AIS for Malware Detection in a Realistic IoT System: Challenges and Opportunities

With the expansion of the digital world, the number of Internet of things (IoT) devices is evolving dramatically. IoT devices have limited computational power and a small memory. Consequently, existing and complex security methods are not suitable to detect unknown malware attacks in IoT networks. This has become a major concern in the advent of increasingly unpredictable and innovative cyberattacks. In this context, artificial immune systems (AISs) have emerged as an effective malware detection mechanism with low requirements for computation and memory. In this research, we first validate the malware detection results of a recent AIS solution using multiple datasets with different types of malware attacks. Next, we examine the potential gains and limitations of promising AIS solutions under realistic implementation scenarios. We design a realistic IoT framework mimicking real-life IoT system architectures. The objective is to evaluate the AIS solutions’ performance with regard to the system constraints. We demonstrate that AIS solutions succeed in detecting unknown malware in the most challenging conditions. Furthermore, the systemic results with different system architectures reveal the AIS solutions’ ability to transfer learning between IoT devices. Transfer learning is a pivotal feature in the presence of highly constrained devices in the network. More importantly, this work highlights that previously published AIS performance results, which were obtained in a simulation environment, cannot be taken at face value. In reality, AIS’s malware detection accuracy for IoT systems is 91% in the most restricted designed system compared to the 99% accuracy rate reported in the simulation experiment

The role and uses of antibodies in COVID-19 infections: a living review

Coronavirus disease 2019 has generated a rapidly evolving field of research, with the global scientific community striving for solutions to the current pandemic. Characterizing humoral responses towards SARS-CoV-2, as well as closely related strains, will help determine whether antibodies are central to infection control, and aid the design of therapeutics and vaccine candidates. This review outlines the major aspects of SARS-CoV-2-specific antibody research to date, with a focus on the various prophylactic and therapeutic uses of antibodies to alleviate disease in addition to the potential of cross-reactive therapies and the implications of long-term immunity

T cell phenotypes in COVID-19 - a living review

COVID-19 is characterized by profound lymphopenia in the peripheral blood, and the remaining T cells display altered phenotypes, characterized by a spectrum of activation and exhaustion. However, antigen-specific T cell responses are emerging as a crucial mechanism for both clearance of the virus and as the most likely route to long-lasting immune memory that would protect against re-infection. Therefore, T cell responses are also of considerable interest in vaccine development. Furthermore, persistent alterations in T cell subset composition and function post-infection have important implications for patients’ long-term immune function. In this review, we examine T cell phenotypes, including those of innate T cells, in both peripheral blood and lungs, and consider how key markers of activation and exhaustion correlate with, and may be able to predict, disease severity. We focus on SARS-CoV-2-specific T cells to elucidate markers that may indicate formation of antigen-specific T cell memory. We also examine peripheral T cell phenotypes in recovery and the likelihood of long-lasting immune disruption. Finally, we discuss T cell phenotypes in the lung as important drivers of both virus clearance and tissue damage. As our knowledge of the adaptive immune response to COVID-19 rapidly evolves, it has become clear that while some areas of the T cell response have been investigated in some detail, others, such as the T cell response in children remain largely unexplored. Therefore, this review will also highlight areas where T cell phenotypes require urgent characterisation

Artificial Immune Systems for Detecting Unknown Malware in the IoT

With the expansion of the digital world, the number of the Internet of Things (IoT) devices is evolving dramatically. IoT devices have limited computational power and small memory. Also, they are not part of traditional computer networks. Consequently, existing and often complex security methods are unsuitable for malware detection in IoT networks. This has become a significant concern in the advent of increasingly unpredictable and innovative cyber-attacks. In this context, artificial immune systems (AIS) have emerged as effective IoT malware detection mechanisms with low computational requirements. In this research, we present a critical analysis to highlight the limitations of the AIS state-of-the-art solutions and identify promising research directions. Next, we propose Negative-Positive-Selection (NPS) method, which is an AIS-based for malware detection. The NPS is suitable for IoT's computation restrictions and security challenges. The NPS performance is benchmarked against the state-of-the-art using multiple real-time datasets. The simulation results show a 21% improvement in malware detection and a 65% reduction in the number of detectors. Then, we examine AIS solutions' potential gains and limitations under realistic implementation scenarios. We design a framework to mimic real-life IoT systems. The objective is to evaluate the method's lightweight, fault tolerance, and detection performance with regard to the system constraints. We demonstrate that AIS solutions successfully detect unknown malware in the most challenging IoT environment in terms of memory capacity and processing power. Furthermore, the systemic results with different system architectures reveal the AIS solutions' ability to transfer learning between IoT devices. Transfer learning is a critical feature in the presence of highly constrained devices in the network. More importantly, we highlight that the simulation environment cannot be taken at face value. In reality, AIS malware detection accuracy for IoT systems is likely to be close to 10% worse than simulation results, as indicated by the study results

A novel negative and positive selection algorithm to detect unknown malware in the IoT

The Internet of Things (IoT) paradigm is a key enabler to many critical applications, thus demands reliable security measures. IoT devices have limited computational power, hence, are inadequate to carry rigorous security mechanisms. This paper proposes the Negative-Positive-Selection (NPS) method which uses an artificial immunity system technique for malware detection. NPS is suitable for the computation restrictions and security challenges associated with IoT. The performance of NPS is benchmarked against state-of-the-art malware detection schemes using a real dataset. Our results show a 21% improvement in malware detection and a 65% reduction in the number of detectors. NPS meets IoT-specific requirements as it outperforms other malware detection mechanisms whilst having less demanding computational requirements

Challenges of malware detection in the IoT and a review of artificial immune system approaches

The fast growth of the Internet of Things (IoT) and its diverse applications increase the risk of cyberattacks, one type of which is malware attacks. Due to the IoT devices’ different capabilities and the dynamic and ever-evolving environment, applying complex security measures is challenging, and applying only basic security standards is risky. Artificial Immune Systems (AIS) are intrusion-detecting algorithms inspired by the human body’s adaptive immune system techniques. Most of these algorithms imitate the human’s body B-cell and T-cell defensive mechanisms. They are lightweight, adaptive, and able to detect malware attacks without prior knowledge. In this work, we review the recent advances in employing AIS for the improved detection of malware in IoT networks. We present a critical analysis that highlights the limitations of the state-of-the-art in AIS research and offer insights into promising new research directions

Correction: RV144 HIV-1 vaccination impacts post-infection antibody responses.

[This corrects the article DOI: 10.1371/journal.ppat.1009101.]

RV144 HIV-1 vaccination impacts post-infection antibody responses.

The RV144 vaccine efficacy clinical trial showed a reduction in HIV-1 infections by 31%. Vaccine efficacy was associated with stronger binding antibody responses to the HIV Envelope (Env) V1V2 region, with decreased efficacy as responses wane. High levels of Ab-dependent cellular cytotoxicity (ADCC) together with low plasma levels of Env-specific IgA also correlated with decreased infection risk. We investigated whether B cell priming from RV144 vaccination impacted functional antibody responses to HIV-1 following infection. Antibody responses were assessed in 37 vaccine and 63 placebo recipients at 6, 12, and 36 months following HIV diagnosis. The magnitude, specificity, dynamics, subclass recognition and distribution of the binding antibody response following infection were different in RV144 vaccine recipients compared to placebo recipients. Vaccine recipients demonstrated increased IgG1 binding specifically to V1V2, as well as increased IgG2 and IgG4 but decreased IgG3 to HIV-1 Env. No difference in IgA binding to HIV-1 Env was detected between the vaccine and placebo recipients following infection. RV144 vaccination limited the development of broadly neutralizing antibodies post-infection, but enhanced Fc-mediated effector functions indicating B cell priming by RV144 vaccination impacted downstream antibody function. However, these functional responses were not associated with clinical markers of disease progression. These data reveal that RV144 vaccination primed B cells towards specific binding and functional antibody responses following HIV-1 infection