In this paper, we present Anagram, a content anomaly detector that models a mixture of high-order n-grams (n > 1) designed to detect anomalous and “suspicious” network packet payloads. By using higher-order n-grams, Anagram can detect significant anomalous byte sequences and generate robust signatures of validated malicious packet content. The Anagram content models are implemented using highly...

Six previous studies of the variables affecting anagram solution are reexamined for the evidence that number of syllables contributes to solution difficulty. It was shown that the number of syllables in a solution word was confounded with imagery for one study and with digram frequency for another. More importantly it was shown that the number of syllables has a large effect on anagram solution...

In this paper, we present Anagram, a content anomaly detector that models a mixture of high-order n-grams (n > 1) designed to detect anomalous and “suspicious” network packet payloads. By using higher-order n-grams, Anagram can detect significant anomalous byte sequences and generate robust signatures of validated malicious packet content. The Anagram content models are implemented using highly...

When compared to signature-based Intrusion Detection Systems (IDS), anomaly detectors present the potential advantage of detecting previously unseen attacks, which makes them an attractive solution against zero-day exploits and other attacks for which a signature is unavailable. Most anomaly detectors rely on machine learning algorithms to derive a model of normality that is later used to detec...

Eliminating the controllability of a noxious stimulus may induce a learned helplessness (LH) that resembles aspects of depression and post-traumatic stress disorder (PTSD). This study examined whether repetitive transcranial magnetic stimulation (rTMS) of the left dorsolateral prefrontal cortex (DLPFC) promotes resilience in an aversive stimulus model of LH. All 55 participants were told that a...