Biblio

This talk will explore a scalable data analytics pipeline for real-time attack detection through the use of customized honeypots at the National Center for Supercomputing Applications (NCSA). Attack detection tools are common and are constantly improving, but validating these tools is challenging. You must: (i) identify data (e.g., system-level events) that is essential for detecting attacks, (ii) extract this data from multiple data logs collected by runtime monitors, and (iii) present the data to the attack detection tools. On top of this, such an approach must scale with an ever-increasing amount of data, while allowing integration of new monitors and attack detection tools. All of these require an infrastructure to host and validate the developed tools before deployment into a production environment.

We will present a generalized architecture that aims for a real-time, scalable, and extensible pipeline that can be deployed in diverse infrastructures to validate arbitrary attack detection tools. To motivate our approach, we will show an example deployment of our pipeline based on open-sourced tools. The example deployment uses as its data sources: (i) a customized honeypot environment at NCSA and (ii) a container-based testbed infrastructure for interactive attack replay. Each of these data sources is equipped with network and host-based monitoring tools such as Bro (a network-based intrusion detection system) and OSSEC (a host-based intrusion detection system) to allow for the runtime collection of data on system/user behavior. Finally, we will present an attack detection tool that we developed and that we look to validate through our pipeline. In conclusion, the talk will discuss the challenges of transitioning attack detection from theory to practice and how the proposed data analytics pipeline can help that transition.

This paper presents the architecture of an end-to-end secu- rity testbed and security analytics framework, which aims to: i) understand real-world exploitation of known security vulnerabilities and ii) preemptively detect multi-stage at- tacks, i.e., before the system misuse. With the increasing number of security vulnerabilities, it is necessary for secu- rity researchers and practitioners to understand: i) system and network behaviors under attacks and ii) potential ef- fects of attacks to the target infrastructure. To safely em- ulate and instrument exploits of known vulnerabilities, we use virtualization techniques to isolate attacks in contain- ers, e.g., Linux-based containers or Virtual Machines, and to deploy monitors, e.g., kernel probes or network packet captures, across a system and network stack. To infer the evolution of attack stages from monitoring data, we use a probabilistic graphical model, namely AttackTagger, that represents learned knowledge of simulated attacks in our se- curity testbed and real-world attacks. Experiments are be- ing run on a real-world deployment of the framework at the National Center for Supercomputing Applications (NCSA) at the University of Illinois at Urbana-Champaign.

Norms are a promising basis for governance in secure, collaborative environments---systems in which multiple principals interact. Yet, many aspects of norm-governance remain poorly understood, inhibiting adoption in real-life collaborative systems. This work focuses on the combined effects of sanction and the observability of the sanctioner in a secure, collaborative environment.  We present CARLOS, a multiagent simulation of graduate students performing research within a university lab setting, to explore these phenomena. The simulation consists of agents maintaining ``compliance" to enforced security norms while remaining ``motivated" as researchers. We hypothesize that (1) delayed observability of the environment would lead to greater motivation of agents to complete research tasks than immediate observability and (2) sanctioning a group for a violation would lead to greater compliance to security norms than sanctioning an individual. We find that only the latter hypothesis is supported.  Group sanction is an interesting topic for future research regarding a means for norm-governance which yields significant compliance with enforced security policy at a lower cost. Our ultimate contribution is to apply social simulation as a way to explore environmental properties and policies to evaluate key transitions in outcome, as a basis for guiding further and more demanding empirical research.

During impact analysis on object-oriented code, statically extracting dependencies is often complicated by subclassing, programming to interfaces, aliasing, and collections, among others. When a tool recommends a large number of types or does not rank its recommendations, it may lead developers to explore more irrelevant code. We propose to mine and rank dependencies based on a global, hierarchical points-to graph that is extracted using abstract interpretation. A previous whole-program static analysis interprets a program enriched with annotations that express hierarchy, and over-approximates all the objects that may be created at runtime and how they may communicate. In this paper, an analysis mines the hierarchy and the edges in the graph to extract and rank dependencies such as the most important classes related to a class, or the most important classes behind an interface. An evaluation using two case studies on two systems totaling 10,000 lines of code and five completed code modification tasks shows that following dependencies based on abstract interpretation achieves higher effectiveness compared to following dependencies extracted from the abstract syntax tree. As a result, developers explore less irrelevant code.

Network reconnaissance of IP addresses and ports is prerequisite to many host and network attacks. Meanwhile, static configurations of networks and hosts simplify this adversarial reconnaissance. In this paper, we present a novel proactive-adaptive defense technique that turns end-hosts into untraceable moving targets, and establishes dynamics into static systems by monitoring the adversarial behavior and reconfiguring the addresses of network hosts adaptively. This adaptability is achieved by discovering hazardous network ranges and addresses and evacuating network hosts from them quickly. Our approach maximizes adaptability by (1) using fast and accurate hypothesis testing for characterization of adversarial behavior, and (2) achieving a very fast IP randomization (i.e., update) rate through separating randomization from end-hosts and managing it via network appliances. The architecture and protocols of our approach can be transparently deployed on legacy networks, as well as software-defined networks. Our extensive analysis and evaluation show that by adaptive distortion of adversarial reconnaissance, our approach slows down the attack and increases its detectability, thus significantly raising the bar against stealthy scanning, major classes of evasive scanning and worm propagation, as well as targeted (hacking) attacks.
 

The ever increasing expansion of mobile applications into nearly every aspect of modern life, from banking to healthcare systems, is making their security more important than ever. Modern smartphone operating systems (OS) rely substantially on the permission-based security model to enforce restrictions on the operations that each application can perform. In this paper, we perform an analysis of the permission protocol implemented in Android, a popular OS for smartphones. We propose a formal model of the Android permission protocol in Alloy, and describe a fully automatic analysis that identifies potential flaws in the protocol. A study of real-world Android applications corroborates our finding that the flaws in the Android permission protocol can have severe security implications, in some cases allowing the attacker to bypass the permission checks entirely.

A fundamental drawback of current anomaly detection systems (ADSs) is the ability of a skilled attacker to evade detection. This is due to the flawed assumption that an attacker does not have any information about an ADS. Advanced persistent threats that are capable of monitoring network behavior can always estimate some information about ADSs which makes these ADSs susceptible to evasion attacks. Hence in this paper, we first assume the role of an attacker to launch evasion attacks on anomaly detection systems. We show that the ADSs can be completely paralyzed by parameter estimation attacks. We then present a mathematical model to measure evasion margin with the aim to understand the science of evasion due to ADS design. Finally, to minimize the evasion margin, we propose a key-based randomization scheme for existing ADSs and discuss its robustness against evasion attacks. Case studies are presented to illustrate the design methodology and extensive experimentation is performed to corroborate the results.
 

The need of cyber security is increasing as cyber attacks are escalating day by day. Cyber attacks are now so many and sophisticated that many will unavoidably get through. Therefore, there is an immense need to employ resilient architectures to defend known or unknown threats. Engineer- ing resilient system/infrastructure is a challenging task, that implies how to measure the resilience and how to obtain sufficient resilience necessary to maintain its service delivery under diverse situations. This paper has two fold objective, the first is to propose a formal approach to measure cyber resilience from different aspects (i.e., attacks, failures) and at different levels (i.e., pro-active, resistive and reactive). To achieve the first objective, we propose a formal frame- work named as: Cyber Resilience Engineering Framework (CREF). The second objective is to build a resilient system by construction. The idea is to build a formal model of a cyber system, which is initially not resilient with respect to attacks. Then by systematic refinements of the formal model and by its model checking, we attain resiliency. We exemplify our technique through the case study of simple cyber security device (i.e., network firewall).

Hardware Trojans are an emerging threat that intrudes in the design and manufacturing cycle of the chips and has gained much attention lately due to the severity of the problems it draws to the chip supply chain. Hardware Typically, hardware Trojans are not detected during the usual manufacturing testing due to the fact that they are activated as an effect of a rare event. A class of published HTs are based on the geometrical characteristics of the circuit and claim to be undetectable, in the sense that their activation cannot be detected. In this work we study the effect of continuously monitoring the inputs of the module under test with respect to the detection of HTs possibly inserted in the module, either in the design or the manufacturing stage.

In order to enhance the supply chain security at airports, the German federal ministry of education and research has initiated the project ESECLOG (enhanced security in the air cargo chain) which has the goal to improve the threat detection accuracy using one-sided access methods. In this paper, we present a new X-ray backscatter technology for non-intrusive imaging of suspicious objects (mainly low-Z explosives) in luggage's and parcels with only a single-sided access. A key element in this technology is the X-ray backscatter camera embedded with a special twisted-slit collimator. The developed technology has efficiently resolved the problem related to the imaging of complex interior of the object by fixing source and object positions and changing only the scanning direction of the X-ray backscatter camera. Experiments were carried out on luggages and parcels packed with mock-up dangerous materials including liquid and solid explosive simulants. In addition, the quality of the X-ray backscatter image was enhanced by employing high-resolution digital detector arrays. Experimental results are discussed and the efficiency of the present technique to detect suspicious objects in luggages and parcels is demonstrated. At the end, important applications of the proposed backscatter imaging technology to the aviation security are presented.

Design-time analysis and verification of distributed real-time embedded systems necessitates the modeling of the time-varying performance of the network and comparing that to application requirements. Earlier work has shown how to build a system network model that abstracted away the network's physical medium and protocols which govern its access and multiplexing. In this work we show how to apply a network medium channel access protocol, such as Time-Division Multiple Access (TDMA), to our network analysis methods and use the results to show that the abstracted model without the explicit model of the protocol is valid.

Content delivery such as P2P or video streaming generates the main part of the Internet traffic and Content Centric Network (CCN) appears as an appropriate architecture to satisfy the user needs. However, the lack of scalable routing scheme is one of the main obstacles that slows down a large deployment of CCN at an Internet-scale. In this paper we propose to use the Software-Defined Networking (SDN) paradigm to decouple data plane and control plane and present SRSC, a new routing scheme for CCN. Our solution is a clean-slate approach using only CCN messages and the SDN paradigm. We implemented our solution into the NS-3 simulator and perform simulations of our proposal. SRSC shows better performances than the flooding scheme used by default in CCN: it reduces the number of messages, while still improves CCN caching performances.

Online Social Networks exploit a lightweight process to identify their users so as to facilitate their fast adoption. However, such convenience comes at the price of making legitimate users subject to different threats created by fake accounts. Therefore, there is a crucial need to empower users with tools helping them in assigning a level of trust to whomever they interact with. To cope with this issue, in this paper we introduce a novel model, DIVa, that leverages on mining techniques to find correlations among user profile attributes. These correlations are discovered not from user population as a whole, but from individual communities, where the correlations are more pronounced. DIVa exploits a decentralized learning approach and ensures privacy preservation as each node in the OSN independently processes its local data and is required to know only its direct neighbors. Extensive experiments using real-world OSN datasets show that DIVa is able to extract fine-grained community-aware correlations among profile attributes with average improvements up to 50% than the global approach.

Data security has always been a major concern and a huge challenge for governments and individuals throughout the world since early times. Recent advances in technology, such as the introduction of cloud computing, make it even a bigger challenge to keep data secure. In parallel, high throughput mobile devices such as smartphones and tablets are designed to support these new technologies. The high throughput requires power-efficient designs to maintain the battery-life. In this paper, we propose a novel Joint Security and Advanced Low Density Parity Check (LDPC) Coding (JSALC) method. The JSALC is composed of two parts: the Joint Security and Advanced LDPC-based Encryption (JSALE) and the dual-step Secure LDPC code for Channel Coding (SLCC). The JSALE is obtained by interlacing Advanced Encryption System (AES)-like rounds and Quasi-Cyclic (QC)-LDPC rows into a single primitive. Both the JSALE code and the SLCC code share the same base quasi-cyclic parity check matrix (PCM) which retains the power efficiency compared to conventional systems. We show that the overall JSALC Frame-Error-Rate (FER) performance outperforms other cryptcoding methods by over 1.5 dB while maintaining the AES-128 security level. Moreover, the JSALC enables error resilience and has higher diffusion than AES-128.

Biomimetic flapping wing vehicles have attracted recent interest because of their numerous potential military and civilian applications. In this paper we describe the design of a multi-agent adaptive controller for such a vehicle. This controller is responsible for estimating the vehicle pose (position and orientation) and then generating four parameters needed for split-cycle control of wing movements to correct pose errors. These parameters are produced via a subsumption architecture rule base. The control strategy is fault tolerant. Using an online learning process an agent continuously monitors the vehicle's behavior and initiates diagnostics if the behavior has degraded. This agent can then autonomously adapt the rule base if necessary. Each rule base is constructed using a combination of extrinsic and intrinsic evolution. Details on the vehicle, the multi-agent system architecture, agent task scheduling, rule base design, and vehicle control are provided.

The main challenge of ultra-reliable machine-to-machine (M2M) control applications is to meet the stringent timing and reliability requirements of control systems, despite the adverse properties of wireless communication for delay and packet errors, and limited battery resources of the sensor nodes. Since the transmission delay and energy consumption of a sensor node are determined by the transmission power and rate of that sensor node and the concurrently transmitting nodes, the transmission schedule should be optimized jointly with the transmission power and rate of the sensor nodes. Previously, it has been shown that the optimization of power control and rate adaptation for each node subset can be separately formulated, solved and then used in the scheduling algorithm in the optimal solution of the joint optimization of power control, rate adaptation and scheduling problem. However, the power control and rate adaptation problem has been only formulated and solved for continuous rate transmission model, in which Shannon's capacity formulation for an Additive White Gaussian Noise (AWGN) wireless channel is used in the calculation of the maximum achievable rate as a function of Signal-to-Interference-plus-Noise Ratio (SINR). In this paper, we formulate the power control and rate adaptation problem with the objective of minimizing the time required for the concurrent transmission of a set of sensor nodes while satisfying their transmission delay, reliability and energy consumption requirements based on the more realistic discrete rate transmission model, in which only a finite set of transmit rates are supported. We propose a polynomial time algorithm to solve this problem and prove the optimality of the proposed algorithm. We then combine it with the previously proposed scheduling algorithms and demonstrate its close to optimal performance via extensive simulations.

he Advanced Metering Infrastructure (AMI) in a smart grid comprises of a large number of smart meters along with heterogeneous cyber-physical components. These components communicate with each other through different communication media, protocols, and delivery modes for transmitting usage reports and control commands to and from the utility. There is potential for dependability threats especially due to misconfigurations, which can easily disrupt the operations in AMI. Therefore, an AMI must be configured correctly. In this paper, we present an automated configuration synthesis framework that mitigates potential threats by eliminating mis-configurations. We have manifold contributions in this research: (i) formal modeling of AMI configurations including AMI device configurations, topology and communication properties, and data flows among the devices; (ii) formal modeling of AMI operational integrity properties considering the interdependencies among AMI devices' configurations; and (iii) implementing the model using Satisfiability Modulo Theories (SMT), execution of which synthesizes necessary AMI configurations. We demonstrate the proposed framework on an example case study and evaluate the scalability of the framework on various synthetic AMI networks.
 

Hash based biometric template protection schemes (BTPS), such as fuzzy commitment, fuzzy vault, and secure sketch, address the privacy leakage concern on the plain biometric template storage in a database through using cryptographic hash calculation for template verification. However, cryptographic hashes have only computational security whose being cracked shall leak the biometric feature in these BTPS; and furthermore, existing BTPS are rarely able to detect during a verification process whether a probe template has been leaked from the database or not (i.e., being used by an imposter or a genuine user). In this paper we tailor the "honeywords" idea, which was proposed to detect the hashed password cracking, to enable the detectability of biometric template database leakage. However, unlike passwords, biometric features encoded in a template cannot be renewed after being cracked and thus not straightforwardly able to be protected by the honeyword idea. To enable the honeyword idea on biometrics, diversifiability (and thus renewability) is required on the biometric features. We propose to use BTPS for his purpose in this paper and present a machine learning based protected template generation protocol to ensure the best anonymity of the generated sugar template (from a user's genuine biometric feature) among other honey ones (from synthesized biometric features).

In the Internet-of-Things (IoT), users might share part of their data with different IoT prosumers, which offer applications or services. Within this open environment, the existence of an adversary introduces security risks. These can be related, for instance, to the theft of user data, and they vary depending on the security controls that each IoT prosumer has put in place. To minimize such risks, users might seek an "optimal" set of prosumers. However, assuming the adversary has the same information as the users about the existing security measures, he can then devise which prosumers will be preferable (e.g., with the highest security levels) and attack them more intensively. This paper proposes a decision-support approach that minimizes security risks in the above scenario. We propose a non-cooperative, two-player game entitled Prosumers Selection Game (PSG). The Nash Equilibria of PSG determine subsets of prosumers that optimize users' payoffs. We refer to any game solution as the Nash Prosumers Selection (NPS), which is a vector of probabilities over subsets of prosumers. We show that when using NPS, a user faces the least expected damages. Additionally, we show that according to NPS every prosumer, even the least secure one, is selected with some non-zero probability. We have also performed simulations to compare NPS against two different heuristic selection algorithms. The former is proven to be approximately 38% more effective in terms of security-risk mitigation.

In this paper we present a framework for Quality of Information (QoI)-aware networking. QoI quantifies how useful a piece of information is for a given query or application. Herein, we present a general QoI model, as well as a specific example instantiation that carries throughout the rest of the paper. In this model, we focus on the tradeoffs between precision and accuracy. As a motivating example, we look at traffic video analysis. We present simple algorithms for deriving various traffic metrics from video, such as vehicle count and average speed. We implement these algorithms both on a desktop workstation and less-capable mobile device. We then show how QoI-awareness enables end devices to make intelligent decisions about how to process queries and form responses, such that huge bandwidth savings are realized.

Secret key establishment is considered to be one of the main challenging issues in cryptography. Many security algorithms are implemented in practice using complicated mathematical methods to exchange secret keys, but those methods are not desirable in power limited terminals such as cellular and sensor networks. In this paper, we propose a physical layer method for exchanging secret key bits in precoding based multi-input multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. The proposed method uniquely relates the key bits to the indices of the precoding matrix used for MIMO channel precoding. The basic idea of the technique is to utilize a MIMO-OFDM precoding codebook. Comparative analysis with respect to the average number of mismatch bits, named key error rate (KER), shows an interesting lead for the new method relative to existing work. In addition, it will be shown that the proposed technique requires lower computation per byte per secret key.

NSTX used MDSplus extensively to record data, relay information and control data acquisition hardware. For NSTX-U the same functionality is expected as well as an expansion into the realm of securely maintaining parameters for machine protection. Specifically, we designed the Digital Coil Protection System (DCPS) to use MDSplus to manage our physical and electrical limit values and relay information about the state of our acquisition system to DCPS. Additionally, test and development systems need to use many of the same resources concurrently without causing interference with other critical systems. Further complications include providing access to critical, protected data without risking changes being made to it by unauthorized users or through unsupported or uncontrolled methods either maliciously or unintentionally. To achieve a level of confidence with an existing software system designed with minimal security controls, a number of changes to how MDSplus is used were designed and implemented. Trees would need to be verified and checked for changes before use. Concurrent creation of trees from vastly different use-cases and varying requirements would need to be supported. This paper will further discuss the impetus for developing such designs and the methods used to implement them.