Biblio

Today's cyber-physical systems (CPSs) can have very different characteristics in terms of control algorithms, configurations, underlying infrastructure, communication protocols, and real-time requirements. Despite these variations, they all face the threat of malicious attacks that exploit the vulnerabilities in the cyber domain as footholds to introduce safety violations in the physical processes. In this paper, we focus on a class of attacks that impact the physical processes without introducing anomalies in the cyber domain. We present the common challenges in detecting this type of attacks in the contexts of two very different CPSs (i.e., power grids and surgical robots). In addition, we present a general principle for detecting such cyber-physical attacks, which combine the knowledge of both cyber and physical domains to estimate the adverse consequences of malicious activities in a timely manner.

Spear-phishing attacks pose a serious threat to sensitive computer systems, since they sidestep technical security mechanisms by exploiting the carelessness of authorized users. A common way to mitigate such attacks is to use e-mail filters which block e-mails with a maliciousness score above a chosen threshold. Optimal choice of such a threshold involves a tradeoff between the risk from delivered malicious emails and the cost of blocking benign traffic. A further complicating factor is the strategic nature of an attacker, who may selectively target users offering the best value in terms of likelihood of success and resulting access privileges. Previous work on strategic threshold-selection considered a single organization choosing thresholds for all users. In reality, many organizations are potential targets of such attacks, and their incentives need not be well aligned. We therefore consider the problem of strategic threshold-selection by a collection of independent self-interested users. We characterize both Stackelberg multi-defender equilibria, corresponding to short-term strategic dynamics, as well as Nash equilibria of the simultaneous game between all users and the attacker, modeling long-term dynamics, and exhibit a polynomial-time algorithm for computing short-term (Stackelberg) equilibria. We find that while Stackelberg multi-defender equilibrium need not exist, Nash equilibrium always exists, and remarkably, both equilibria are unique and socially optimal.

This paper presents a Factor Graph based framework called AttackTagger for highly accurate and preemptive detection of attacks, i.e., before the system misuse. We use secu- rity logs on real incidents that occurred over a six-year pe- riod at the National Center for Supercomputing Applica- tions (NCSA) to evaluate AttackTagger. Our data consist of security incidents that led to compromise of the target system, i.e., the attacks in the incidents were only identified after the fact by security analysts. AttackTagger detected 74 percent of attacks, and the majority them were detected before the system misuse. Finally, AttackTagger uncovered six hidden attacks that were not detected by intrusion de- tection systems during the incidents or by security analysts in post-incident forensic analysis.

As mobile technology begins to dominate computing, understanding how their use impacts security becomes increasingly important. Fortunately, this challenge is also an opportunity: the rich set of sensors with which most mobile devices are equipped provide a rich contextual dataset, one that should enable mobile user behavior to be modeled well enough to predict when users are likely to act insecurely, and provide cognitively grounded explanations of those behaviors. We will evaluate this hypothesis with a series of experiments designed first to confirm that mobile sensor data can reliably predict user stress, and that users experiencing such stress are more likely to act insecurely.

Detecting and preventing attacks before they compromise a system can be done using acceptance testing, redundancy based mechanisms, and using external consistency checking such external monitoring and watchdog processes. Diversity-based adjudication, is a step towards an oracle that uses knowable behavior of a healthy system. That approach, under best circumstances, is able to detect even zero-day attacks. In this approach we use functionally equivalent but in some way diverse components and we compare their output vectors and reactions for a given input vector. This paper discusses practical relevance of this approach in the context of recent web-service attacks.

This paper presents a system named SPOT to achieve high accuracy and preemptive detection of attacks. We use security logs of real-incidents that occurred over a six-year period at National Center for Supercomputing Applications (NCSA) to evaluate SPOT. Our data consists of attacks that led directly to the target system being compromised, i.e., not detected in advance, either by the security analysts or by intrusion detection systems. Our approach can detect 75 percent of attacks as early as minutes to tens of hours before attack payloads are executed.

The InViz tool is a functional prototype that provides graphical visualizations of log file events to support real-time attack investigation. Through visualization, both experts and novices in cybersecurity can analyze patterns of application behavior and investigate potential cybersecurity attacks. The goal of this research is to identify and evaluate the cybersecurity information to visualize that reduces the amount of time required to perform cyber forensics.

While automated methods are the first line of defense for detecting attacks on webservers, a human agent is required to understand the attacker's intent and the attack process. The goal of this research is to understand the value of various log fields and the cognitive processes by which log information is grouped, searched, and correlated. Such knowledge will enable the development of human-focused log file investigation technologies. We performed controlled experiments with 65 subjects (IT professionals and novices) who investigated excerpts from six webserver log files. Quantitative and qualitative data were gathered to: 1) analyze subject accuracy in identifying malicious activity; 2) identify the most useful pieces of log file information; and 3) understand the techniques and strategies used by subjects to process the information. Statistically significant effects were observed in the accuracy of identifying attacks and time taken depending on the type of attack. Systematic differences were also observed in the log fields used by high-performing and low-performing groups. The findings include: 1) new insights into how specific log data fields are used to effectively assess potentially malicious activity; 2) obfuscating factors in log data from a human cognitive perspective; and 3) practical implications for tools to support log file investigations.

One hundred-sixty four participants from the United States, India and China completed a survey designed to assess past phishing experiences and whether they engaged in certain online safety practices (e.g., reading a privacy policy). The study investigated participants' reported agreement regarding the characteristics of phishing attacks, types of media where phishing occurs and the consequences of phishing. A multivariate analysis of covariance indicated that there were significant differences in agreement regarding phishing characteristics, phishing consequences and types of media where phishing occurs for these three nationalities. Chronological age and education did not influence the agreement ratings; therefore, the samples were demographically equivalent with regards to these variables. A logistic regression analysis was conducted to analyze the categorical variables and nationality data. Results based on self-report data indicated that (1) Indians were more likely to be phished than Americans, (2) Americans took protective actions more frequently than Indians by destroying old documents, and (3) Americans were more likely to notice the "padlock" security icon than either Indian or Chinese respondents. The potential implications of these results are discussed in terms of designing culturally sensitive anti-phishing solutions.