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With the rapid development of Internet technology and its application, the existence of network vulnerabilities is very common. Attackers may use the defects of software, hardware, or system security policy in the network system to access or destroy the system without authorization. How to nip in the bud and carry out a safety risk assessment and early warning is an urgent problem to be solved. Based on the overall assessment of the risk factors in the whole network, the more dangerous nodes are found and priority measures are taken. The method proposed in this paper can reflect and predict the actions of attackers, repair, and adjust the previously predicted probability. It is compared with the method that evaluates the uncertainty in the network solely by calculating the static probability. The proposed new ideas and methods better reflect the real-time changes in the actual environment of the Internet, thereby better responding to the actual situation. This method can be well applied to threat detection, threat analysis, and risk assessment of monitoring system networks, enabling monitoring network managers to evaluate and protect the security of real-time power grids. It is of great significance to effectively defend against network attacks, ensure system security, and study the resistance of control systems under network attacks.

Purpose: While radiotherapy remains the leading clinical treatment for many tumors, its efficacy can be significantly hampered by the insensitivity of cells in the S phase of the cell cycle to such irradiation. Methods: Here, we designed a highly targeted drug delivery platform in which exosomes were loaded with the FDA-approved anti-tumor drug camptothecin (CPT) which is capable of regulating cell cycle. The utilized exosomes were isolated from patient tumors, enabling the personalized treatment of individuals to ensure better therapeutic outcomes. Results: This exosome-mediated delivery strategy was exhibited robust targeted to patient-derived tumor cells in vitro and in established patient-derived xenograft models. By delivering CPT to tumor cells, this nanoplatform was able to decrease cell cycle arrest in the S phase, increasing the frequency of cells in the G1 and G2/M phases such that they were more radiosensitive. Conclusion: This therapeutic approach was able to substantially enhance the sensitivity of patient-derived tumors to ionizing radiation, thereby improving the overall efficacy of radiotherapy without the need for a higher radiation dose.

PurposeDepression and anxiety’s impact on glioma patient survival lacks consensus. Understanding these effects can highlight the importance of identifying depression and anxiety in glioma patients, and inform future treatments. This systematic review and meta-analysis aims to clarify the impact of depression and anxiety on glioma patient survival.MethodsWe conducted a systematic literature search of major databases, including PubMed, Embase, Web of Science Core Collection, Cochrane Library, and PsycINFO, from inception to June 2023, to identify relevant studies. Eligible studies were those that examined the association between depression, anxiety, or both, and survival outcomes in glioma patients. Data were extracted and analyzed using fixed-effects meta-analysis models to calculate pooled hazard ratios (HRs) and 95% confidence intervals (CIs).ResultsA total of 15 studies met the inclusion criteria, encompassing a diverse range of glioma patients across different clinical settings and stages. The meta-analysis revealed a statistically significant association between depression and reduced overall survival in glioma patients, with a pooled HR of 1.65 (95% CI: 1.41–1.83, 11 studies). The preliminary univariate meta-regression results indicate no impact of individual study characteristics on the effect size. Likewise, anxiety was associated with worse overall survival, with a pooled HR of 1.65 (95% CI: 1.18–2.31, 5 studies).ConclusionsThis meta-analysis underscores the vital need to identify and treat depression and anxiety in glioma patients. Future research should explore the underlying mechanisms, aiding the creation of interventions enhancing both mental health and clinical outcomes for this vulnerable group.

As an important component of the smart grid, vehicle-to-grid (V2G) networks can deliver diverse auxiliary services and enhance the overall resilience of electrical power systems. However, V2G networks face two main challenges due to a large number of devices that connect to it. First, V2G networks suffer from serious security threats, such as doubtful authenticity and privacy leakage. Second, the efficiency will decrease significantly due to the massive requirements of authentication. To tackle these problems, this paper proposes a cross-domain authentication scheme for V2G networks based on consortium blockchain and certificateless signature technology. Featuring decentralized, open, and transparent transactions that cannot be tampered with, this scheme achieves good performance on both security and efficiency, which proves to be suitable for V2G scenarios in the smart grid.

To address the issue of low accuracy in detecting malicious program behaviors in new power system edge-side applications, we present a detection model based on API call sequences that combines rule matching and deep learning techniques in this paper. We first use the PrefixSpan algorithm to mine frequent API call sequences in different threads of the same program within a malicious program dataset to create a rule base for malicious behavior sequences. The API call sequences to be examined are then matched using the malicious behavior sequence matching model, and those that do not match are fed into the TextCNN deep learning detection model for additional detection. The two models collaborate to accomplish program behavior detection. Experimental results demonstrate that the proposed detection model can effectively identify malicious samples and discern malicious program behaviors.

Objective Supplementary motor area (SMA) syndrome is a common complication after SMA glioma resection. The compensatory mechanism of the structural sensorimotor network (SMN) and the factors influencing the recovery time of SMA syndrome have not been investigated. Methods Pre‐ and postoperative diffusion tensor images of 42 low‐grade glioma patients with SMA syndrome were processed to construct white matter connectomes. Patients were classified into fast and slow‐recovery groups according to whether postoperative motor disorder recovers within 7 days. Fiber counts between nodes and graph theory topological properties were calculated. The shortest distance from the surgical region to the corticospinal tract (dCST) and the upper limb region of Brodmann area 4 (A4ul) was measured to find correlations with recovery time. Cox regressions were conducted to identify factors associated with SMA syndrome recovery time. A general linear model was formed using significant factors in multivariate Cox analysis to predict recovery time. Results Decrease of fiber number between lesioned‐hemispheric A4ul and contralateral SMN is correlated with prolongation of recovery time. Compared with the slow‐recovery group, a higher increase of nodal degree centrality and nodal efficiency of ipsilateral A4ul was found in the fast‐recovery group (nodal efficiency: left pre‐op: 0.182 ± 0.009, left post‐op: 0.231 ± 0.008, p < 0.0001; right pre‐op: 0.157 ± 0.021, right post‐op: 0.195 ± 0.018, p = 0.0011); (nodal degree centrality: left pre‐op: 1.985 ± 0.166; left post‐op: 3.195 ± 0.230, p < 0.0001; right pre‐op: 1.620 ± 0.389; right post‐op: 2.411 ± 0.452, p = 0.0005). Multivariate Cox analysis indicated that the increase in nodal efficiency of A4ul and dCST were protective factors for SMA syndrome recovery time. A significant negative correlation between the predict score and recovery time was found in the left lesion group (r = −0.756, p < 0.0001), and the same trend was found in the right lesion group (r = −0.531, p = 0.076). Conclusions This study revealed an increase in lesioned‐hemispheric A4ul nodal efficiency and long dCST as protective factors in SMA syndrome recovery. A decrease in the number of interhemispheric fibers connecting lesioned‐hemispheric A4ul to nodes on the contralateral hemisphere was correlated with the long recovery time of SMA syndrome. Analysis of the structural network of the patients with SMA glioma revealed that the ipsilateral upper limb region of Brodmann area 4 (A4ul) and the surgical region to the corticospinal tract (CST) were related to SMA syndrome recovery time.

In the process of gradually expanding the scale of computer networks and the design of network systems becoming more and more complex, people pay more and more attention to the construction of network security protection systems. Starting from the blockchain encryption technology, the article establishes the authentication and access management key based on the elliptic curve encryption algorithm and combines the maximum entropy model with the hidden Markov model to construct the MEMM for intrusion detection of network security. Based on the effective signal-to-noise ratio model of the network channel, an adaptive channel selection strategy based on the UCB algorithm is proposed. The IoT security prevention system is built based on IoT technology, and each functional module of the system is designed. The system’s authentication security, network intrusion detection, adaptive channel selection, and concurrency performance were tested after the design was completed. The encryption operation time of the ECC algorithm was improved by 41.53% compared to the RSA algorithm, the average time of the MEMM network intrusion detection was 41.54ms, and the false alarm rate of the intrusion detection was kept below 16.5%. The average packet collection rate of the nodes in the adaptive channel selection algorithm is 90.98%. The maximum system throughput is up to 62.19MB, and the extreme difference in data volume between different nodes is only 38 entries. Constructing a network security prevention system based on IoT technology and combining multiple encryption techniques can ensure the secure transmission of network data.

With the technological progress of the Internet and 5G communication network, more and more Internet of Things devices are used in it. Limited by the cost, power consumption and other factors of Internet of Things devices, the systems carried by the Internet of Things devices often lack the security protection provided by larger equipment systems such as desktop computers. Because the current personal computers and servers mostly use the x86 architecture, and the previous research on security tools or hardware-based security analysis feature support is mostly based on the x86 architecture, the traditional security analysis techniques cannot be applied to the current large-scale ARM-based and MIPS-based Internet of Things devices. Based on this, this paper studies the firmware binary program of common Linux-based Internet of Things devices. A binary static instrumentation technology based on taint information analysis is proposed. The paper also analyzes how to use the binary static instrumentation technology combined with static analysis results to rewrite binary programs and obtain taint path information when binary programs are executed. Firmware binary fuzzing technology based on model constraints and path feedback is studied to cover more dangerous execution paths in the target program. Finally, iootfuzzer, a prototype vulnerability mining system for firmware binaries of Internet of Things devices, is used to test and analyze the two technologies. The results show that its fuzzing efficiency for Internet of Things devices is better than other fuzzing technologies such as boofuzz and Peach 3. It can fill in some gaps in the current security analysis tools for the Internet of Things devices and improve the efficiency of security analysis for Internet of Things devices, which contributes to the field through automated security vulnerability detection systems.

With the development of computer technology and communication technology, computer network will increasingly become an important means of information exchange, and permeate into every field of social life. However, the network has the potential threat and the reality existence each kind of security question, therefore we must take the strong security policy to ensure the network security. The purpose of this paper is to study the network computer security hidden trouble and vulnerability mining technology. In this paper, the types of security hidden dangers are analyzed, and the vulnerability detection technology Fuzzing technology is deeply studied. Then, the inspection and test time is analyzed for the existing vulnerability detection tools. The experimental results prove that vulnerability detection technology can protect network security with high efficiency of vulnerability detection. In this paper, three vulnerability detection tools WS Fuzzer, Web Fuzz and Webvul Scan were used to analyze the detection time of open source system, personal blog, shopping mall and forum. The average detection time was 1.9s, 8.7s, 20.5s and 59.7s, respectively. It can be seen that the vulnerability mining technology has a certain practical role.