Ransomware, a class of self-propagating malware that uses encryption to hold the victims' data ransom, has emerged in recent years as one of the most dangerous cyber threats, with widespread damage; e.g., zero-day ransomware WannaCry has caused world-wide catastrophe, from knocking U.K. National Health Service hospitals offline to shutting down a Honda Motor Company in Japan [1]. Our close collaboration with security operations of large enterprises reveals that defense against ransomware relies on tedious analysis from high-volume systems logs of the first few infections. Sandbox analysis of freshly captured malware is also commonplace in operation.We introduce a method to identify and rank the most discriminating ransomware features from a set of ambient (non-attack) system logs and at least one log stream containing both ambient and ransomware behavior. These ranked features reveal a set of malware actions that are produced automatically from system logs, and can help automate tedious manual analysis. We test our approach using WannaCry and two polymorphic samples by producing logs with Cuckoo Sandbox during both ambient, and ambient plus ransomware executions. Our goal is to extract the features of the malware from the logs with only knowledge that malware was present. We compare outputs with a detailed analysis of WannaCry allowing validation of the algorithm's feature extraction and provide analysis of the method's robustness to variations of input data-changing quality/quantity of ambient data and testing polymorphic ransomware. Most notably, our patterns are accurate and unwavering when generated from polymorphic WannaCry copies, on which 63 (of 63 tested) antivirus (AV) products fail.

Modern vehicles rely on hundreds of on-board electronic control units (ECUs) communicating over in-vehicle networks. As external interfaces to the car control networks (such as the on-board diagnostic (OBD) port, auxiliary media ports, etc.) become common, and vehicle-to-vehicle / vehicle-to-infrastructure technology is in the near future, the a ack surface for vehicles grows, exposing control networks to potentially life-critical a acks. is paper addresses the need for securing the controller area network (CAN) bus by detecting anomalous tra c pa erns via unusual refresh rates of certain commands. While previous works have identi ed signal frequency as an important feature for CAN bus intrusion detection, this paper provides the rst such algorithm with experiments using three a acks in ve (total) scenarios. Our data-driven anomaly detection algorithm requires only ve seconds of training time (on normal data) and achieves true positive / false discovery rates of 0.9998/0.00298, respectively (micro-averaged across the ve experimental tests).

In this paper we describe an ontology developed for a cyber security knowledge graph database. This is intended to provide an organized schema that incorporates information from a large variety of structured and unstructured data sources, and includes all relevant concepts within the domain. We compare the resulting ontology with previous efforts, discuss its strengths and limitations, and describe areas for future work.

This survey focuses on intrusion detection systems (IDS) that leverage host-based data sources for detecting attacks on enterprise network. The host-based IDS (HIDS) literature is organized by the input data source, presenting targeted sub-surveys of HIDS research leveraging system logs, audit data, Windows Registry, file systems, and program analysis. While system calls are generally included in audit data, several publicly available system call datasets have spawned a flurry of IDS research on this topic, which merits a separate section. To accommodate current researchers, a section giving descriptions of publicly available datasets is included, outlining their characteristics and shortcomings when used for IDS evaluation. Related surveys are organized and described. All sections are accompanied by tables concisely organizing the literature and datasets discussed. Finally, challenges, trends, and broader observations are throughout the survey and in the conclusion along with future directions of IDS research. Overall, this survey was designed to allow easy access to the diverse types of data available on a host for sensing intrusion, the progressions of research using each, and the accessible datasets for prototyping in the area.

Mechanistic photosynthesis models are at the heart of terrestrial biosphere models (TBMs) simulating the daily, monthly, annual and decadal rhythms of carbon assimilation (A). These models are founded on robust mathematical hypotheses that describe how A responds to changes in light and atmospheric CO2 concentration. Two predominant photosynthesis models are in common usage: Farquhar (FvCB) and Collatz (CBGB). However, a detailed quantitative comparison of these two models has never been undertaken. In this study, we unify the FvCB and CBGB models to a common parameter set and use novel multi‐hypothesis methods (that account for both hypothesis and parameter variability) for process‐level sensitivity analysis. These models represent three key biological processes: carboxylation, electron transport, triose phosphate use (TPU) and an additional model process: limiting‐rate selection. Each of the four processes comprises 1–3 alternative hypotheses giving 12 possible individual models with a total of 14 parameters. To broaden inference, TBM simulations were run and novel, high‐resolution photosynthesis measurements were made. We show that parameters associated with carboxylation are the most influential parameters but also reveal the surprising and marked dominance of the limiting‐rate selection process (accounting for 57% of the variation in A vs. 22% for carboxylation). The limiting‐rate selection assumption proposed by CBGB smooths the transition between limiting rates and always reduces A below the minimum of all potentially limiting rates, by up to 25%, effectively imposing a fourth limitation on A. Evaluation of the CBGB smoothing function in three TBMs demonstrated a reduction in global A by 4%–10%, equivalent to 50%–160% of current annual fossil fuel emissions. This analysis reveals a surprising and previously unquantified influence of a process that has been integral to many TBMs for decades, highlighting the value of multi‐hypothesis methods.

In 1950, a 12-month-old child was referred to the University of Minnesota Hospitals with a syndrome consisting of chronic suppurative lymphadenitis, hepatosplenomegaly, pulmonary infiltrations, and an eczematoid dermatitis about the eyes, nose, and mouth. Since then we have seen three other children with an almost identical clinical picture. This syndrome appears to be a distinct clinical entity not previously described in the medical literature. This durated matted lymph nodes were still present in the inguinal areas. The liver edge was palpable 2 cm. below the right costal margin. There were no further positive findings in the remainder of the physical examination. The family history was negative. This child had two normal older siblings, one brother and one sister. Except for the present illness the child's past history was not remarkable. On admission he had a hemoglobin of 8.9 gm/100 ml. with a white blood cell count of 17,000 (neutro¬ phils 68, lymphocytes 29, monocytes 2, eosinophils 1 ). A urinalysis was negative. The sedimentation rate was 44 mm. in one hour (Westergren). A blood culture was sterile. Stool cultures were also negative. The total serum protein was 7.6 gm/100 ml. with an albumin of 4.4 gm/100 ml. and a globu¬ lin of 3.2 gm/100 ml. Both a Wassermann and a Kline test were negative. A 1:1,000 tuberculin skin test was negative. On incision and drainage of the

Despite the best efforts of cyber security analysts, networked computing assets are routinely compromised, resulting in the loss of intellectual property, the disclosure of state secrets, and major financial damages. Anomaly detection methods are beneficial for detecting new types of attacks and abnormal network activity, but such algorithms can be difficult to understand and trust. Network operators and cyber analysts need fast and scalable tools to help identify suspicious behavior that bypasses automated security systems, but operators do not want another automated tool with algorithms they do not trust. Experts need tools to augment their own domain expertise and to provide a contextual understanding of suspicious behavior to help them make decisions. In this paper we present Situ, a visual analytics system for discovering suspicious behavior in streaming network data. Situ provides a scalable solution that combines anomaly detection with information visualization. The system's visualizations enable operators to identify and investigate the most anomalous events and IP addresses, and the tool provides context to help operators understand why they are anomalous. Finally, operators need tools that can be integrated into their workflow and with their existing tools. This paper describes the Situ platform and its deployment in an operational network setting. We discuss how operators are currently using the tool in a large organization's security operations center and present the results of expert reviews with professionals.

JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org.. Wiley and Society for the Scientific Study of Religion are collaborating with JSTOR to digitize, preserve and extend access to Journal for the Scientific Study of Religion.With samples of college students, church members, and seminary students, the hypothesis that Batson's measure of quest faith would relate positively to conflict and anxiety was supported. Psychometric problems with the quest measure prompted construction of an alternate form with improved reliability. This resulted in associations with other variables more in line with Batson's original conceptualization. Problems still exist, but questions are raised regarding the meaning of conflict and anxiety in the context of social and religious deviance. Additional research directions are suggested.