators as the foundation of this ***,the codesigned parallel graph systems and their distributed techniques are presented to support graph ***,we introduce our efforts on novel graph applications and hardware *** results show that various graph applications can be efficiently accelerated in this graph processing ecosystem.
This systematic literature review delves into the dynamic realm of graphical passwords, focusing on the myriad security attacks they face and the diverse countermeasures devised to mitigate these threats. The core obj...
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Berth Allocation Problem (BAP) is a renowned difficult combinatorial optimization problem that plays a crucial role in maritime transportation systems. BAP is categorized as non-deterministic polynomial-time hard (NP-...
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Low-light image enhancement is highly desirable for outdoor image processing and computer vision applications. Research conducted in recent years has shown that images taken in low-light conditions often pose two main...
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Liver cirrhosis often occurs as a result of the lengthy and persistent progression of chronic liver disorders. It is a key crucial cause of death on a global scale. Early diagnosis and identification of cirrhosis are ...
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With the widespread use of blockchain technology for smart contracts and decentralized applications on the Ethereum platform, the blockchain has become a cornerstone of trust in the modern financial system. However, i...
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With the widespread use of blockchain technology for smart contracts and decentralized applications on the Ethereum platform, the blockchain has become a cornerstone of trust in the modern financial system. However, its anonymity has provided new ways for Ponzi schemes to commit fraud, posing significant risks to investors. Current research still has some limitations, for example, Ponzi schemes are difficult to detect in the early stages of smart contract deployment, and data imbalance is not considered. In addition, there is room for improving the detection accuracy. To address the above issues, this paper proposes LT-SPSD (LSTM-Transformer smart Ponzi schemes detection), which is a Ponzi scheme detection method that combines Long Short-Term Memory (LSTM) and Transformer considering the time-series transaction information of smart contracts as well as the global information. Based on the verified smart contract addresses, account features, and code features are extracted to construct a feature dataset, and the SMOTE-Tomek algorithm is used to deal with the imbalanced data classification problem. By comparing our method with the other four typical detection methods in the experiment, the LT-SPSD method shows significant performance improvement in precision, recall, and F1-score. The results of the experiment confirm the efficacy of the model, which has some application value in Ethereum Ponzi scheme smart contract detection.
The challenge of bankruptcy prediction, critical for averting financial sector losses, is amplified by the prevalence of imbalanced datasets, which often skew prediction models. Addressing this, our study introduces t...
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