Biblio

The security of manycore systems has become increasingly critical. In system-on-chips (SoCs), Hardware Trojans (HTs) manipulate the functionalities of the routing components to saturate the on-chip network, degrade performance, and result in the leakage of sensitive data. Existing HT detection techniques, including runtime monitoring and state-of-the-art learning-based methods, are unable to timely and accurately identify the implanted HTs, due to the increasingly dynamic and complex nature of on-chip communication behaviors. We propose AGAPE, a novel Generative Adversarial Network (GAN)-based anomaly detection and mitigation method against HTs for secured on-chip communication. AGAPE learns the distribution of the multivariate time series of a number of NoC attributes captured by on-chip sensors under both HT-free and HT-infected working conditions. The proposed GAN can learn the potential latent interactions among different runtime attributes concurrently, accurately distinguish abnormal attacked situations from normal SoC behaviors, and identify the type and location of the implanted HTs. Using the detection results, we apply the most suitable protection techniques to each type of detected HTs instead of simply isolating the entire HT-infected router, with the aim to mitigate security threats as well as reducing performance loss. Simulation results show that AGAPE enhances the HT detection accuracy by 19%, reduces network latency and power consumption by 39% and 30%, respectively, as compared to state-of-the-art security designs.

As a large amount of renewable energy is injected into the power grid, the source side of the power grid becomes extremely uncertain. Traditional static safety analysis methods based on pure physical models can no longer quickly and reliably give analysis results. Therefore, this paper proposes a deep learning-based static security analytical method. First, the static security assessment index of the power grid under the N-1 principle is proposed. Secondly, a neural network model and its input and output data for static safety analysis problems are designed. Finally, the validity of the proposed method was verified by IEEE grid data. Experiments show that the proposed method can quickly and accurately give the static security analysis results of the source-side high uncertainty grid.

Data are the basis in Digital Twin (DT) to set up bidirectional mapping between physical and virtual spaces, and realize critical environmental sensing, decision making and execution. Thus, trustworthiness is a necessity in data content as well as data operations. A dual blockchain framework is proposed to realize comprehensive data security in various DT scenarios. It is highly adaptable, scalable, evolvable, and easy to be integrated into Digital Twin Network (DTN) as enhancement.

Regular expression denial of service (ReDoS)— which exploits the super-linear running time of matching regular expressions against carefully crafted inputs—is an emerging class of DoS attacks to web services. One challenging question for a victim web service under ReDoS attacks is how to quickly recover its normal operation after ReDoS attacks, especially these zero-day ones exploiting previously unknown vulnerabilities.In this paper, we present RegexNet, the first payload-based, automated, reactive ReDoS recovery system for web services. RegexNet adopts a learning model, which is updated constantly in a feedback loop during runtime, to classify payloads of upcoming requests including the request contents and database query responses. If detected as a cause leading to ReDoS, RegexNet migrates those requests to a sandbox and isolates their execution for a fast, first-measure recovery.We have implemented a RegexNet prototype and integrated it with HAProxy and Node.js. Evaluation results show that RegexNet is effective in recovering the performance of web services against zero-day ReDoS attacks, responsive on reacting to attacks in sub-minute, and resilient to different ReDoS attack types including adaptive ones that are designed to evade RegexNet on purpose.

In recent years, with the continuous development of various new Internet technologies, big data, cloud computing and other technologies have been widely used in work and life. The further improvement of data scale and computing capability has promoted the breakthrough development of artificial intelligence technology. The generalization and classification of financial science and technology not only have a certain impact on the traditional financial business, but also put forward higher requirements for commercial banks to operate financial science and technology business. Artificial intelligence brings fresh experience to financial services and is conducive to increasing customer stickiness. Artificial intelligence technology helps the standardization, modeling and intelligence of banking business, and helps credit decision-making, risk early warning and supervision. This paper first discusses the influence of artificial intelligence on financial innovation, and on this basis puts forward measures for the innovation and development of bank financial science and technology. Finally, it discusses the problem of computer information security management in bank financial innovation in the era of artificial intelligence.

We present TendrilStaller, an eclipse attack targeting at Bitcoin's peer-to-peer network. TendrilStaller enables an adversary to delay block propagation to a victim for 10 minutes. The adversary thus impedes the victim from getting the latest blockchain state. It only takes as few as one Bitcoin full node and two light weight nodes to perform the attack. The light weight nodes perform a subset of the functions of a full Bitcoin node. The attack exploits a recent block propagation protocol introduced in April 2016. The protocol prescribes a Bitcoin node to select 3 neighbors that can send new blocks unsolicited. These neighbors are selected based on their recent performance in providing blocks quickly. The adversary induces the victim to select 3 attack nodes by having attack nodes send valid blocks to the victim more quickly than other neighbors. For this purpose, the adversary deploys a handful of light weight nodes so that the adversary itself receives new blocks faster. The adversary then performs the attack to delay blocks propagated to the victim. We implement the attack on top of current default Bitcoin protocol We deploy the attack nodes in multiple locations around the globe and randomly select victim nodes. Depending on the round-trip time between the adversary and the victim, 50%-85% of the blocks could be delayed to the victim. We further show that the adoption of light weight nodes greatly increases the attack probability by 15% in average. Finally, we propose several countermeasures to mitigate this eclipse attack.