Traffic flow prediction plays an important role in ITS (Intelligent Transportation System). This task is challenging due to the complex spatial and temporal correlations (e.g., constraints of road network law dynamic change with time). Existing work tried solve this problem by exploiting a variety spatiotemporal models. However, we observe that more semantic pair-wise among possibly distant roads are also critical for traffic prediction. To jointly model spatial, temporal, various global...

The booming popularity of smartphones is partly a result application markets where users can easily download wide range third-party applications. However, due to the open nature markets, especially on Android, there have been several privacy and security concerns with these On Google Play, as most other direct access natural-language descriptions those applications, which give an intuitive idea functionality including security-related information Play also provides permissions requested by...

Recent hardware advances have led to the development and consumerization of mobile devices, which mainly include smartphones various wearable devices. To protect privacy users, user authentication mechanisms been proposed. In particular, biometrics has widely used for multi-factor authentication. However, biometrics-based usually require costly sensors deployed on rely explicit input Internet connection performing this article, we propose a system, called RISKCOG, can authenticate ownership...

Advanced Persistent Threat (APT) attacks have caused serious security threats and financial losses worldwide. Various real-time detection mechanisms that combine context information provenance graphs been proposed to defend against APT attacks. However, existing suffer from accuracy efficiency issues due inaccurate models the growing size of graphs. To address issue, we propose a novel accurate model removes unnecessary phases focuses on remaining ones with improved definitions. state-based...

To develop an automated classification system using a machine learning classifier to distinguish clinically unaffected eyes in patients with keratoconus from normal control population based on combination of Scheimpflug camera images and ultra-high-resolution optical coherence tomography (UHR-OCT) imaging data.A total 121 participants were classified by 2 cornea experts into 3 groups: (50 eyes), (38 eyes) or subclinical (33 eyes). All imaged UHR-OCT. Corneal morphological features extracted...

Advanced Persistent Threat (APT) attacks have caused massive financial loss worldwide. Researchers thereby proposed a series of solutions to detect APT attacks, such as dynamic/static code analysis, traffic detection, sandbox technology, endpoint detection and response (EDR), etc. However, existing defenses are failed accurately effectively defend against the current that exhibit strong persistent, stealthy, diverse dynamic characteristics due weak data source integrity, large processing...

To defend against Advanced Persistent Threats on the endpoint, threat hunting employs security knowledge such as cyber intelligence to continuously analyze system audit logs through retrospective scanning, querying, or pattern matching, aiming uncover attack patterns/graphs that traditional detection methods (e.g., recognition for Point of Interest) fail capture. However, existing systems based provenance graphs face challenges high false negatives, positives, and low efficiency when...

Gait authentication, especially sensor-based patterns, has been studied by researchers for decades. Nowadays, gait authentication become an important facet of biometric systems due to the so-called unique characteristics each user. With development various technologies (i.e., hardware, data processing, features extraction, and learning algorithms), performance methods is gradually improving. But we have found that vulnerability most existing can be compromised easily. In this paper, propose...

APTs (Advanced Persistent Threats) have caused serious security threats worldwide. Most existing APT detection systems are implemented based on sophisticated forensic analysis rules. However, the design of these rules requires in-depth domain knowledge and lack generalization ability. On other hand, deep learning technique could automatically create model from training samples with little knowledge. due to persistence, stealth, diversity attacks, suffers a series problems including...

In recent years, PowerShell is increasingly reported to appear in a variety of cyber attacks ranging from advanced persistent threat, ransomware, phishing emails, cryptojacking, financial threats, fileless attacks. However, since the language dynamic by design and can construct script pieces at different levels, state-of-the-art static analysis based attack detection approaches are inherently vulnerable obfuscations. To overcome this challenge, paper we first effective light-weight...

The damage caused by Advanced Persistent Threat (APT) attacks to governments and large enterprises is gradually escalating. Once an attack event detected, forensic analysis will use the dependencies between system audit logs rapidly locate intrusion points determine impact of attacks. Due high persistence APT attacks, huge amounts data be stored meet needs analysis, which not only brings great storage overhead, but also sharply increases computing costs. To compact without affecting several...

Deep learning has made important contributions to classification tasks applied fault diagnosis. However, it is crucial integrate the technologies into real industrial applications through cost-effective hardware. Edge computing, a new computing paradigm, potential accelerate system response time, reduce bandwidth for transmission, and use fewer resources. In this article, distillation quantization compression method based on energy entropy compress convolutional neural network (CNN), which...

Photovoltaic (PV) power generation is greatly affected by meteorological environmental factors, with obvious fluctuations and intermittencies. The large-scale PV grid connection has an impact on the source-load stability of large grid. To scientifically rationally formulate dispatching plan, it necessary to realize output prediction. prediction single plants no longer applicable dispatching. Therefore, demand for multiple in entire region becoming increasingly important. In view drawbacks...

Condition monitoring is used to assess the reliability and equipment efficiency of wind turbines. Feature extraction an essential preprocessing step achieve a high level performance in condition monitoring. However, fluctuating conditions turbines usually cause sudden variations monitored features, which may lead inaccurate prediction maintenance schedule. In this scenario, article proposed novel methodology detect multiple levels faults rolling bearings variable operating conditions. First,...

Power quality disturbances (PQDs) have a large negative impact on electric power systems with the increasing use of sensitive electrical loads. This paper presents novel hybrid algorithm for PQD detection and classification. The proposed method is constructed while using following main steps: computer simulation signals, signal decomposition, feature extraction, heuristic selection selection, First, different types signals are generated by simulation. Second, variational mode decomposition...

With the popularity of smartphones and development hardware, mobile devices are widely used by people. To ensure availability security, how to protect private data in without disturbing users has become a key issue. Mobile user authentication methods based on motion sensors have been proposed many works, but existing series problems such as poor de-noising ability, insufficient availability, low coverage feature extraction. Based shortcomings methods, this paper proposes hybrid deep learning...

Malicious HTTP traffic detection plays an important role in web application security. Most existing work applies machine learning and deep techniques to build the malicious model. However, they still suffer from problems of huge training data collection cost low cross-dataset generalization ability. Aiming at these problems, this paper proposes DeepPTSD, a method for payload based detection. First, it treats as text classification problem trains initial model using TextCNN on public dataset,...

Online social networks (OSNs) are extremely popular among Internet users. However, spam originating from friends and acquaintances not only reduces the joy of surfing but also causes damage to less security-savvy Prior countermeasures combat OSN different angles. Due diversity spam, there is hardly any existing method that can independently detect majority or most spam. In this paper, we empirically analyze textual pattern a large collection An inspiring finding (e.g., 76.4% in 2015)...