A Non-Parametric Learning Approach to Identify Online Human Trafficking

Human trafficking is among the most challenging law enforcement problems which demands persistent fight against from all over the globe. In this study, we leverage readily available data from the website "Backpage"-- used for classified advertisement-- to discern potential patterns of human trafficking activities which manifest online and identify most likely trafficking related advertisements. Due to the lack of ground truth, we rely on two human analysts --one human trafficking victim survivor and one from law enforcement, for hand-labeling the small portion of the crawled data. We then present a semi-supervised learning approach that is trained on the available labeled and unlabeled data and evaluated on unseen data with further verification of experts.Comment: Accepted in IEEE Intelligence and Security Informatics 2016 Conference (ISI 2016

RIPEx: Extracting malicious IP addresses from security forums using cross-forum learning

Is it possible to extract malicious IP addresses reported in security forums in an automatic way? This is the question at the heart of our work. We focus on security forums, where security professionals and hackers share knowledge and information, and often report misbehaving IP addresses. So far, there have only been a few efforts to extract information from such security forums. We propose RIPEx, a systematic approach to identify and label IP addresses in security forums by utilizing a cross-forum learning method. In more detail, the challenge is twofold: (a) identifying IP addresses from other numerical entities, such as software version numbers, and (b) classifying the IP address as benign or malicious. We propose an integrated solution that tackles both these problems. A novelty of our approach is that it does not require training data for each new forum. Our approach does knowledge transfer across forums: we use a classifier from our source forums to identify seed information for training a classifier on the target forum. We evaluate our method using data collected from five security forums with a total of 31K users and 542K posts. First, RIPEx can distinguish IP address from other numeric expressions with 95% precision and above 93% recall on average. Second, RIPEx identifies malicious IP addresses with an average precision of 88% and over 78% recall, using our cross-forum learning. Our work is a first step towards harnessing the wealth of useful information that can be found in security forums.Comment: 12 pages, Accepted in n 22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 201

Conflict Probe Concepts Analysis in Support of Free Flight

This study develops an operational concept and requirements for en route Free Flight using a simulation of the Cleveland Air Route Traffic Control Center, and develops requirements for an automated conflict probe for use in the Air Traffic Control (ATC) Centers. In this paper, we present the results of simulation studies and summarize implementation concepts and infrastructure requirements to transition from the current air traffic control system to mature Free Right. The transition path to Free Flight envisioned in this paper assumes an orderly development of communications, navigation, and surveillance (CNS) technologies based on results from our simulation studies. The main purpose of this study is to provide an overall context and methodology for evaluating airborne and ground-based requirements for cooperative development of the future ATC system

Peritrophic matrix of Phlebotomus duboscqi and its kinetics during Leishmania major development

Light microscopy of native preparations, histology, and electron microscopy have revealed that Phlebotomus duboscqi belongs to a class of sand fly species with prompt development of the peritrophic matrix (PM). Secretion of electron-lucent fibrils, presumably chitin, starts immediately after the ingestion of a blood meal and, about 6 h later, is followed by secretion of amorphous electron-dense components, presumably proteins and glycoproteins. The PM matures in less than 12 h and consists of a thin laminar outer layer and a thick amorphous inner layer. No differences have been found in the timing of the disintegration of the PM in females infected with Leishmania major. In both groups of females (infected and uninfected), the disintegration of the PM is initiated at the posterior end. Although parasites are present at high densities in the anterior part of the blood meal bolus, they escape from the PM at the posterior end only. These results suggest that L. major chitinase does not have an important role in parasite escape from the PM. Promastigotes remain in the intraperitrophic space until the PM is broken down by sand-fly-derived chitinases and only then migrate anteriorly. Disintegration of the PM occurs simultaneously with the morphological transformation of parasites from procyclic forms to long nectomonads. A novel role is ascribed to the anterior plug, a component of the PM secreted by the thoracic midgut; this plug functions as a temporary barrier to stop the forward migration of nectomonads to the thoracic midgut

Leishmania major Survival in Selective Phlebotomus papatasi Sand Fly Vector Requires a Specific SCG-Encoded Lipophosphoglycan Galactosylation Pattern

Phlebotomine sand flies that transmit the protozoan parasite Leishmania differ greatly in their ability to support different parasite species or strains in the laboratory: while some show considerable selectivity, others are more permissive. In “selective” sand flies, Leishmania binding and survival in the fly midgut typically depends upon the abundant promastigote surface adhesin lipophosphoglycan (LPG), which exhibits species- and strain-specific modifications of the dominant phosphoglycan (PG) repeat units. For the “selective” fly Phlebotomus papatasi PpapJ, side chain galactosyl-modifications (scGal) of PG repeats play key roles in parasite binding. We probed the specificity and properties of this scGal-LPG PAMP (Pathogen Associated Molecular Pattern) through studies of natural isolates exhibiting a wide range of galactosylation patterns, and of a panel of isogenic L. major engineered to express similar scGal-LPG diversity by transfection of SCG-encoded β1,3-galactosyltransferases with different activities. Surprisingly, both ‘poly-scGal’ and ‘null-scGal’ lines survived poorly relative to PpapJ-sympatric L. major FV1 and other ‘mono-scGal’ lines. However, survival of all lines was equivalent in P. duboscqi, which naturally transmit L. major strains bearing ‘null-scGal’-LPG PAMPs. We then asked whether scGal-LPG-mediated interactions were sufficient for PpapJ midgut survival by engineering Leishmania donovani, which normally express unsubstituted LPG, to express a ‘PpapJ-optimal’ scGal-LPG PAMP. Unexpectedly, these “L. major FV1-cloaked” L. donovani-SCG lines remained unable to survive within PpapJ flies. These studies establish that midgut survival of L. major in PpapJ flies is exquisitely sensitive to the scGal-LPG PAMP, requiring a specific ‘mono-scGal’ pattern. However, failure of ‘mono-scGal’ L. donovani-SCG lines to survive in selective PpapJ flies suggests a requirement for an additional, as yet unidentified L. major-specific parasite factor(s). The interplay of the LPG PAMP and additional factor(s) with sand fly midgut receptors may determine whether a given sand fly host is “selective” or “permissive”, with important consequences to both disease transmission and the natural co-evolution of sand flies and Leishmania

Approximating Fixation Probabilities in the Generalized Moran Process

We consider the Moran process, as generalized by Lieberman, Hauert and Nowak (Nature, 433:312--316, 2005). A population resides on the vertices of a finite, connected, undirected graph and, at each time step, an individual is chosen at random with probability proportional to its assigned 'fitness' value. It reproduces, placing a copy of itself on a neighbouring vertex chosen uniformly at random, replacing the individual that was there. The initial population consists of a single mutant of fitness $r>0$ placed uniformly at random, with every other vertex occupied by an individual of fitness 1. The main quantities of interest are the probabilities that the descendants of the initial mutant come to occupy the whole graph (fixation) and that they die out (extinction); almost surely, these are the only possibilities. In general, exact computation of these quantities by standard Markov chain techniques requires solving a system of linear equations of size exponential in the order of the graph so is not feasible. We show that, with high probability, the number of steps needed to reach fixation or extinction is bounded by a polynomial in the number of vertices in the graph. This bound allows us to construct fully polynomial randomized approximation schemes (FPRAS) for the probability of fixation (when $r\geq 1$) and of extinction (for all $r>0$).Comment: updated to the final version, which appeared in Algorithmic

Similar pathogen targets in Arabidopsis thaliana and homo sapiens protein networks.

We study the behavior of pathogens on host protein networks for humans and Arabidopsis - noting striking similarities. Specifically, we preform [Formula: see text]-shell decomposition analysis on these networks - which groups the proteins into various "shells" based on network structure. We observe that shells with a higher average degree are more highly targeted (with a power-law relationship) and that highly targeted nodes lie in shells closer to the inner-core of the network. Additionally, we also note that the inner core of the network is significantly under-targeted. We show that these core proteins may have a role in intra-cellular communication and hypothesize that they are less attacked to ensure survival of the host. This may explain why certain high-degree proteins are not significantly attacked

Minimum number of pathogen interactions for a given node vs. fraction of EHF neighbors for that node.

<p>Minimum number of pathogen interactions for a given node vs. fraction of EHF neighbors for that node.</p