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In today’s digital landscape, critical services are increasingly dependent on network connectivity, thus cybersecurity has become paramount. Indeed, the constant escalation of cyberattacks, including zero-day exploits, poses a significant threat. While Network Intrusion Detection Systems (NIDSs) leveraging machine-learning and deep-learning models have proven effective in recent studies, they encounter limitations such as the need for abundant samples of malicious traffic and full retraining upon encountering new attacks. These limitations hinder their adaptability in real-world scenarios. To address these challenges, we design a novel NIDS capable of promptly adapting to classify new attacks and provide timely predictions. Our proposal for attack-traffic classification adopts Few-Shot Class-Incremental Learning (FSCIL) and is based on the Rethinking Few-Shot (RFS) approach, which we experimentally prove to overcome other FSCIL state-of-the-art alternatives based on either meta-learning or transfer learning. We evaluate the proposed NIDS across a wide array of cyberattacks whose traffic is collected in recent publicly available datasets to demonstrate its robustness across diverse network-attack scenarios, including malicious activities in an Internet-of-Things context and cyberattacks targeting servers. We validate various design choices as well, involving the number of traffic samples per attack available, the impact of the features used to represent the traffic objects, and the time to deliver the classification verdict. Experimental results witness that our proposed NIDS effectively retains previously acquired knowledge (with over 94% F1-score) while adapting to new attacks with only few samples available (with over 98% F1-score). Thus, it outperforms non-FSCIL state of the art in terms of classification effectiveness and adaptation time. Moreover, our NIDS exhibits high performance even with traffic collected within short time frames, achieving 95% F1-score while reducing the time-to-insight. Finally, we identify possible limitations likely arising in specific application contexts and envision promising research avenues to mitigate them.

Significant in lifestyle have reshaped the Internet landscape, resulting in notable shifts in both the magnitude of Internet traffic and the diversity of apps utilized. The increased adoption of communication-and-collaboration apps, also fueled by lockdowns in the COVID pandemic years, has heavily impacted the management of network infrastructures and their traffic. A notable characteristic of these apps is their multi-activity nature, e.g., they can be used for chat and (interactive) audio/video in the same usage session: predicting and managing the traffic they generate is an important but especially challenging task. In this study, we focus on real data from four popular apps belonging to the aforementioned category: Skype, Teams, Webex, and Zoom. First, we collect traffic data from these apps, reliably label it with both the app and the specific user activity and analyze it from the perspective of traffic prediction. Second, we design data-driven models to predict this traffic at the finest granularity (i.e., at packet level) employing four advanced multitask deep learning architectures and investigating three different training strategies. The trade-off between performance and complexity is explored as well. We publish the dataset and release our code as open source to foster the replicability of our analysis. Third, we leverage the packet-level prediction approach to perform aggregate prediction at different timescales. Fourth, our study pioneers the trustworthiness analysis of these predictors via the application of eXplainable Artificial Intelligence to $(a)$ interpret their forecasting results and $(b)$ evaluate their reliability, highlighting the relative importance of different parts of observed traffic and thus offering insights for future analyses and applications. The insights gained from the analysis provided with this work have implications for various network management tasks, including monitoring, planning, resource allocation, and enforcing security policies.

Software-Defined Networking (SDN) and Blockchain are leading technologies used worldwide to establish safe network communication as well as build secure network infrastructures. They provide a robust and reliable platform to address threats and face challenges such as security, privacy, flexibility, scalability, and confidentiality. Driven by these assumptions, this paper presents an optimized energy-efficient and secure Blockchain-based software-defined IoT framework for smart networks. Indeed, SDN and Blockchain technologies have proven to be able to suitably manage resource utilization and to develop secure network communication across the IoT ecosystem. However, there is a lack of research works that present a comprehensive definition of such a framework that can meet the requirements of the IoT ecosystem (i.e. efficient energy utilization and reduced end-to-end delay). Therefore, in this research, we present a layered hierarchical architecture for the deployment of a distributed yet efficient Blockchain-enabled SDN-IoT framework that ensures efficient cluster-head selection and secure network communication via the identification and isolation of rouge switches. Besides, the Blockchain-enabled flow-rules record keeps track of the rules enforced in the switches and maintains the consistency within the controller cluster. Finally, we assess the performance of the proposed framework in a simulation environment and show that it can achieve optimized energy-utilization, end-to-end delay, and throughput compared to considered baselines, thus being able to achieve efficiency and security in the smart network.

Abstract Background Asparagus acutifolius L. is a dioecious and native plant species, widely distributed in the Mediterranean Basin. It is known for its fine flavour and could represent an important resource for cultivation programs in desert areas. Few molecular studies have been performed on this species. In the present paper, the ISSR technique was employed to study genetic diversity in Italian A. acutifolius. Results Twenty-three primers produced a total of 228 polymorphic fragments used to evaluate genetic variation. FST (0.4561) and Theta B (0.4776) values indicate a wide genetic variation among the samples examined. The distance UPGMA tree grouped together the genotypes strictly according to their geographical origin, showing that each sample is genetically structured and can be considered a distinct population. AMOVA analysis further confirmed genetic structuring of the populations. Population-specific fragments were also detected. Conclusion The results suggest that ISSR markers are useful in distinguishing the populations of A. acutifolius according to geographical origin, and confirm the importance of genetic studies for designing germplasm conservation strategies.

AbstractAn increase in the circulation of HIV-1 non-B subtypes has been observed in recent years in Western European countries. Due to the lack of data on the circulation of HIV-1 non-B subtypes among European HIV-1-infected men who have sex with men (MSM), a biomolecular study was conducted in Rome, Italy. HIV-1 partial polgene sequences from 111 MSM individuals (76 drug naive and 35 drug experienced) were collected during the years 2004–2006. All these sequences were analyzed using the REGA HIV-1 Subtyping Tool, and aligned using CLUSTAL X followed by manual editing using the Bioedit software. A BLAST search for non-B subtype sequences was also performed. Twenty-six (23.4%) MSM were not Italians. Eight individuals (7.2%) were diagnosed as HIV infected before 1991, 20 (18.0%) between 1991 and 1999, and 83 (74.8%) from 2000 to 2006. Fifteen (15/111, 13.5%) individuals were infected with the non-B subtype. The percentage of infection with HIV-1 non-B subtypes was 8.2% (7/85) among Italian MSM and 30.8% (8/26) among the non-Italians (OR = 4.95 95% IC: 1.40–17.87). Individuals infected with the non-B subtype were significantly younger than those infected with the HIV-1 B subtype (28 years vs. 34 years, p= 0.003). The CRFs were more prevalent (8.1%) than pure subtypes (5.4%), which were distributed as follows: subtype C (2.6%), subtype A1 (1.7%), and subtype F1 (0.9%). Major mutations conferring resistance to antiretroviral drugs (ARV) were not found among HIV-1 non-B subtype drug-naive patients but were found in two ARV-experienced individuals. The data show that viral diversity is likely increasing in a population group that had been previously characterized by the circulation of HIV-1 subtype B. [ABSTRACT FROM AUTHOR]

AbstractLittle information is available about the prevalence of resistance mutations to reverse transcriptase (RT) and protease (PR) inhibitors of HIV-1, after the introduction of antiretroviral treatment in Bulgaria. To fill this gap, we analyzed 80 plasma samples from HIV-1-infected Bulgarian patients, 22 naive at antiretroviral treatment (ARV) and 58 ARV experienced. The subtypes B and A resulted in the two most prevalent (41 patients and 18 patients, respectively). The proportion of subtype B among naive and treated patients was similar in each group (57 vs. 47, p 0.62), while a major proportion of subtypes A was present in drug-naive patients rather than in treated patients [8/22 (36.4) vs. 10/58 (17.2), p 0.08]. Two (9.1) naive patients and 40 (70.1) drug-experienced patients had viruses carrying at least one mutation conferring resistance to ARV drugs. Of 57 patients having experience with nucleoside reverse transcriptase inhibitors (NRTI), 32 (56.1) had NRTI resistance mutations; 8/14 (57.2) patients having experience with non-NRTI (NNRTI) had viruses carrying NNRTI resistance mutations; and 21/46 (45.7) patients having experience with protease inhibitors (PI) had PI resistance mutations. The commonest resistance mutations resulted in the NRTI mutation M184V (42.1) and the PI mutation L90M (24.1). In conclusion, due to the detection of the substantial transmission of resistant variants to newly infected individuals, continuous surveillance is required, since greater access to highly active antiretroviral therapy (HAART) will be expected in Bulgaria. Furthermore, surveillance of PR and RT sequences is also convenient to monitor the introduction of nonsubtype B HIV-1 strains in Bulgaria. [ABSTRACT FROM AUTHOR]

AbstractLittle is known about the HIV-1 epidemic in Balkan countries. To fill the gap, we investigated the viral genetic diversity in Bulgaria, by sequencing and phylogenetic characterization of 86 plasma samples collected between 2002 and 2006 from seropositive individuals diagnosed within 1986–2006. Analysis of polgene sequences assigned 51 of the samples to HIV-1 subtype B and 27 to subtype A1. HIV-1 subtype C, F, G, H, and a few putative recombinant forms were also found. Phylogenetic and molecular clock analysis showed a continuous exchange of subtype A and B between Bulgaria and Western as well as other Eastern European countries. At least three separate introductions of HIV-1 subtype A and four of HIV-1 subtype B have occurred within the past 25 years in Bulgaria. The central geographic location of Bulgaria, the substantial genetic heterogeneity of the epidemic with multiple subtypes, and the significant viral flow observed to and from the Balkan countries have the potential to modify the current HIV-1 epidemiological structure in Europe and highlight the importance of more extensive and continuous monitoring of the epidemic in the Balkans. [ABSTRACT FROM AUTHOR]

Enfuvirtide is the first of a new class of antiretroviral drugs that inhibits HIV entry. It is a 36 amino acid synthetic peptide that mimics the HR2 region of the HIV-1 gp41, preventing the fusion of viral and cellular membranes. Up to now, enfuvirtide was designed based on the HIV-1 B-subtype gp41, and resistance mutations to the fusion inhibitor have been investigated primarily in individuals infected with this subtype. To fill the gap, we analyzed the full length gp41 protein sequence of HIV-1 non-B strains from individuals receiving enfuvirtide-containing regimens. No primary resistance to the enfuvirtide binding domain (36–45 residues) was found. Resistance mutations were detected at follow-up visits and were comparable to those described among B-subtype HIV-1-infected patients; no sequence changes were detected in crucial HR1HR2 gp41 sites such as the cytotoxic T lymphocyte epitope, cysteine loop, ectodomain, and 5-helix interaction and binding region. [ABSTRACT FROM AUTHOR]

Hereditary spastic paraplegias (HSPs) are a group of genetically heterogeneous neurodegenerative conditions. They are characterized by progressive spastic paralysis of the legs as a result of selective, length-dependent degeneration of the axons of the corticospinal tract. Mutations in 3 genes encoding proteins that work together to shape the ER into sheets and tubules - receptor accessory protein 1 (REEP1), atlastin-1 (ATL1), and spastin (SPAST) - have been found to underlie many cases of HSP in Northern Europe and North America. Applying Sanger and exome sequencing, we have now identified 3 mutations in reticulon 2 (RTN2), which encodes a member of the reticulon family of prototypic ER-shaping proteins, in families with spastic paraplegia 12 (SPG12). These autosomal dominant mutations included a complete deletion of RTN2 and a frameshift mutation predicted to produce a highly truncated protein. Wild-type reticulon 2, but not the truncated protein potentially encoded by the frameshift allele, localized to the ER. RTN2 interacted with spastin, and this interaction required a hydrophobic region in spastin that is involved in ER localization and that is predicted to form a curvature-inducing/sensing hairpin loop domain. Our results directly implicate a reticulon protein in axonopathy, show that this protein participates in a network of interactions among HSP proteins involved in ER shaping, and further support the hypothesis that abnormal ER morphogenesis is a pathogenic mechanism in HSP.