Visible to the public Biblio

Filters: Author is Njilla, L.  [Clear All Filters]
2019-09-09
Zhang, Z., Yu, Q., Njilla, L., Kamhoua, C..  2018.  FPGA-oriented moving target defense against security threats from malicious FPGA tools. 2018 IEEE International Symposium on Hardware Oriented Security and Trust (HOST). :163–166.
The imbalance relationship between FPGA hardware/software providers and FPGA users challenges the assurance of secure design on FPGAs. Existing efforts on FPGA security primarily focus on reverse engineering the downloaded FPGA configuration, retrieving the authentication code or crypto key stored on the embedded memory in FPGAs, and countermeasures for the security threats above. In this work, we investigate new security threats from malicious FPGA tools, and identify stealthy attacks that could occur during FPGA deployment. To address those attacks, we exploit the principles of moving target defense (MTD) and propose a FPGA-oriented MTD (FOMTD) method. Our method is composed of three defense lines, which are formed by an improved user constraint file, random selection of design replicas, and runtime submodule assembling, respectively. The FPGA emulation results show that the proposed FOMTD method reduces the hardware Trojan hit rate by 60% over the baseline, at the cost of 10.76% more power consumption.
2019-06-10
Kornish, D., Geary, J., Sansing, V., Ezekiel, S., Pearlstein, L., Njilla, L..  2018.  Malware Classification Using Deep Convolutional Neural Networks. 2018 IEEE Applied Imagery Pattern Recognition Workshop (AIPR). :1-6.

In recent years, deep convolution neural networks (DCNNs) have won many contests in machine learning, object detection, and pattern recognition. Furthermore, deep learning techniques achieved exceptional performance in image classification, reaching accuracy levels beyond human capability. Malware variants from similar categories often contain similarities due to code reuse. Converting malware samples into images can cause these patterns to manifest as image features, which can be exploited for DCNN classification. Techniques for converting malware binaries into images for visualization and classification have been reported in the literature, and while these methods do reach a high level of classification accuracy on training datasets, they tend to be vulnerable to overfitting and perform poorly on previously unseen samples. In this paper, we explore and document a variety of techniques for representing malware binaries as images with the goal of discovering a format best suited for deep learning. We implement a database for malware binaries from several families, stored in hexadecimal format. These malware samples are converted into images using various approaches and are used to train a neural network to recognize visual patterns in the input and classify malware based on the feature vectors. Each image type is assessed using a variety of learning models, such as transfer learning with existing DCNN architectures and feature extraction for support vector machine classifier training. Each technique is evaluated in terms of classification accuracy, result consistency, and time per trial. Our preliminary results indicate that improved image representation has the potential to enable more effective classification of new malware.

2018-05-24
Tosh, D. K., Shetty, S., Liang, X., Kamhoua, C. A., Kwiat, K. A., Njilla, L..  2017.  Security Implications of Blockchain Cloud with Analysis of Block Withholding Attack. 2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID). :458–467.

The blockchain technology has emerged as an attractive solution to address performance and security issues in distributed systems. Blockchain's public and distributed peer-to-peer ledger capability benefits cloud computing services which require functions such as, assured data provenance, auditing, management of digital assets, and distributed consensus. Blockchain's underlying consensus mechanism allows to build a tamper-proof environment, where transactions on any digital assets are verified by set of authentic participants or miners. With use of strong cryptographic methods, blocks of transactions are chained together to enable immutability on the records. However, achieving consensus demands computational power from the miners in exchange of handsome reward. Therefore, greedy miners always try to exploit the system by augmenting their mining power. In this paper, we first discuss blockchain's capability in providing assured data provenance in cloud and present vulnerabilities in blockchain cloud. We model the block withholding (BWH) attack in a blockchain cloud considering distinct pool reward mechanisms. BWH attack provides rogue miner ample resources in the blockchain cloud for disrupting honest miners' mining efforts, which was verified through simulations.

2018-05-09
Bobda, C., Whitaker, T. J. L., Kamhoua, C., Kwiat, K., Njilla, L..  2017.  Synthesis of Hardware Sandboxes for Trojan Mitigation in Systems on Chip. 2017 IEEE International Symposium on Hardware Oriented Security and Trust (HOST). :172–172.

In this work, we propose a design flow for automatic generation of hardware sandboxes purposed for IP security in trusted system-on-chips (SoCs). Our tool CAPSL, the Component Authentication Process for Sandboxed Layouts, is capable of detecting trojan activation and nullifying possible damage to a system at run-time, avoiding complex pre-fabrication and pre-deployment testing for trojans. Our approach captures the behavioral properties of non-trusted IPs, typically from a third-party or components off the shelf (COTS), with the formalism of interface automata and the Property Specification Language's sequential extended regular expressions (SERE). Using the concept of hardware sandboxing, we translate the property specifications to checker automata and partition an untrusted sector of the system, with included virtualized resources and controllers, to isolate sandbox-system interactions upon deviation from the behavioral checkers. Our design flow is verified with benchmarks from Trust-Hub.org, which show 100% trojan detection with reduced checker overhead compared to other run-time verification techniques.

2018-04-02
Cheng, Q., Kwiat, K., Kamhoua, C. A., Njilla, L..  2017.  Attack Graph Based Network Risk Assessment: Exact Inference vs Region-Based Approximation. 2017 IEEE 18th International Symposium on High Assurance Systems Engineering (HASE). :84–87.

Quantitative risk assessment is a critical first step in risk management and assured design of networked computer systems. It is challenging to evaluate the marginal probabilities of target states/conditions when using a probabilistic attack graph to represent all possible attack paths and the probabilistic cause-consequence relations among nodes. The brute force approach has the exponential complexity and the belief propagation method gives approximation when the corresponding factor graph has cycles. To improve the approximation accuracy, a region-based method is adopted, which clusters some highly dependent nodes into regions and messages are passed among regions. Experiments are conducted to compare the performance of the different methods.

2018-03-19
Kamdem, G., Kamhoua, C., Lu, Y., Shetty, S., Njilla, L..  2017.  A Markov Game Theoritic Approach for Power Grid Security. 2017 IEEE 37th International Conference on Distributed Computing Systems Workshops (ICDCSW). :139–144.

The extensive use of information and communication technologies in power grid systems make them vulnerable to cyber-attacks. One class of cyber-attack is advanced persistent threats where highly skilled attackers can steal user authentication information's and then move laterally in the network, from host to host in a hidden manner, until they reach an attractive target. Once the presence of the attacker has been detected in the network, appropriate actions should be taken quickly to prevent the attacker going deeper. This paper presents a game theoretic approach to optimize the defense against an invader attempting to use a set of known vulnerabilities to reach critical nodes in the network. First, the network is modeled as a vulnerability multi-graph where the nodes represent physical hosts and edges the vulnerabilities that the attacker can exploit to move laterally from one host to another. Secondly, a two-player zero-sum Markov game is built where the states of the game represent the nodes of the vulnerability multi-graph graph and transitions correspond to the edge vulnerabilities that the attacker can exploit. The solution of the game gives the optimal strategy to disconnect vulnerable services and thus slow down the attack.

2017-12-20
Dutta, R. G., Guo, Xiaolong, Zhang, Teng, Kwiat, K., Kamhoua, C., Njilla, L., Jin, Y..  2017.  Estimation of safe sensor measurements of autonomous system under attack. 2017 54th ACM/EDAC/IEEE Design Automation Conference (DAC). :1–6.
The introduction of automation in cyber-physical systems (CPS) has raised major safety and security concerns. One attack vector is the sensing unit whose measurements can be manipulated by an adversary through attacks such as denial of service and delay injection. To secure an autonomous CPS from such attacks, we use a challenge response authentication (CRA) technique for detection of attack in active sensors data and estimate safe measurements using the recursive least square algorithm. For demonstrating effectiveness of our proposed approach, a car-follower model is considered where the follower vehicle's radar sensor measurements are manipulated in an attempt to cause a collision.