Langerhans cells (LCs) constitute a subset of DCs that initiate immune responses in skin.Using leprosy as a model, we investigated whether expression of CD1a and langerin, an LC-specific C-type lectin, imparts a specific functional role to LCs. LC-like DCs and freshly isolated epidermal LCs presented nonpeptide antigens of Mycobacterium leprae to T cell clones derived from a leprosy patient in a CD1a-restricted and langerin-dependent manner. LC-like DCs were more efficient at CD1a-restricted antigen presentation than monocyte-derived DCs. LCs in leprosy lesions coexpress CD1a and langerin, placing LCs in position to efficiently present a subset of antigens to T cells as part of the host response to human infectious disease.

Abstract. The latency gap between caches and main memory has been successfully exploited for recovering sensitive input to programs, such as cryptographic keys from implementation of AES and RSA. So far, there are no practical general-purpose countermeasures against this threat. In this paper we propose a novel method for automatically deriving upper bounds on the amount of information about the input that an adversary can extract from a program by observing the CPU's cache behavior. At the heart of our approach is a novel technique for efficient counting of concretizations of abstract cache states that enables us to connect state-of-the-art techniques for static cache analysis and quantitative information-flow. We implement our counting procedure on top of the AbsInt TimingExplorer, one of the most advanced engines for static cache analysis. We use our tool to perform a case study where we derive upper bounds on the cache leakage of a 128-bit AES executable on an ARM processor with a realistic cache configuration. We also analyze this implementation with a commonly suggested (but until now heuristic) countermeasure applied, obtaining a formal account of the corresponding increase in security.

Granulomas are complex cellular structures comprised predominantly of macrophages and lymphocytes that function to contain and kill invading pathogens. Here, we investigated single cell phenotypes associated with antimicrobial responses in human leprosy granulomas by applying single cell and spatial sequencing to leprosy biopsy specimens. We focused on reversal reactions (RR), a dynamic process in which some patients with disseminated lepromatous leprosy (L-lep) transition towards self-limiting tuberculoid leprosy (T-lep), mounting effective antimicrobial responses. We identified a set of genes encoding proteins involved in antimicrobial responses that are differentially expressed in RR versus L-lep lesions, and regulated by IFN-γ and IL-1β. By integrating the spatial coordinates of the key cell types and antimicrobial gene expression in RR and T-lep lesions, we constructed a map revealing the organized architecture of granulomas depicting compositional and functional layers by which macrophages, T cells, keratinocytes and fibroblasts can each contribute to the antimicrobial response.Nat Immunol.

Machine-learning based intrusion detection classifiers are able to detect unknown attacks, but at the same time they may be susceptible to evasion by obfuscation techniques. An adversary intruder which possesses a crucial knowledge about a protection system can easily bypass the detection module. The main objective of our work is to improve the performance capabilities of intrusion detection classifiers against such adversaries. To this end, we firstly propose several obfuscation techniques of remote attacks that are based on the modification of various properties of network connections; then we conduct a set of comprehensive experiments to evaluate the effectiveness of intrusion detection classifiers against obfuscated attacks. We instantiate our approach by means of a tool, based on NetEm and Metasploit, which implements our obfuscation operators on any TCP communication. This allows us to generate modified network traffic for machine learning experiments employing features for assessing network statistics and behavior of TCP connections. We perform evaluation on five classifiers: Gaussian Naïve Bayes, Gaussian Naïve Bayes with kernel density estimation, Logistic Regression, Decision Tree, and Support Vector Machines. Our experiments confirm the assumption that it is possible to evade the intrusion detection capability of all classifiers trained without prior knowledge about obfuscated attacks, causing an exacerbation of the TPR ranging from 7.8% to 66.8%. Further, when widening the training knowledge of the classifiers by a subset of obfuscated attacks, we achieve a significant improvement of the TPR by 4.21% -73.3%, while the FPR is deteriorated only slightly (0.1% -1.48%). Finally, we test the capability of an obfuscations-aware classifier to detect unknown obfuscated attacks, where we achieve over 90% detection rate on average for most of the obfuscations. intrusion detection suffers from undetected attacks such as zero-day attacks or polymorphism, enabling an exploit-code to avoid positive signature matching of the packet payload data. Therefore, researchers and developers are motivated to design new methods to detect various versions of the modified network attacks including the zero-day ones. These goals motivate the popularity of Anomaly Detection Systems (ADS) and also the classification approaches in the context 1 EAI Endorsed Transactions Preprint

Concolic (concrete+symbolic) execution has recently gained popularity as an e↵ective means to uncover non-trivial vulnerabilities in software, such as subtle bu↵er overflows. However, symbolic execution tools that are designed to optimize statement coverage often fail to cover potentially vulnerable code because of complex system interactions and scalability issues of constraint solvers. In this paper, we present a tool (MACKE) that is based on the modular interactions inferred by static code analysis, which is combined with symbolic execution and directed inter-procedural path exploration. This provides an advantage in terms of statement coverage and ability to uncover more vulnerabilities. Our tool includes a novel feature in the form of interactive vulnerability report generation that helps developers prioritize bug fixing based on severity scores. A demo of our tool is available at https://youtu.be/icC3jc3mHEU.

Background Mycobacterium leprae was thought to be the exclusive causative agent of leprosy until Mycobacterium lepromatosis was identified in a rare form of leprosy known as diffuse lepromatous leprosy (DLL). Methods We isolated M. lepromatosis from a patient with DLL and propagated it in athymic nude mouse footpads. Genomic analysis of this strain (NHDP-385) identified a unique repetitive element, RLPM, on which a specific real-time quantitative polymerase chain reaction assay was developed. The RLPM assay, and a previously developed RLEP quantitative polymerase chain reaction assay for M. leprae, were validated as clinical diagnostic assays according to Clinical Laboratory Improvement Amendments guidelines. We tested DNA from archived histological sections, patient specimens from the United States, Philippines, and Mexico, and US wild armadillos. Results The limit of detection for the RLEP and RLPM assays is 30 M. leprae per specimen (0.76 bacilli per reaction; coefficient of variation, 0.65%–2.44%) and 122 M. lepromatosis per specimen (3.05 bacilli per reaction; 0.84%–2.9%), respectively. In histological sections (n = 10), 1 lepromatous leprosy (LL), 1 DLL, and 3 Lucio reactions contained M. lepromatosis; 2 LL and 2 Lucio reactions contained M. leprae; and 1 LL reaction contained both species. M. lepromatosis was detected in 3 of 218 US biopsy specimens (1.38%). All Philippines specimens (n = 180) were M. lepromatosis negative and M. leprae positive. Conversely, 15 of 47 Mexican specimens (31.91%) were positive for M. lepromatosis, 19 of 47 (40.43%) were positive for M. leprae, and 2 of 47 (4.26%) contained both organisms. All armadillos were M. lepromatosis negative. Conclusions The RLPM and RLEP assays will aid healthcare providers in the clinical diagnosis and surveillance of leprosy.

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In this work we apply machine learning algorithms on network traffic data for accurate identification of IoT devices connected to a network. To train and evaluate the classifier, we collected and labeled network traffic data from nine distinct IoT devices, and PCs and smartphones. Using supervised learning, we trained a multi-stage meta classifier; in the first stage, the classifier can distinguish between traffic generated by IoT and non-IoT devices. In the second stage, each IoT device is associated a specific IoT device class. The overall IoT classification accuracy of our model is 99.281%. CCS Concepts •Security and privacy → Mobile and wireless security; •Computing methodologies → Machine learning;