Leverage Variational Graph Representation for Model Poisoning on Federated Learning.

This article puts forth a new training data-untethered model poisoning (MP) attack on federated learning (FL). The new MP attack extends an adversarial variational graph autoencoder (VGAE) to create malicious local models based solely on the benign local models overheard without any access to the training data of FL. Such an advancement leads to the VGAE-MP attack that is not only efficacious but also remains elusive to detection. VGAE-MP attack extracts graph structural correlations among the benign local models and the training data features, adversarially regenerates the graph structure, and generates malicious local models using the adversarial graph structure and benign models' features. Moreover, a new attacking algorithm is presented to train the malicious local models using VGAE and sub-gradient descent, while enabling an optimal selection of the benign local models for training the VGAE. Experiments demonstrate a gradual drop in FL accuracy under the proposed VGAE-MP attack and the ineffectiveness of existing defense mechanisms in detecting the attack, posing a severe threat to FL.

An Optimization Model for Appraising Intrusion-Detection Systems for Network Security Communications: Applications, Challenges, and Solutions.

Cyber-attacks are getting increasingly complex, and as a result, the functional concerns of intrusion-detection systems (IDSs) are becoming increasingly difficult to resolve. The credibility of security services, such as privacy preservation, authenticity, and accessibility, may be jeopardized if breaches are not detected. Different organizations currently utilize a variety of tactics, strategies, and technology to protect the systems' credibility in order to combat these dangers. Safeguarding approaches include establishing rules and procedures, developing user awareness, deploying firewall and verification systems, regulating system access, and forming computer-issue management groups. The effectiveness of intrusion-detection systems is not sufficiently recognized. IDS is used in businesses to examine possibly harmful tendencies occurring in technological environments. Determining an effective IDS is a complex task for organizations that require consideration of many key criteria and their sub-aspects. To deal with these multiple and interrelated criteria and their sub-aspects, a multi-criteria decision-making (MCMD) approach was applied. These criteria and their sub-aspects can also include some ambiguity and uncertainty, and thus they were treated using q-rung orthopair fuzzy sets (q-ROFS) and q-rung orthopair fuzzy numbers (q-ROFNs). Additionally, the problem of combining expert and specialist opinions was dealt with using the q-rung orthopair fuzzy weighted geometric (q-ROFWG). Initially, the entropy method was applied to assess the priorities of the key criteria and their sub-aspects. Then, the combined compromised solution (CoCoSo) method was applied to evaluate six IDSs according to their effectiveness and reliability. Afterward, comparative and sensitivity analyses were performed to confirm the stability, reliability, and performance of the proposed approach. The findings indicate that most of the IDSs appear to be systems with high potential. According to the results, Suricata is the best IDS that relies on multi-threading performance.

Time-resolved and temperature tuneable measurements of fluorescent intensity using a smartphone fluorimeter.

A smartphone fluorimeter capable of time-based fluorescence intensity measurements at various temperatures is reported. Excitation is provided by an integrated UV LED (λex = 370 nm) and detection obtained using the in-built CMOS camera. A Peltier is integrated to allow measurements of the intensity over T = 10 to 40 °C. All components are controlled using a smartphone battery powered Arduino microcontroller and a customised Android application that allows sequential fluorescence imaging and quantification every δt = 4 seconds. The temperature dependence of fluorescence intensity for four emitters (rhodamine B, rhodamine 6G, 5,10,15,20-tetraphenylporphyrin and 6-(1,4,8,11-tetraazacyclotetradecane)2-ethyl-naphthalimide) are characterised. The normalised fluorescence intensity over time of the latter chemosensor dye complex in the presence of Zn2+ is observed to accelerate with an increasing rate constant, k = 1.94 min-1 at T = 15 °C and k = 3.64 min-1 at T = 30 °C, approaching a factor of ∼2 with only a change in temperature of ΔT = 15 °C. Thermally tuning these twist and bend associated rates to optimise sensor approaches and device applications is proposed.

Optical fiber smartphone spectrometer.

An optical fiber-based smartphone spectrometer incorporating an endoscopic fiber bundle is demonstrated. The endoscope allows transmission of the smartphone camera LED light to a sample, removing complications from varying background illumination. The reflected spectra collected from a surface or interface is dispersed onto the camera CMOS using a reflecting diffraction grating. A spectral resolution as low as δλ∼2.0 nm over a bandwidth of Δλ∼250 nm is obtained using a slit width, ωslit=0.7 mm. The instrument has vast potential in a number of industrial applications including agricultural produce analysis. Spectral analysis of apples shows straightforward measurement of the pigments anthocyanins, carotenoid, and chlorophyll, all of which decrease with increasing storage time.

Smartphone laser beam spatial profiler.

A simple, low-cost, portable, smartphone-based laser beam profiler for characterizing laser beam profiles is reported. The beam profiler utilizes a phosphor silica glass plate to convert UV light into visible (green) light that can be directly imaged onto an existing smartphone CMOS chip and analyzed using a customized app. 3D printing enables the ready fabrication of the instrument package. The beam's diameter, shape, divergence, beam quality factor, and output power are measured for two UV lasers: a CW 244 nm frequency-doubled Ar ion laser and a pulsed 193 nm ArF exciplex laser. The availability of specialized phosphor converters can extend the instrument from the UV to the near infrared and beyond, and the smartphone platform extends the Internet of Things to map laser beam profiles simultaneously in different locations.

Combined "dual" absorption and fluorescence smartphone spectrometers.

A combined "dual" absorption and fluorescence smartphone spectrometer is demonstrated. The optical sources used in the system are the white flash LED of the smartphone and an orthogonally positioned and interchangeable UV (λex=370 nm) and blue (λex=450 nm) LED. The dispersive element is a low-cost, nano-imprinted diffraction grating coated with Au. Detection over a 300 nm span with 0.42 nm/pixel resolution was carried out with the camera CMOS chip. By integrating the blue and UV excitation sources into the white LED circuitry, the entire system is self-contained within a 3D printed case and powered from the smartphone battery; the design can be scaled to add further excitation sources. Using a customized app, acquisition of absorption and fluorescence spectra are demonstrated using a blue-absorbing and green-emitting pH-sensitive amino-naphthalimide-based fluorescent probe and a UV-absorbing and blue-emitting Zn2+-sensitive fluoro-ionophore.