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In today’s interconnected world, network traffic is replete with adversarial attacks. As technology evolves, these attacks are also becoming increasingly sophisticated, making them even harder to detect. Fortunately, artificial intelligence (AI) and, specifically machine learning (ML), have shown great success in fast and accurate detection, classification, and even analysis of such threats. Accordingly, there is a growing body of literature addressing how subfields of AI/ML (e.g., natural language processing (NLP)) are getting leveraged to accurately detect evasive malicious patterns in network traffic. In this paper, we delve into the current advancements in ML-based network traffic classification using image visualization. Through a rigorous experimental methodology, we first explore the process of network traffic to image conversion. Subsequently, we investigate how machine learning techniques can effectively leverage image visualization to accurately classify evasive malicious traces within network traffic. Through the utilization of production-level tools and utilities in realistic experiments, our proposed solution achieves an impressive accuracy rate of 99.48% in detecting fileless malware, which is widely regarded as one of the most elusive classes of malicious software.

 

Two-dimensional (2D) materials such as semiconductors and ferroelectrics are promising for future energy-efficient logic devices because of their extraordinary electronic properties at atomic thickness. In this work, we investigated a van der Waals heterostructure composited of 2D semiconducting MoS₂and 2D ferroelectric CuInP₂S₆(CIPS) and NiPS₃. Instead of using 2D ferroelectrics as conventional gate dielectric layers, here we applied CIPS and NiPS₃as a ferroelectric capping layer, and investigated a long-distance coupling effect with the gate upon the sandwiched 2D MoS₂channels. Our experimental results showed an outstanding enhancement of the electrodynamic gating in 2D MoS₂transistors, represented by a significant reduction of subthreshold swing at room temperature. This was due to the coupling-induced polarization of 2D ferroelectrics at 2D semiconductor surface which led to an effective and dynamic magnification of the gate capacitance. Meanwhile, the electrostatic gating was remained steady after adding the ferroelectric capping layer, providing ease and compatibility for further implementation with existing circuit and system design. Our work demonstrates the long-distance coupling effect of 2D ferroelectrics in a capping architecture, reveals its impacts from both electrodynamic and electrostatic perspectives, and expands the potential of 2D ferroelectrics to further improve the performance of energy-efficient nanoelectronics.

 

Abstract Two-dimensional (2D) molybdenum disulfide (MoS 2 ) has been recognized as a potential substitution of platinum (Pt) for electrochemical hydrogen evolution reaction (HER). However, the broad adoption of MoS 2 is hindered by its limited number of active sites and low inherent electrical conductivity. In this work, we employed a one-step solvothermal synthesis technique to construct a ternary hybrid structure consisting of dual-phase MoS 2, titanium carbide (Ti 3 C 2 ) MXene, and carbon nanotubes (CNTs), and demonstrated synergistic effects for active site exposure, surface area enlargement, and electrical conductivity improvement of the catalyst. The dual-phase MoS 2 (DP-MoS 2 ) is directly formed on the MXene with CNTs acting as crosslinks between 2D islands. The existence of edge-enriched metallic phase MoS 2 , the conductive backbone of MXene along with the crosslink function of CNTs clearly improves the overall HER performance of the ternary nanocomposite. Moreover, the integration of MoS 2 with MXene not only increases the interlayer distance of the 2D layers but also partially suppresses the MXene oxidation and the 2D layer restacking, leading to good catalytic stability. As a result, an overpotential of 169 mV and a low Tafel slope of 51 mV/dec was successfully achieved. This work paves a way for 2D-based electrocatalyst engineering and sheds light on the development of the next-generation noble metal-free HER electrocatalysts. 

Tailoring thermal transport by structural parameters could result in mechanically fragile and brittle networks. An indispensable goal is to design hierarchical architecture materials that combine thermal and mechanical properties in a continuous and cohesive network. A promising strategy to create such a hierarchical network targets additive manufacturing of hybrid porous voxels at nanoscale. Here we describe the convergence of agile additive manufacturing of porous hybrid voxels to tailor hierarchically and mechanically tunable objects. In one strategy, the uniformly distributed porous silica voxels, which form the basis for the control of thermal transport, are non-covalently interfaced with polymeric networks, yielding hierarchic super-elastic architectures with thermal insulation properties. Another additive strategy for achieving mechanical strength involves the versatile orthogonal surface hybridization of porous silica voxels retains its low thermal conductivity of 19.1 mW m⁻¹K⁻¹, flexible compressive recovery strain (85%), and tailored mechanical strength from 71.6 kPa to 1.5 MPa. The printed lightweight high-fidelity objects promise thermal aging mitigation for lithium-ion batteries, providing a thermal management pathway using 3D printed silica objects.

 

Soft, worm-like robots show promise in complex and constrained environments due to their robust, yet simple movement patterns. Although many such robots have been developed, they either rely on tethered power supplies and complex designs or cannot move external loads. To address these issues, we here introduce a novel, maggot-inspired, magnetically driven “mag-bot” that utilizes shape memory alloy-induced, thermoresponsive actuation and surface pattern-induced anisotropic friction to achieve locomotion inspired by fly larvae. This simple, untethered design can carry cargo that weighs up to three times its own weight with only a 17% reduction in speed over unloaded conditions thereby demonstrating, for the first time, how soft, untethered robots may be used to carry loads in controlled environments. Given their small scale and low cost, we expect that these mag-bots may be used in remote, confined spaces for small objects handling or as components in more complex designs.