Biblio

Web attacks have increased rapidly in recent years. However, traditional methods are useless to track web attackers. Browser fingerprint, as a stateless tracking technique, can be used to solve this problem. Given browser fingerprint changes easily and frequently, it is easy to lose track. Therefore, we need to improve the stability of browser fingerprint by linking the new one to the previous chain. In this paper, we propose LSTM model to learn the potential relationship of browser fingerprint evolution. In addition, we adjust the input feature vector to time series and construct training set to train the model. The results show that our model can construct the tracking chain perfectly well with average ownership up to 99.3%.

Reversible logic is a building block for adiabatic and quantum computing in addition to other applications. Since common functions are non-reversible, one needs to embed them into proper-size reversible functions by adding ancillary inputs and garbage outputs. We explore the Intellectual Property (IP) piracy of reversible circuits. The number of embeddings of regular functions in a reversible function and the percent of leaked ancillary inputs measure the difficulty of recovering the embedded function. To illustrate the key concepts, we study reversible logic circuits designed using reversible logic synthesis tools based on Binary Decision Diagrams and Quantum Multi-valued Decision Diagrams.

Web attacks have proliferated across the whole Internet in recent years. To protect websites, security vendors and researchers collect attack information using web honeypots. However, web attackers can hide themselves by using stepping stones (e.g., VPN, encrypted proxy) or anonymous networks (e.g., Tor network). Conventional web honeypots lack an effective way to gather information about an attacker's identity, which raises a big obstacle for cybercrime traceability and forensics. Traditional forensics methods are based on traffic analysis; it requires that defenders gain access to the entire network. It is not suitable for honeypots. In this paper, we present the design, implementation, and deployment of the Micro-Honeypot, which aims to use the browser fingerprinting technique to track a web attacker. Traditional honeypot lure attackers and records attacker's activity. Micro-Honeypot is deployed in a honeypot. It will run and gather identity information when an attacker visits the honeypot. Our preliminary results show that Micro-Honeypot could collect more information and track attackers although they might have used proxies or anonymous networks to hide themselves.

The outsourcing for fabrication introduces security threats, namely hardware Trojans (HTs). Many design-for-trust (DFT) techniques have been proposed to address such threats. However, many HT detection techniques are not effective due to the dependence on golden chips, limitation of useful information available and process variations. In this paper, we data-mine on path delay information and propose a variation-tolerant path delay order encoding technique to detect HTs.

Barrier coverage has been widely adopted to prevent unauthorized invasion of important areas in sensor networks. As sensors are typically placed outdoors, they are susceptible to getting faulty. Previous works assumed that faulty sensors are easy to recognize, e.g., they may stop functioning or output apparently deviant sensory data. In practice, it is, however, extremely difficult to recognize faulty sensors as well as their invalid output. We, in this paper, propose a novel fault-tolerant intrusion detection algorithm (TrusDet) based on trust management to address this challenging issue. TrusDet comprises of three steps: i) sensor-level detection, ii) sink-level decision by collective voting, and iii) trust management and fault determination. In the Step i) and ii), TrusDet divides the surveillance area into a set of fine- grained subareas and exploits temporal and spatial correlation of sensory output among sensors in different subareas to yield a more accurate and robust performance of barrier coverage. In the Step iii), TrusDet builds a trust management based framework to determine the confidence level of sensors being faulty. We implement TrusDet on HC- SR501 infrared sensors and demonstrate that TrusDet has a desired performance.