Biblio
Recognising user's risky behaviours in real-time is an important element of providing appropriate solutions and recommending suitable actions for responding to cybersecurity threats. Employing user modelling and machine learning can make this process automated by requires high-performance intelligent agent to create the user security profile. User profiling is the process of producing a profile of the user from historical information and past details. This research tries to identify the monitoring factors and suggests a novel observation solution to create high-performance sensors to generate the user security profile for a home user concerning the user's privacy. This observer agent helps to create a decision-making model that influences the user's decision following real-time threats or risky behaviours.
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.
Cybersecurity is a major issue today. It is predicted that cybercrime will cost the world \$6 trillion annually by 2021. It is important to make logins secure as well as to make advances in security in order to catch cybercriminals. This paper will design and create a device that will use Fuzzy logic to identify a person by the rhythm and frequency of their typing. The device will take data from a user from a normal password entry session. This data will be used to make a Fuzzy system that will be able to identify the user by their typing speed. An application of this project could be used to make a more secure log-in system for a user. The log-in system would not only check that the correct password was entered but also that the rhythm of how the password was typed matched the user. Another application of this system could be used to help catch cybercriminals. A cybercriminal may have a certain rhythm at which they type at and this could be used like a fingerprint to help officials locate cybercriminals.
Safety and security of complex critical infrastructures is very important for economic, environmental and social reasons. The interdisciplinary and inter-system dependencies within these infrastructures introduce difficulties in the safety and security design. Late discovery of safety and security design weaknesses can lead to increased costs, additional system complexity, ineffective mitigation measures and delays to the deployment of the systems. Traditionally, safety and security assessments are handled using different methods and tools, although some concepts are very similar, by specialized experts in different disciplines and are performed at different system design life-cycle phases.The methodology proposed in this paper supports a concurrent safety and security Defense in Depth (DiD) assessment at an early design phase and it is designed to handle safety and security at a high level and not focus on specific practical technologies. It is assumed that regardless of the perceived level of security defenses in place, a determined (motivated, capable and/or well-funded) attacker can find a way to penetrate a layer of defense. While traditional security research focuses on removing vulnerabilities and increasing the difficulty to exploit weaknesses, our higher-level approach focuses on how the attacker's reach can be limited and to increase the system's capability for detection, identification, mitigation and tracking. The proposed method can assess basic safety and security DiD design principles like Redundancy, Physical separation, Functional isolation, Facility functions, Diversity, Defense lines/Facility and Computer Security zones, Safety classes/Security Levels, Safety divisions and physical gates/conduits (as defined by the International Atomic Energy Agency (IAEA) and international standards) concurrently and provide early feedback to the system engineer. A prototype tool is developed that can parse the exported project file of the interdisciplinary model. Based on a set of safety and security attributes, the tool is able to assess aspects of the safety and security DiD capabilities of the design. Its results can be used to identify errors, improve the design and cut costs before a formal human expert inspection. The tool is demonstrated on a case study of an early conceptual design of a complex system of a nuclear power plant.
This work describes a top down systems security requirements analysis approach for understanding and eliciting security requirements for a notional small unmanned aerial system (SUAS). More specifically, the System-Theoretic Process Analysis approach for Security (STPA-Sec) is used to understand and elicit systems security requirements. The effort employs STPA-Sec on a notional SUAS system case study to detail the development of functional-level security requirements, design-level engineering considerations, and architectural-level security specification criteria early in the system life cycle when the solution trade-space is largest rather than merely examining components and adding protections during system operation or sustainment. These details were elaborated during a semester independent study research effort by two United States Air Force Academy Systems Engineering cadets, guided by their instructor and a series of working group sessions with UAS operators and subject matter experts. This work provides insight into a viable systems security requirements analysis approach which results in traceable security, safety, and resiliency requirements that can be designed-for, built-to, and verified with confidence.
A critical need exists for collaboration and action by government, industry, and academia to address cyber weaknesses or vulnerabilities inherent to embedded or cyber physical systems (CPS). These vulnerabilities are introduced as we leverage technologies, methods, products, and services from the global supply chain throughout a system's lifecycle. As adversaries are exploiting these weaknesses as access points for malicious purposes, solutions for system security and resilience become a priority call for action. The SAE G-32 Cyber Physical Systems Security Committee has been convened to address this complex challenge. The SAE G-32 will take a holistic systems engineering approach to integrate system security considerations to develop a Cyber Physical System Security Framework. This framework is intended to bring together multiple industries and develop a method and common language which will enable us to more effectively, efficiently, and consistently communicate a risk, cost, and performance trade space. The standard will allow System Integrators to make decisions utilizing a common framework and language to develop affordable, trustworthy, resilient, and secure systems.
We present ClearTrack, a system that tracks meta-data for each primitive value in Java programs to detect and nullify a range of vulnerabilities such as integer overflow/underflow and SQL/command injection vulnerabilities. Contributions include new techniques for eliminating false positives associated with benign integer overflows and underflows, new metadata-aware techniques for detecting and nullifying SQL/command command injection attacks, and results from an independent evaluation team. These results show that 1) ClearTrack operates successfully on Java programs comprising hundreds of thousands of lines of code (including instrumented jar files and Java system libraries, the majority of the applications comprise over 3 million lines of code), 2) because of computations such as cryptography and hash table calculations, these applications perform millions of benign integer overflows and underflows, and 3) ClearTrack successfully detects and nullifies all tested integer overflow and underflow and SQL/command injection vulnerabilities in the benchmark applications.
In the paper, an intrusion detection system to safeguard computer software is proposed. The detection is based on negative selection algorithm, inspired by the human immunity mechanism. It is composed of two stages, generation of receptors and anomaly detection. Experimental results of the proposed system are presented, analyzed, and concluded.
The purpose of this work is to implement a universal system for collecting and analyzing event logs from sources that use the Windows operating system. The authors use event-forwarding technology to collect data from logs. Security information and event management detects incidents from received events. The authors analyze existing methods for transmitting event log entries from sources running the Windows operating system. This article describes in detail how to connect event sources running on the Windows operating system to the event collector without connecting to a domain controller. Event sources are authenticated using certificates created by the event collector. The authors suggest a scheme for connecting the event collector to security information and event management. Security information and event management must meet the requirements for use in conjunction with event forwarding technology. The authors of the article demonstrate the scheme of the test stand and the result of testing the event forwarding technology.
Cybersecurity community is slowly leveraging Machine Learning (ML) to combat ever evolving threats. One of the biggest drivers for successful adoption of these models is how well domain experts and users are able to understand and trust their functionality. As these black-box models are being employed to make important predictions, the demand for transparency and explainability is increasing from the stakeholders.Explanations supporting the output of ML models are crucial in cyber security, where experts require far more information from the model than a simple binary output for their analysis. Recent approaches in the literature have focused on three different areas: (a) creating and improving explainability methods which help users better understand the internal workings of ML models and their outputs; (b) attacks on interpreters in white box setting; (c) defining the exact properties and metrics of the explanations generated by models. However, they have not covered, the security properties and threat models relevant to cybersecurity domain, and attacks on explainable models in black box settings.In this paper, we bridge this gap by proposing a taxonomy for Explainable Artificial Intelligence (XAI) methods, covering various security properties and threat models relevant to cyber security domain. We design a novel black box attack for analyzing the consistency, correctness and confidence security properties of gradient based XAI methods. We validate our proposed system on 3 security-relevant data-sets and models, and demonstrate that the method achieves attacker's goal of misleading both the classifier and explanation report and, only explainability method without affecting the classifier output. Our evaluation of the proposed approach shows promising results and can help in designing secure and robust XAI methods.
A rapid rise in cyber-attacks on Cyber Physical Systems (CPS) has been observed in the last decade. It becomes even more concerning that several of these attacks were on critical infrastructures that indeed succeeded and resulted into significant physical and financial damages. Experimental testbeds capable of providing flexible, scalable and interoperable platform for executing various cybersecurity experiments is highly in need by all stakeholders. A container-based SCADA testbed is presented in this work as a potential platform for executing cybersecurity experiments. Through this testbed, a network traffic containing ARP spoofing is generated that represents a Man in the middle (MITM) attack. While doing so, scanning of different systems within the network is performed which represents a reconnaissance attack. The network traffic generated by both ARP spoofing and network scanning are captured and further used for preparing a dataset. The dataset is utilized for training a network classification model through a machine learning algorithm. Performance of the trained model is evaluated through a series of tests where promising results are obtained.
Vehicle-logo location is a crucial step in vehicle-logo recognition system. In this paper, a novel approach of the vehicle-logo location based on edge detection and morphological filter is proposed. Firstly, the approximate location of the vehicle-logo region is determined by the prior knowledge about the position of the vehicle-logo; Secondly, the texture measure is defined to recognize the texture of the vehicle-logo background; Then, vertical edge detection is executed for the vehicle-logo background with the horizontal texture and horizontal edge detection is implemented for the vehicle-logo background with the vertical texture; Finally, position of the vehicle-logo is located accurately by mathematical morphology filter. Experimental results show the proposed method is effective.
In cyber threat information sharing, secure transfer and protecting privacy are very important. In this paper we solve these issues by suggesting a platform based on private permissioned Blockchain, which provides us with access control as well. The platform is called Anon-ISAC and is built on the Enhanced Privacy ID (EPID) zero-knowledge proof scheme. It makes use of permissioned Blockchain as a way to keep identity anonymous. Organizations can share their information on incidents or other artifacts among trusted parties, while they keep their identity hidden. This will save them from unwanted consequences of exposure of sensitive security information.
In the northern gas fields, most data are transmitted via wireless networks, which requires special transmission security measures. Herewith, the gas field infrastructure dictates cybersecurity modules to not only meet standard requirements but also ensure reduced energy consumption. The paper discusses the issue of building such a module for a process control system based on the RTP-04M recorder operating in conjunction with an Android-based mobile device. The software options used for the RSA and Diffie-Hellman data encryption and decryption algorithms on both the RTP-04M and the Android-based mobile device sides in the Keil μVision4 and Android Studio software environments, respectively, have shown that the Diffie-Hellman algorithm is preferable. It provides significant savings in RAM and CPU resources and power consumption of the recorder. In terms of energy efficiency, the implemented programs have been analyzed in the Android Studio (Android Profiler) and Simplicity Studio (Advanced Energy Monitor) environments. The integration of this module into the existing software will improve the field's PCS cybersecurity level due to protecting data transmitted from third-party attacks.
Most anti-collusion audio fingerprinting schemes are aiming at finding colluders from the illegal redistributed audio copies. However, the loss caused by the redistributed versions is inevitable. In this letter, a novel fingerprinting scheme is proposed to eliminate the motivation of collusion attack. The audio signal is transformed to the frequency domain by the Fourier transform, and the coefficients in frequency domain are reversed in different degrees according to the fingerprint sequence. Different from other fingerprinting schemes, the coefficients of the host media are excessively modified by the proposed method in order to reduce the quality of the colluded version significantly, but the imperceptibility is well preserved. Experiments show that the colluded audio cannot be reused because of the poor quality. In addition, the proposed method can also resist other common attacks. Various kinds of copyright risks and losses caused by the illegal redistribution are effectively avoided, which is significant for protecting the copyright of audio.
Advancements in computing, communication, and sensing technologies are making it possible to embed, control, and gather vital information from tiny devices that are being deployed and utilized in practically every aspect of our modernized society. From smart home appliances to municipal water and electric industrial facilities to our everyday work environments, the next Internet frontier, dubbed IoT, is promising to revolutionize our lives and tackle some of our nations' most pressing challenges. While the seamless interconnection of IoT devices with the physical realm is envisioned to bring a plethora of critical improvements in many aspects and diverse domains, it will undoubtedly pave the way for attackers that will target and exploit such devices, threatening the integrity of their data and the reliability of critical infrastructure. Further, such compromised devices will undeniably be leveraged as the next generation of botnets, given their increased processing capabilities and abundant bandwidth. While several demonstrations exist in the literature describing the exploitation procedures of a number of IoT devices, the up-to-date inference, characterization, and analysis of unsolicited IoT devices that are currently deployed "in the wild" is still in its infancy. In this article, we address this imperative task by leveraging active and passive measurements to report on unsolicited Internet-scale IoT devices. This work describes a first step toward exploring the utilization of passive measurements in combination with the results of active measurements to shed light on the Internet-scale insecurities of the IoT paradigm. By correlating results of Internet-wide scanning with Internet background radiation traffic, we disclose close to 14,000 compromised IoT devices in diverse sectors, including critical infrastructure and smart home appliances. To this end, we also analyze their generated traffic to create effective mitigation signatures that could be deployed in local IoT realms. To support largescale empirical data analytics in the context of IoT, we make available the inferred and extracted IoT malicious raw data through an authenticated front-end service. The outcomes of this work confirm the existence of such compromised devices on an Internet scale, while the generated inferences and insights are postulated to be employed for inferring other similarly compromised IoT devices, in addition to contributing to IoT cyber security situational awareness.