Biblio

Dynamic adaptation should not leave a software system in an inconsistent state, as it could lead to failure. Prior research has used inter-component dependency models of a system to determine a safe interval for the adaptation of its components, where the most important tradeoff is between disruption in the operations of the system and reachability of safe intervals. This article presents Savasana, which automatically analyzes a software system’s code to extract both inter- and intra-component dependencies. In this way, Savasana is able to obtain more fine-grained models compared to previous approaches. Savasana then uses the detailed models to find safe adaptation intervals that cannot be determined using techniques from prior research. This allows Savasana to achieve a better tradeoff between disruption and reachability. The article demonstrates how Savasana infers safe adaptation intervals for components of a software system under various use cases and conditions.

When encountering a packet flow for which it has no covering rule, a software-defined networking (SDN) switch requests an appropriate rule from its controller; this request delays the routing of the flow until the controller responds. We show that this delay gives rise to a timing side channel in which an attacker can test for the recent occurrence of a target flow by judiciously probing the switch with forged flows and using the delays they suffer to discern whether covering rules were previously installed in the switch. We develop a Markov model of an SDN switch to permit the attacker to select the best probe (or probes) to infer whether a target flow has recently occurred. Our model captures complexities related to rule evictions to make room for other rules; rule timeouts due to inactivity; the presence of multiple rules that apply to overlapping sets of flows; and rule priorities. We show that our model permits detection of target flows with considerable accuracy in many cases.

When encountering a packet for which it has no matching forwarding rule, a software-defined networking (SDN) switch requests an appropriate rule from its controller; this request delays the routing of the flow until the controller responds.  We show that this delay gives rise to a timing side channel in which an attacker can test for the recent occurrence of a target flow by judiciously probing the switch with forged flows and using the delays they encounter to discern whether covering rules were previously installed in the switch.  We develop a Markov model of an SDN switch to permit the attacker to select the best probe (or probes) to infer whether a target flow has recently occurred.  Our model captures practical challenges related to rule evictions to make room for other rules; rule timeouts due to inactivity; the presence of multiple rules that apply to overlapping sets of flows; and rule priorities.  We show that our model enables detection of target flows with considerable accuracy in many cases.

In parallel with the meteoric rise of mobile software, we are witnessing an alarming escalation in the number and sophistication of the security threats targeted at mobile platforms, particularly Android, as the dominant platform. While existing research has made significant progress towards detection and mitigation of Android security, gaps and challenges remain. This paper contributes a comprehensive taxonomy to classify and characterize the state-of-the-art research in this area. We have carefully followed the systematic literature review process, and analyzed the results of more than 300 research papers, resulting in the most comprehensive and elaborate investigation of the literature in this area of research. The systematic analysis of the research literature has revealed patterns, trends, and gaps in the existing literature, and underlined key challenges and opportunities that will shape the focus of future research efforts.

Software-defined networking (SDN) overcomes many limitations of traditional networking architectures because of its programmable and flexible nature. Security applications, for instance, can dynamically reprogram a network to respond to ongoing threats in real time. However, the same flexibility also creates risk, since it can be used against the network. Current SDN architectures potentially allow adversaries to disrupt one or more SDN system components and to hide their actions in doing so. That makes assurance and reasoning about past network events more difficult, if not impossible. In this paper, we argue that an SDN architecture must incorporate various notions of accountability for achieving systemwide cyber resiliency goals. We analyze accountability based on a conceptual framework, and we identify how that analysis fits in with the SDN architecture's entities and processes. We further consider a case study in which accountability is necessary for SDN network applications, and we discuss the limits of current approaches.

The accessibility of on-chip embedded infrastructure for test, reconfiguration, or debug poses a serious security problem. Access mechanisms based on IEEE Std 1149.1 (JTAG), and especially reconfigurable scan networks (RSNs), as allowed by IEEE Std 1500, IEEE Std 1149.1-2013, and IEEE Std 1687 (IJTAG), require special care in the design and development. This work studies the threats to trustworthy data transmission in RSNs posed by untrusted components within the RSN and external interfaces. We propose a novel scan pattern generation method that finds trustworthy access sequences to prevent sniffing and spoofing of transmitted data in the RSN. For insecure RSNs, for which such accesses do not exist, we present an automated transformation that improves the security and trustworthiness while preserving the accessibility to attached instruments. The area overhead is reduced based on results from trustworthy access pattern generation. As a result, sensitive data is not exposed to untrusted components in the RSN, and compromised data cannot be injected during trustworthy accesses.

Security researchers do not have sufficient example systems for conducting research on advanced persistent threats, and companies and agencies that experience attacks in the wild are reluctant to release detailed information that can be examined. In this paper, we describe an Advanced Persistent Threat Exemplar that is intended to provide a real-world attack scenario with sufficient complexity for reasoning about defensive system adaptation, while not containing so much information as to be too complex. It draws from actual published attacks and experiences as a security engineer by the authors.

The Security Behavior Observatory (SBO) is a longitudinal field-study of computer security habits that provides a novel dataset for validating computer security metrics. This paper demonstrates a new strategy for validating phishing detection ability metrics by comparing performance on a phishing signal detection task with data logs found in the SBO. We report: (1) a test of the robustness of performance on the signal detection task by replicating Canfield, Fischhoff and Davis (2016), (2) an assessment of the task's construct validity, and (3) evaluation of its predictive validity using data logs. We find that members of the SBO sample had similar signal detection ability compared to members of the previous mTurk sample and that performance on the task correlated with the Security Behavior Intentions Scale (SeBIS). However, there was no evidence of predictive validity, as the signal detection task performance was unrelated to computer security outcomes in the SBO, including the presence of malicious URLs, malware, and malicious files. We discuss the implications of these findings and the challenges of comparing behavior on structured experimental tasks to behavior in complex real-world settings.

Lateral movement-based attacks are increasingly leading to compromises in large private and government networks, often resulting in information exfiltration or service disruption. Such attacks are often slow and stealthy and usually evade existing security products. To enable effective detection of such attacks, we present a new approach based on graph-based modeling of the security state of the target system and correlation of diverse indicators of anomalous host behavior. We believe that irrespective of the specific attack vectors used, attackers typically establish a command and control channel to operate, and move in the target system to escalate their privileges and reach sensitive areas. Accordingly, we identify important features of command and control and lateral movement activities and extract them from internal and external communication traffic. Driven by the analysis of the features, we propose the use of multiple anomaly detection techniques to identify compromised hosts. These methods include Principal Component Analysis, k-means clustering, and Median Absolute Deviation-based outlier detection. We evaluate the accuracy of identifying compromised hosts by using injected attack traffic in a real enterprise network dataset, for various attack communication models. Our results show that the proposed approach can detect infected hosts with high accuracy and a low false positive rate.

Choosing how to write natural language scenarios is challenging, because stakeholders may over-generalize their descriptions or overlook or be unaware of alternate scenarios. In security, for example, this can result in weak security constraints that are too general, or missing constraints. Another challenge is that analysts are unclear on where to stop generating new scenarios. In this paper, we introduce the Multifactor Quality Method (MQM) to help requirements analysts to empirically collect system constraints in scenarios based on elicited expert preferences. The method combines quantitative statistical analysis to measure system quality with qualitative coding to extract new requirements. The method is bootstrapped with minimal analyst expertise in the domain affected by the quality area, and then guides an analyst toward selecting expert-recommended requirements to monotonically increase system quality. We report the results of applying the method to security. This include 550 requirements elicited from 69 security experts during a bootstrapping stage, and subsequent evaluation of these results in a verification stage with 45 security experts to measure the overall improvement of the new requirements. Security experts in our studies have an average of 10 years of experience. Our results show that using our method, we detect an increase in the security quality ratings collected in the verification stage. Finally, we discuss how our proposed method helps to improve security requirements elicitation, analysis, and measurement. 

Abstract—Lateral movement-based attacks are increasingly leading to compromises in large private and government networks, often resulting in information exfiltration or service disruption. Such attacks are often slow and stealthy and usually evade existing security products. To enable effective detection of such attacks, we present a new approach based on graph-based modeling of the security state of the target system and correlation of diverse indicators of anomalous host behavior. We believe that irrespective of the specific attack vectors used, attackers typically establish a command and control channel to operate, and move in the target system to escalate their privileges and reach sensitive areas. Accordingly, we identify important features of command and control and lateral movement activities and extract them from internal and external communication traffic. Driven by the analysis of the features, we propose the use of multiple anomaly detection techniques to identify compromised hosts. These methods include Principal Component Analysis, k-means clustering, and Median Absolute Deviation-based utlier detection. We evaluate the accuracy of identifying compromised hosts by using injected attack traffic in a real enterprise network dataset, for various attack communication models. Our results show that the proposed approach can detect infected hosts with high accuracy and a low false positive rate.

In this paper, we analyze the security of cyber-physical systems using the ADversary VIew Security Evaluation (ADVISE) meta modeling approach, taking into consideration the efects of physical attacks. To build our model of the system, we construct an ontology that describes the system components and the relationships among them. The ontology also deines attack steps that represent cyber and physical actions that afect the system entities. We apply the ADVISE meta modeling approach, which admits as input our deined ontology, to a railway system use case to obtain insights regarding the system’s security. The ADVISE Meta tool takes in a system model of a railway station and generates an attack execution graph that shows the actions that adversaries may take to reach their goal. We consider several adversary proiles, ranging from outsiders to insider staf members, and compare their attack paths in terms of targeted assets, time to achieve the goal, and probability of detection. The generated results show that even adversaries with access to noncritical assets can afect system service by intelligently crafting their attacks to trigger a physical sequence of efects. We also identify the physical devices and user actions that require more in-depth monitoring to reinforce the system’s security.

Cyber researchers at Sandia National Laboratories are applying deceptive strategies in defending systems against hackers. Deception strategies are being applied through the use of a recently patented alternative reality by the name of HADES (High-fidelity Adaptive Deception & Emulation System). Instead of obstructing or removing a hacker upon infiltration into a system, HADES leads them to a simulated reality in which cloned virtual hard drives, data sets, and memory that have been inconspicuously altered, are presented. The goal is to introduce doubt to adversaries. 

The growing complexity and frequency of cyberattacks call for advanced methods to enhance the detection and prevention of such attacks. Deception is a cyber defense technique that is drawing more attention from organizations. This technique could be used to detect, deceive, and lure attackers away from sensitive data upon infiltration into a system. It is important to look at the most common features of distributed deception platforms such as high-interaction deception, adaptive deception, and more. 

The Deception technology enhances both Network and Cyber Security into a more effective security strategy by reducing the false positives, profiling the attack, attacker and the ways of attack. 

Lots of traditional embedded systems can be called closed systems in that they do not connect and communicate with systems or devices outside of the entities they are embedded, and some part of these systems are designed based on proprietary protocols or standards. Open embedded systems connect and communicate with other systems or devices through the Internet or other networks, and are designed based on open protocols and standards. This paper discusses two types of security challenges facing open embedded systems: the security of the devices themselves that host embedded systems, and the security of information collected, processed, communicated, and consumed by embedded systems. We also discuss solution techniques to address these challenges.

Authentication and encryption within an embedded system environment using cameras, sensors, thermostats, autonomous vehicles, medical implants, RFID, etc. is becoming increasing important with ubiquitious wireless connectivity. Hardware-based authentication and encryption offer several advantages in these types of resource-constrained applications, including smaller footprints and lower energy consumption. Bitstring and key generation implemented with Physical Unclonable Functions or PUFs can further reduce resource utilization for authentication and encryption operations and reduce overall system cost by eliminating on-chip non-volatile-memory (NVM). In this paper, we propose a dynamic partial reconfiguration (DPR) strategy for implementing both authentication and encryption using a PUF for bitstring and key generation on FPGAs as a means of optimizing the utilization of the limited area resources. We show that the time and energy penalties associated with DPR are small in modern SoC-based architectures, such as the Xilinx Zynq SoC, and therefore, the overall approach is very attractive for emerging resource-constrained IoT applications.

Verifying that hardware design implementations adhere to specifications is a time intensive and sometimes intractable problem due to the massive size of the system's state space. Formal methods techniques can be used to prove certain tractable specification properties; however, they are expensive, and often require subject matter experts to develop and solve. Nonetheless, hardware verification is a critical process to ensure security and safety properties are met, and encapsulates problems associated with trust and reliability. For complex designs where coverage of the entire state space is unattainable, prioritizing regions most vulnerable to security or reliability threats would allow efficient allocation of valuable verification resources. Stackelberg security games model interactions between a defender, whose goal is to assign resources to protect a set of targets, and an attacker, who aims to inflict maximum damage on the targets after first observing the defender's strategy. In equilibrium, the defender has an optimal security deployment strategy, given the attacker's best response. We apply this Stackelberg security framework to synthesized hardware implementations using the design's network structure and logic to inform defender valuations and verification costs. The defender's strategy in equilibrium is thus interpreted as a prioritization of the allocation of verification resources in the presence of an adversary. We demonstrate this technique on several open-source synthesized hardware designs.

Platform as a Service (PaaS) provides middleware resources to cloud customers. As demand for PaaS services increases, so do concerns about the security of PaaS. This paper discusses principal PaaS security and integrity requirements, and vulnerabilities and the corresponding countermeasures. We consider three core cloud elements—multi-tenancy, isolation, and virtualization and how they relate to PaaS services and security trends and concerns such as user and resource isolation, side-channel vulnerabilities in multi-tenant environments, and protection of sensitive data.

In this paper, a novel quantum encryption algorithm for color image is proposed based on multiple discrete chaotic systems. The proposed quantum image encryption algorithm utilize the quantum controlled-NOT image generated by chaotic logistic map, asymmetric tent map and logistic Chebyshev map to control the XOR operation in the encryption process. Experiment results and analysis show that the proposed algorithm has high efficiency and security against differential and statistical attacks.

Once organizations have the security incident and breaches, they have to pay tremendous costs. Although visible cost, such as the incident response cost, customer follow-up care, and legal cost are predictable and calculable, it is tough to evaluate and estimate the invisible damage, such as losing customer loyalty, reputation impact, and the damage of branding. This paper proposes a new method, called "Event Study Methodology with Twitter Sentimental Analysis" to evaluate the invisible cost. This method helps to assess the impact of the security breach and the impact on corporate valuation.

A low power consumption three-position four-way direct drive control valve based on hybrid excited linear actuator (HELA-DDCV) was provided to meet the requirements of the response time and the power consumption. A coupling system numerical model was established and validated by experiments, which is based on Matlab/Simulink, from four points of view: electric circuit, electromagnetic field, mechanism and fluid mechanics. A dual-closed-loop PI control strategy for both spool displacement and coil current is adopted, and the process of displacement response was analyzed as well as the power consumption performances. The results show that the prototype valve spool displacement response time is less than 9.6ms. Furthermore, the holding current is less than 30% of the peak current in working process, which reduces the power consumption effectively and improves the system stability. Note that the holding current can be eliminated when the spool working at the ends of stroke, and 0.26 J energy is needed in once action independent of the working time.