Biblio
Cyber attacks and the associated costs made cybersecurity a vital part of any system. User behavior and decisions are still a major part in the coping with these risks. We developed a model of optimal investment and human decisions with security measures, given that the effectiveness of each measure depends partly on the performance of the others. In an online experiment, participants classified events as malicious or non-malicious, based on the value of an observed variable. Prior to making the decisions, they had invested in three security measures - a firewall, an IDS or insurance. In three experimental conditions, maximal investment in only one of the measures was optimal, while in a fourth condition, participants should not have invested in any of the measures. A previous paper presents the analysis of the investment decisions. This paper reports users' classifications of events when interacting with these systems. The use of security mechanisms helped participants gain higher scores. Participants benefited in particular from purchasing IDS and/or Cyber Insurance. Participants also showed higher sensitivity and compliance with the alerting system when they could benefit from investing in the IDS. Participants, however, did not adjust their behavior optimally to the security settings they had chosen. The results demonstrate the complex nature of risk-related behaviors and the need to consider human abilities and biases when designing cyber security systems.
Enterprise networks are increasingly moving towards Software Defined Networking, which is becoming a major trend in the networking arena. With the increased popularity of SDN, there is a greater need for security measures for protecting the enterprise networks. This paper focuses on the design and implementation of an integrated security architecture for SDN based enterprise networks. The integrated security architecture uses a policy-based approach to coordinate different security mechanisms to detect and counteract a range of security attacks in the SDN. A distinguishing characteristic of the proposed architecture is its ability to deal with dynamic changes in the security attacks as well as changes in trust associated with the network devices in the infrastructure. The adaptability of the proposed architecture to dynamic changes is achieved by having feedback between the various security components/mechanisms in the architecture and managing them using a dynamic policy framework. The paper describes the prototype implementation of the proposed architecture and presents security and performance analysis for different attack scenarios. We believe that the proposed integrated security architecture provides a significant step towards achieving a secure SDN for enterprises.
IoT devices introduce unprecedented threats into home and professional networks. As they fail to adhere to security best practices, they are broadly exploited by malicious actors to build botnets or steal sensitive information. Their adoption challenges established security standard as classic security measures are often inappropriate to secure them. This is even more problematic in sensitive environments where the presence of insecure IoTs can be exploited to bypass strict security policies. In this paper, we demonstrate an attack against a highly secured network using a Bluetooth smart bulb. This attack allows a malicious actor to take advantage of a smart bulb to exfiltrate data from an air gapped network.
In today's IIoT world, most of the IoT platform providers like Microsoft, Amazon and Google are focused towards connecting devices and extract data from the devices and send the data to the Cloud for analytics. Only there are few companies concentrating on Security measures implemented on Edge Node. Gartner estimates that by 2020, more than 25 percent of all enterprise attackers will make use of the Industrial IoT. As Cyber Security Threat is getting more important, it is essential to ensure protection of data both at rest and at motion. The reflex of Cyber Security in the Industrial IoT Domain is much more severe when compared to the Consumer IoT Segment. The new bottleneck in this are security services which employ computationally intensive software operations and system services [1]. Resilient services consume considerable resources in a design. When such measures are added to thwart security attacks, the resource requirements grow even more demanding. Since the standard IIoT Gateways and other sub devices are resource constrained in nature the conventional design for security services will not be applicable in this case. This paper proposes an intelligent architectural paradigm for the Constrained IIoT Gateways that can efficiently identify the Cyber-Attacks in the Industrial IoT domain.
Nowadays, everyone is living in a digital world with various of virtual experiences and realities, but all of them may eventually cause real threats in our real world. Some of these threats have been born together with the first electronic mail service. Some of them might be considered as really basic and simple, compared to others that were developed and advanced in time to adapt themselves for the security defense mechanisms of the modern digital world. On a daily basis, more than 238.4 billion emails are sent worldwide, which makes more than 2.7 million emails per second, and these statistics are only from the publicly visible networks. Having that information and considering around 60% and above of all emails as threatening or not legitimate, is more than concerning. Unfortunately, even the modern security measures and systems are not capable to identify and prevent all the fraudulent content that is created and distributed every day. In this paper we will cover the most common attack vectors, involving the already mass email infrastructures, the required contra measures to minimize the impact over the corporate environments and what else should be developed to mitigate the modern sophisticated email attacks.
Nowadays, Information Technology is one of the important parts of human life and also of organizations. Organizations face problems such as IT problems. To solve these problems, they have to improve their security sections. Thus there is a need for security assessments within organizations to ensure security conditions. The use of security standards and general metric can be useful for measuring the safety of an organization; however, it should be noted that the general metric which are applied to businesses in general cannot be effective in this particular situation. Thus it's important to select metric standards for different businesses to improve both cost and organizational security. The selection of suitable security measures lies in the use of an efficient way to identify them. Due to the numerous complexities of these metric and the extent to which they are defined, in this paper that is based on comparative study and the benchmarking method, taxonomy for security measures is considered to be helpful for a business to choose metric tailored to their needs and conditions.
Cyber-attacks and intrusions in cyber-physical control systems are, currently, difficult to reliably prevent. Knowing a system's vulnerabilities and implementing static mitigations is not enough, since threats are advancing faster than the pace at which static cyber solutions can counteract. Accordingly, the practice of cybersecurity needs to ensure that intrusion and compromise do not result in system or environment damage or loss. In a previous paper [2], we described the Cyberspace Security Econometrics System (CSES), which is a stakeholder-aware and economics-based risk assessment method for cybersecurity. CSES allows an analyst to assess a system in terms of estimated loss resulting from security breakdowns. In this paper, we describe two new related contributions: 1) We map the Cyberspace Security Econometrics System (CSES) method to the evaluation and mitigation steps described by the NIST Guide to Industrial Control Systems (ICS) Security, Special Publication 800-82r2. Hence, presenting an economics-based and stakeholder-aware risk evaluation method for the implementation of the NIST-SP-800-82 guide; and 2) We describe the application of this tailored method through the use of a fictitious example of a critical infrastructure system of an electric and gas utility.
With the continuous development of mobile based Wireless technologies, Bluetooth plays a vital role in smart-phone Era. In such scenario, the security measures are needed to be enhanced for Bluetooth. We propose a Node Energy Based Virus Propagation Model (NBV) for Bluetooth. The algorithm works with key features of node capacity and node energy in Bluetooth network. This proposed NBV model works along with E-mail worm Propagation model. Finally, this work simulates and compares the virus propagation with respect to Node Energy and network traffic.
In a number of information security scenarios, human beings can be better than technical security measures at detecting threats. This is particularly the case when a threat is based on deception of the user rather than exploitation of a specific technical flaw, as is the case of spear-phishing, application spoofing, multimedia masquerading and other semantic social engineering attacks. Here, we put the concept of the human-as-a-security-sensor to the test with a first case study on a small number of participants subjected to different attacks in a controlled laboratory environment and provided with a mechanism to report these attacks if they spot them. A key challenge is to estimate the reliability of each report, which we address with a machine learning approach. For comparison, we evaluate the ability of known technical security countermeasures in detecting the same threats. This initial proof of concept study shows that the concept is viable.
The power grid is a prime target of cyber criminals and warrants special attention as it forms the backbone of major infrastructures that drive the nation's defense and economy. Developing security measures for the power grid is challenging since it is physically dispersed and interacts dynamically with associated cyber infrastructures that control its operation. This paper presents a mathematical framework to investigate stability of two area systems due to data attacks on Automatic Generation Control (AGC) system. Analytical and simulation results are presented to identify attack levels that could drive the AGC system to potentially become unstable.
Cyber-physical system integrity requires both hardware and software security. Many of the cyber attacks are successful as they are designed to selectively target a specific hardware or software component in an embedded system and trigger its failure. Existing security measures also use attack vector models and isolate the malicious component as a counter-measure. Isolated security primitives do not provide the overall trust required in an embedded system. Trust enhancements are proposed to a hardware security platform, where the trust specifications are implemented in both software and hardware. This distribution of trust makes it difficult for a hardware-only or software-only attack to cripple the system. The proposed approach is applied to a smart grid application consisting of third-party soft IP cores, where an attack on this module can result in a blackout. System integrity is preserved in the event of an attack and the anomalous behavior of the IP core is recorded by a supervisory module. The IP core also provides a snapshot of its trust metric, which is logged for further diagnostics.
RFID-enabled product supply chain visibility is usually implemented by building up a view of the product history of its activities starting from manufacturing or even earlier with a dynamically updated e-pedigree for track-and-trace, which is examined and authenticated at each node of the supply chain for data consistence with the pre-defined one. However, while effectively reducing the risk of fakes, this visibility can't guarantee that the product is authentic without taking further security measures. To the best of our knowledge, this requires deeper understandings on associations of object events with the counterfeiting activities, which is unfortunately left blank. In this paper, the taxonomy of counterfeiting possibilities is initially developed and analyzed, the structure of EPC-based events is then re-examined, and an object-centric coding mechanism is proposed to construct the object-based event “pedigree” for such event exception detection and inference. On this basis, the system architecture framework to achieve the objectivity of object event visibility for anti-counterfeiting is presented, which is also applicable to other aspects of supply chain management.
Due to limited time and resources, web software engineers need support in identifying vulnerable code. A practical approach to predicting vulnerable code would enable them to prioritize security auditing efforts. In this paper, we propose using a set of hybrid (static+dynamic) code attributes that characterize input validation and input sanitization code patterns and are expected to be significant indicators of web application vulnerabilities. Because static and dynamic program analyses complement each other, both techniques are used to extract the proposed attributes in an accurate and scalable way. Current vulnerability prediction techniques rely on the availability of data labeled with vulnerability information for training. For many real world applications, past vulnerability data is often not available or at least not complete. Hence, to address both situations where labeled past data is fully available or not, we apply both supervised and semi-supervised learning when building vulnerability predictors based on hybrid code attributes. Given that semi-supervised learning is entirely unexplored in this domain, we describe how to use this learning scheme effectively for vulnerability prediction. We performed empirical case studies on seven open source projects where we built and evaluated supervised and semi-supervised models. When cross validated with fully available labeled data, the supervised models achieve an average of 77 percent recall and 5 percent probability of false alarm for predicting SQL injection, cross site scripting, remote code execution and file inclusion vulnerabilities. With a low amount of labeled data, when compared to the supervised model, the semi-supervised model showed an average improvement of 24 percent higher recall and 3 percent lower probability of false alarm, thus suggesting semi-supervised learning may be a preferable solution for many real world applications where vulnerability data is missing.