Users really do respond to smishing

Text phish messages, referred to as Smishing is a type of social engineering attack where fake text messages are created, and used to lure users into responding to those messages. These messages aim to obtain user credentials, install malware on the phones, or launch smishing attacks. They ask users to reply to their message, click on a URL that redirects them to a phishing website, or call the provided number. Thousands of mobile users are affected by smishing attacks daily. Drawing inspiration by the works of Tu et al. (USENIX Security, 2019) on Robocalls and Tischer et al. (IEEE Symposium on Security and Privacy, 2016) on USB drives, this paper investigates why smishing works. Accordingly, we designed smishing experiments and sent phishing SMSes to 265 users to measure the efficacy of smishing attacks. We sent eight fake text messages to participants and recorded their CLICK, REPLY, and CALL responses along with their feedback in a post-test survey. Our results reveal that 16.92% of our participants had potentially fallen for our smishing attack. To test repeat phishing, we subjected a set of randomly selected participants to a second round of smishing attacks with a different message than the one they received in the first round. As a result, we observed that 12.82% potentially fell for the attack again. Using logistic regression, we observed that a combination of user REPLY and CLICK actions increased the odds that a user would respond to our smishing message when compared to CLICK. Additionally, we found a similar statistically significant increase when comparing Facebook and Walmart entity scenario to our IRS baseline.Comment: CODASPY'2

Adversarial Perturbations Against Real-Time Video Classification Systems

Recent research has demonstrated the brittleness of machine learning systems to adversarial perturbations. However, the studies have been mostly limited to perturbations on images and more generally, classification that does not deal with temporally varying inputs. In this paper we ask "Are adversarial perturbations possible in real-time video classification systems and if so, what properties must they satisfy?" Such systems find application in surveillance applications, smart vehicles, and smart elderly care and thus, misclassification could be particularly harmful (e.g., a mishap at an elderly care facility may be missed). We show that accounting for temporal structure is key to generating adversarial examples in such systems. We exploit recent advances in generative adversarial network (GAN) architectures to account for temporal correlations and generate adversarial samples that can cause misclassification rates of over 80% for targeted activities. More importantly, the samples also leave other activities largely unaffected making them extremely stealthy. Finally, we also surprisingly find that in many scenarios, the same perturbation can be applied to every frame in a video clip that makes the adversary's ability to achieve misclassification relatively easy

Collaborative exploration and collection of native plant genetic resources as assisted by agrobiodiversity fair

This article describes the agrobiodiversity fair aided exploration and collection expedition of native plant genetic resources in southern Lalitpur, jointly organized by the National Agriculture Genetic Resources Centre (NAGRC) and Group of Helping Hands (SAHAS) Nepal. In-district one-day agrobiodiversity fairs were organized in February and December month of 2019, altogether two times, and these agrobiodiversity fairs were used as a tool to explore plant genetic resources found in Bagmati and Mahankal Rural Municipalities of Lalitpur district. To collect these explored genetic resources during agrobiodiversity fairs, the joint field expedition, key informant survey, diversity rich farmers discussion was used as a collection tool. The present study explored, inventoried, collected and conserved 148 accessions of 44 crop species, the highest number (18 accessions) was of chayote followed by 10 accessions each of soybean, cowpea and maize and 9 accessions of common bean. Collections are generally new and unique. Many landraces, mostly from rice (13 landraces) were identified as extinct from the surveyed areas and few are under extinction mainly due to attraction of farmers to new high yielding varieties. The collected species with orthodox seeds were tested for germination ability and those that passed a minimum of 85% germination, were preserved in seedbank of NAGRC. NAGRC plans to characterize these accessions in the coming seasons depending upon the season of crop growing. The current expedition collected eight species for which mode of propagation is vegetative or those for which seed storage behavior falls under intermediate mode. NAGRC has been started expanding field genebank coverage using these accessions

Farmers’ preferences and agronomic evaluation of dynamic mixtures of rice and bean in Nepal

Field trials of rice and bean dynamic mixtures were carried out in low input and hill farming systems of Nepal from 2019 to 2021 to improve productivity and resilience. The rice trials were conducted in two locations (Jumla and Lamjung) and those on bean in Jumla, using a randomized complete block design with three replications. Dynamic mixtures were constructed from landraces, improved varieties and breeding lines for both crops. A total of 48 bean entries were used in Jumla, whereas 56 and 66 rice entries were used to make location-specific dynamic mixtures in Lamjung and Jumla, respectively. They were formed by mixing diverse varieties as a strategy to maintain a broad genetic base. Farmers (men and women) and technicians selected from the most complex mixture and the selections were added to the trials starting from second year. In rice, some mixtures and selections from the mixtures gave grain yield comparable to the improved check and higher than the local checks. In the case of bean, differences between entries were not significant but some of the selections received a high preference score. Overall, the dynamic mixtures appear as a reliable material for sustainable increase in yield in the low input and hill farming system of Nepal

Abdominal Wall Dyskinesia in a Child Presenting as Belly Dancers’ Syndrome: A Case Report

Belly dancer’s dyskinesia or syndrome is a rare condition characterized by involuntary, undulating, infrequent diaphragm movements. The etiologies for this disorder include nervous system disorders (peripheral or central), drug-induced, psychological, or idiopathic. This article describes a 10-year-old boy with an underlying psychological stressor who suddenly experienced involuntary abdominal wall movements after salbutamol nebulization. After a detailed history, physical examination, and abdominal ultrasound that revealed rapid rhythmic diaphragm movements, the child was diagnosed with salbutamol-induced belly dancer's dyskinesia with an underlying psychological problem. These movements subsided with medical and psychological therapy for two weeks. Belly dancer’s dyskinesia is a complex disorder that is difficult to diagnose but can be managed with medical treatment and psychological counseling alone in a few patients. In contrast, in other cases, surgical intervention may be required

Machine learning based fileless malware traffic classification using image visualization

Abstract 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

Farmers’ Preferences and Agronomic Evaluation of Dynamic Mixtures of Rice and Bean in Nepal

Field trials of rice and bean dynamic mixtures were carried out in low input and hill farming systems of Nepal from 2019 to 2021 to improve productivity and resilience. The rice trials were conducted in two locations (Jumla and Lamjung) and those on bean in Jumla, using a randomized complete block design with three replications. Dynamic mixtures were constructed from landraces, improved varieties and breeding lines for both crops. A total of 48 bean entries were used in Jumla, whereas 56 and 66 rice entries were used to make location-specific dynamic mixtures in Lamjung and Jumla, respectively. They were formed by mixing diverse varieties as a strategy to maintain a broad genetic base. Farmers (men and women) and technicians selected from the most complex mixture and the selections were added to the trials starting from second year. In rice, some mixtures and selections from the mixtures gave grain yield comparable to the improved check and higher than the local checks. In the case of bean, differences between entries were not significant but some of the selections received a high preference score. Overall, the dynamic mixtures appear as a reliable material for sustainable increase in yield in the low input and hill farming system of Nepal