KEYWORDS: Network security, Information security, Defense and security, Computer security, Network architectures, Defense technologies, Control systems, Defense systems
Moving target defense (MTD) is an emerging defense principle that aims to dynamically change attack surface to confuse attackers. By dynamic reconfiguration, MTD intends to invalidate the attacker's intelligence or information collection during reconnaissance, resulting in wasted resources and high attack cost/complexity for the attacker. One of the key merits of MTD is its capability to offer 'affordable defense,' by working with legacy defense mechanisms, such as intrusion detection systems (IDS) or other cryptographic mechanisms. On the other hand, a well-known drawback of MTD is the additional overhead, such as reconfiguration cost and/or potential interruptions of service availability to normal users. In this work, we aim to develop a highly secure, resilient, and affordable MTD-based proactive defense mechanism, which achieves multiple objectives of minimizing system security vulnerabilities and defense cost while maximizing service availability. To this end, we propose a multi-agent Deep Reinforcement Learning (mDRL)-based network slicing technique that can help determine two key resource management decisions: (1) link bandwidth allocation to meet Quality-of-Service requirements and (2) the frequency of triggering IP shuffling as an MTD operation not to hinder service availability by maintaining normal system operations. Specifically, we apply this strategy in an in-vehicle network that uses software-defined networking (SDN) technology to deploy the IP shuffling-based MTD, which dynamically changes IP addresses assigned to electronic control unit (ECU) nodes to introduce uncertainty or confusion for attackers.
KEYWORDS: Information security, Control systems, Network security, Defense and security, Computer security, Artificial intelligence, Alternate lighting of surfaces, Systems modeling, Internet, Forensic science
In cyber and threat intelligence areas, Indicators of Compromise (IOC) can be used as inputs to security controls to guide defense and mitigation activities. We propose a collaboration model in certain attributes in IOC model related to the (1) seriousness of the threat that the IOC triggers and (2) the confidence in the IOC detection or prediction are built based on a community or collaborative model. In this model, users can subscribe or introduce new IOCs based on their own/systems’ exposures or analysis. They can also assess IOCs created by others and vote to continuously change IOC seriousness and confidence values.
Recent technological advances provide the opportunities to bridge the physical world with cyber-space that leads to complex and multi-domain cyber physical systems (CPS) where physical systems are monitored and controlled using numerous smart sensors and cyber space to respond in real-time based on their operating environment. However, the rapid adoption of smart, adaptive and remotely accessible connected devices in CPS makes the cyberspace more complex and diverse as well as more vulnerable to multitude of cyber-attacks and adversaries. In this paper, we aim to design, develop and evaluate a distributed machine learning algorithm for adversarial resiliency where developed algorithm is expected to provide security in adversarial environment for critical mobile CPS.
KEYWORDS: Signal detection, Modulation, Signal to noise ratio, Orthogonal frequency division multiplexing, Receivers, Feature extraction, Signal processing, Library classification systems, Machine learning, Sensors
There is a need for Radio Frequency Signal Classification (RF-Class) toolbox which can monitor, detect, and classify wireless signals. Rapid developments in the unmanned aerial systems (UAS) have made its usage in a variety of offensive as well as defensive applications especially in military, high priority and sensitive government sites. The ability to accurately classify over-the-air radio signals will provide insights into spectrum utilization, device fingerprinting and protocol identification. These insights can help the Warfighter to constantly be informed about adversarys transmitters capabilities without their knowledge. Recently, few researches have proposed feature-based machine learning techniques to classify RF signals. However, these researches are mostly evaluated on simulated environments, less accurate, and failed to explore advance machine learning techniques. In this research, we proposed a feature-engineering based signal classification (RF-class) toolbox which implements RF signal detection, Cyclostationary Features Extraction and Feature engineering, Automatic Modulation Recognition to automatically recognize modulation as well as sub-modulation types of the received signal. To demonstrate the feasibility and accuracy of our approach, we have evaluated the performance on a real environment with an UAS (Drone DJI Phantom 4). Our initial experimental result showed that we were able to detect presence of drone signal successfully when power on and transmitting. And further experiments are under progress.
KEYWORDS: Data storage, Computing systems, Control systems, Data modeling, Data centers, Clouds, Surveillance, Databases, Computer security, Network security, Network architectures, Web services
Ever increasing adoption of cloud technology scales up the activities like creation, exchange, and alteration of cloud data objects, which create challenges to track malicious activities and security violations. Addressing this issue requires implementation of data provenance framework so that each data object in the federated cloud environment can be tracked and recorded but cannot be modified. The blockchain technology gives a promising decentralized platform to build tamper-proof systems. Its incorruptible distributed ledger/blockchain complements the need of maintaining cloud data provenance. In this paper, we present a cloud based data provenance framework using block chain which traces data record operations and generates provenance data. We anchor provenance data records into block chain transactions, which provide validation on provenance data and preserve user privacy at the same time. Once the provenance data is uploaded to the global block chain network, it is extremely challenging to tamper the provenance data. Besides, the provenance data uses hashed user identifiers prior to uploading so the blockchain nodes cannot link the operations to a particular user. The framework ensures that the privacy is preserved. We implemented the architecture on ownCloud, uploaded records to blockchain network, stored records in a provenance database and developed a prototype in form of a web service.
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