Tiresias: Predicting Security Events Through Deep Learning

With the increased complexity of modern computer attacks, there is a need for defenders not only to detect malicious activity as it happens, but also to predict the specific steps that will be taken by an adversary when performing an attack. However this is still an open research problem, and previous research in predicting malicious events only looked at binary outcomes (e.g., whether an attack would happen or not), but not at the specific steps that an attacker would undertake. To fill this gap we present Tiresias, a system that leverages Recurrent Neural Networks (RNNs) to predict future events on a machine, based on previous observations. We test Tiresias on a dataset of 3.4 billion security events collected from a commercial intrusion prevention system, and show that our approach is effective in predicting the next event that will occur on a machine with a precision of up to 0.93. We also show that the models learned by Tiresias are reasonably stable over time, and provide a mechanism that can identify sudden drops in precision and trigger a retraining of the system. Finally, we show that the long-term memory typical of RNNs is key in performing event prediction, rendering simpler methods not up to the task

Individual correlates of podoconiosis in areas of varying endemicity: a case-control study

BACKGROUND Podoconiosis is a non-filarial form of elephantiasis resulting in lymphedema of the lower legs. Previous studies have suggested that podoconiosis arises from the interplay of individual and environmental factors. Here, our aim was to understand the individual-level correlates of podoconiosis by comparing 460 podoconiosis-affected individuals and 707 unaffected controls. METHODS/PRINCIPAL FINDINGS This was a case-control study carried out in six kebeles (the lowest governmental administrative unit) in northern Ethiopia. Each kebele was classified into one of three endemicity levels: 'low' (prevalence 5%). A total of 142 (30.7%) households had two or more cases of podoconiosis. Compared to controls, the majority of the cases, especially women, were less educated (OR = 1.7, 95% CI = 1.3 to 2.2), were unmarried (OR = 3.4, 95% CI = 2.6-4.6) and had lower income (t = -4.4, p<0.0001). On average, cases started wearing shoes ten years later than controls. Among cases, age of first wearing shoes was positively correlated with age of onset of podoconiosis (r = 0.6, t = 12.5, p<0.0001). Among all study participants average duration of shoe wearing was less than 30 years. Between both cases and controls, people in 'high' and 'medium' endemicity kebeles were less likely than people in 'low' endemicity areas to 'ever' have owned shoes (OR = 0.5, 95% CI = 0.4-0.7). CONCLUSIONS Late use of shoes, usually after the onset of podoconiosis, and inequalities in education, income and marriage were found among cases, particularly among females. There were clustering of cases within households, thus interventions against podoconiosis will benefit from household-targeted case tracing. Most importantly, we identified a secular increase in shoe-wearing over recent years, which may give opportunities to promote shoe-wearing without increasing stigma among those at high risk of podoconiosis

Circulating endothelial cell-derived extracellular vesicles mediate the acute phase response and sickness behaviour associated with CNS inflammation.

Brain injury elicits a systemic acute-phase response (APR), which is responsible for co-ordinating the peripheral immunological response to injury. To date, the mechanisms responsible for signalling the presence of injury or disease to selectively activate responses in distant organs were unclear. Circulating endogenous extracellular vesicles (EVs) are increased after brain injury and have the potential to carry targeted injury signals around the body. Here, we examined the potential of EVs, isolated from rats after focal inflammatory brain lesions using IL-1β, to activate a systemic APR in recipient naïve rats, as well as the behavioural consequences of EV transfer. Focal brain lesions increased EV release, and, following isolation and transfer, the EVs were sequestered by the liver where they initiated an APR. Transfer of blood-borne EVs from brain-injured animals was also enough to suppress exploratory behaviours in recipient naïve animals. EVs derived from brain endothelial cell cultures treated with IL-1β also activated an APR and altered behaviour in recipient animals. These experiments reveal that inflammation-induced circulating EVs derived from endothelial cells are able to initiate the APR to brain injury and are sufficient to generate the associated sickness behaviours, and are the first demonstration that EVs are capable of modifying behavioural responses

What was Progressive in ‘Progressive Conservatism’?

In January 2009 David Cameron announced that the ‘underlying philosophy’ of his government would be progressive conservatism. Despite the ambiguity about this term, it was generally interpreted as a signal that Cameron was moving his party to the left.To some commentators, Cameron was allying with the progressive ‘one nation’ strand of conservative thought.To others, particularly in the media, he was drawing on the more immediate influence of Phillip Blond’s ‘Red Toryism’. However, the focus on the market (as opposed to state or community) found in both Cameron’s speech and subsequent policies sits uneasily with both of these interpretations. Cameron’s progressive conservatism has more in common with Thatcherism – an earlier conservative modernising project – than it does with centrist forms of conservative progressivism. Cameron’s progressive conservatism is progressive, but only in particular, less commonly used, ways – not as a rediscovery of social justice

Tiresias: Predicting Security Events Through Deep Learning

With the increased complexity of modern computer attacks, there is a need for defenders not only to detect malicious activity as it happens, but also to predict the specific steps that will be taken by an adversary when performing an attack. However this is still an open research problem, and previous research in predicting malicious events only looked at binary outcomes (eg. whether an attack would happen or not), but not at the specific steps that an attacker would undertake. To fill this gap we present Tiresias xspace, a system that leverages Recurrent Neural Networks (RNNs) to predict future events on a machine, based on previous observations. We test Tiresias xspace on a dataset of 3.4 billion security events collected from a commercial intrusion prevention system, and show that our approach is effective in predicting the next event that will occur on a machine with a precision of up to 0.93. We also show that the models learned by Tiresias xspace are reasonably stable over time, and provide a mechanism that can identify sudden drops in precision and trigger a retraining of the system. Finally, we show that the long-term memory typical of RNNs is key in performing event prediction, rendering simpler methods not up to the task