Search Results (96)

Cancer treatment has undergone a paradigm shift following the introduction of novel cancer treatment approaches that involve the host’s immune system in fighting established tumors. This new concept aids the immune system in identifying, attacking, and killing the tumor cells. However, although some encouraging results were observed clinically, this approach has its own limitations. For example, the benefits of certain anticancer drugs were only observed in some patients, off-target effects, immune evasion, and poor pharmacokinetics. Recently, several advancements have been made with the understanding and development of tumor-targeted drug delivery systems, which combine both effectiveness and patients’ safety during cancer treatment. In this review, we will focus on the latest progress in targeted drug delivery, particularly applying nanoparticles, liposomes, exosomes, and Wharton’s jelly-derived macrovesicles as immune cell enhancers, as well as overcoming therapeutic resistance. We also characterize major current problems, such as the biocompatibility and scalability of the delivered engineering systems, as well as the required regulations. Lastly, we will show some examples of effective approaches to resolve these issues for more efficient cancer therapy. The importance of this article lies in bridging two sides in a single framework perspective: the novel implementation of unique delivery systems and the latest advances in the field of cancer immunotherapy. Thus, this provides better insights for the future of cancer treatment. Full article

The rapid development of information technology has made cyberspace security an increasingly critical issue. Network intrusion detection methods are practical approaches to protecting network systems from cyber attacks. However, cyberspace security threats have topological dependencies and fine-grained attack semantics. Existing graph-based approaches either underestimate edge-level features or fail to balance detection accuracy with adversarial robustness. To handle these problems, we propose a novel graph neural network–based method for network intrusion detection called the adversarial hierarchical-aware edge attention learning method (AH-EAT). It leverages the natural graph structure of computer networks to achieve robust, multi-grained intrusion detection. Specifically, AH-EAT includes three main modules: an edge-based graph attention embedding module, a hierarchical multi-grained detection module, and an adversarial training module. In the first module, we apply graph attention networks to aggregate node and edge features according to their importance. This effectively captures the network’s key topological information. In the second module, we first perform coarse-grained detection to distinguish malicious flows from benign ones, and then perform fine-grained classification to identify specific attack types. In the third module, we use projected gradient descent to generate adversarial perturbations on network flow features during training, enhancing the model’s robustness to evasion attacks. Experimental results on four benchmark intrusion detection datasets show that AH-EAT achieves 90.73% average coarse-grained accuracy and 1.45% ASR on CIC-IDS2018 under adversarial attacks, outperforming state-of-the-art methods in both detection accuracy and robustness. Full article

Mobile smartphones are prime targets for sophisticated surveillanceware, designed to covertly monitor specific individuals. While mobile operating systems implement various protection mechanisms, their defenses are frequently bypassed due to risky user behaviors or underlying software flaws, leading to persistent successful attacks. This paper addresses the critical research problem of how targeted mobile spyware can be effectively counteracted, particularly given its pervasive and evolving threat amplified by sophisticated evasion techniques. To contribute to this understanding, we comprehensively review mobile surveillanceware variants, namely stalkerware and mercenary spyware. We also critically review mobile OS protection mechanisms, and we detail how surveillanceware bypasses or exploits them. Our analysis reveals that, despite continuous efforts by mobile operating system and device manufacturers, both Android and iOS platforms struggle to protect devices and users, particularly against sophisticated mercenary spyware attacks, remaining vulnerable to these threats. Finally, we systematically review state-of-the-art countermeasures, identify their shortcomings, and highlight unresolved research challenges and concrete directions for future investigation for enhanced prevention and detection. Crucially, this future research must increasingly leverage artificial intelligence, including deep learning and large language models, to effectively keep pace with and overcome the sophisticated tactics employed by modern spyware. Full article

Phishing attacks continue to pose serious challenges to cybersecurity, with attackers constantly refining their methods to bypass detection systems. One particularly evasive technique is Zero-Font phishing, which involves the insertion of invisible or zero-sized characters into email content to deceive both users and traditional email filters. Because these characters are not visible to human readers but still processed by email systems, they can be used to evade detection by traditional email filters, obscuring malicious intent in ways that bypass basic content inspection. This study introduces a proactive phishing detection tool capable of identifying both traditional and Zero-Font phishing attempts. The proposed tool leverages a multi-layered security framework, combining structural inspection and machine learning-based classification to detect both traditional and Zero-Font phishing attempts. At its core, the system incorporates an advanced machine learning model trained on a well-established dataset comprising both phishing and legitimate emails. The model alone achieves an accuracy rate of up to 98.8%, contributing significantly to the overall effectiveness of the tool. This hybrid approach enhances the system’s robustness and detection accuracy across diverse phishing scenarios. The findings underscore the importance of multi-faceted detection mechanisms and contribute to the development of more resilient defenses in the ever-evolving landscape of cybersecurity threats. Full article

With the widespread adoption of deep learning in critical domains, such as computer vision, model security has become a growing concern. Backdoor attacks, as a highly stealthy threat, have emerged as a significant research topic in AI security. Existing backdoor attack methods primarily introduce perturbations in the spatial domain of images, which suffer from limitations, such as visual detectability and signal fragility. Although subsequent approaches, such as those based on steganography, have proposed more covert backdoor attack schemes, they still exhibit various shortcomings. To address these challenges, this paper presents HCBA (high-frequency chroma backdoor attack), a novel backdoor attack method based on high-frequency injection in the UV chroma channels. By leveraging discrete wavelet transform (DWT), HCBA embeds a polarity-triggered perturbation in the high-frequency sub-bands of the UV channels in the YUV color space. This approach capitalizes on the human visual system’s insensitivity to high-frequency signals, thereby enhancing stealthiness. Moreover, high-frequency components exhibit strong stability during data transformations, improving robustness. The frequency-domain operation also simplifies the trigger embedding process, enabling high attack success rates with low poisoning rates. Extensive experimental results demonstrate that HCBA achieves outstanding performance in terms of both stealthiness and evasion of existing defense mechanisms while maintaining a high attack success rate (ASR > 98.5%). Specifically, it improves the PSNR by 25% compared to baseline methods, with corresponding enhancements in SSIM as well. Full article

Chimeric antigen receptor (CAR)-engineered natural killer (NK) cells are a promising platform for off-the-shelf immunotherapy due to their safety advantages over CAR-T cells, including lower risk of graft-versus-host disease, cytokine release syndrome, and neurotoxicity. However, their persistence and efficacy are limited by immunological challenges such as host T-cell-mediated rejection, NK cell fratricide, and macrophage-mediated clearance. This review summarizes gene editing strategies to overcome these barriers, including β2-microglobulin (B2M) knockout and HLA-E overexpression to evade T and NK cell attacks, CD47 overexpression to inhibit phagocytosis, and TIGIT deletion to enhance cytotoxicity. In addition, we discuss functional enhancements such as IL-15 pathway activation, KIR modulation, and transcriptional reprogramming (e.g., FOXO1 knockout) to improve persistence and antitumor activity. We also highlight the role of induced pluripotent stem cell (iPSC)-derived NK platforms, enabling standardized, scalable, and multiplex gene-edited products. Finally, we explore artificial intelligence (AI) applications in immunogenomic profiling and predictive editing to tailor NK cell therapies to patient-specific HLA/KIR/SIRPα contexts. By integrating immune evasion, functional reinforcement, and computational design, we propose a unified roadmap for next-generation CAR-NK development, supporting durable and broadly applicable cell-based therapies. Full article

Pathogenic bacteria of the genus Leptospira are the etiological agents of leptospirosis, a disease that affects humans and animals worldwide. Despite the increasing number of studies, the mechanisms of leptospiral pathogenesis remain poorly comprehended. In this study, we report various interactions of the LigA7’-13’ and LigB1’-7’ domains with host components. The LigA7’-13’ and LigB1’-7’ were cloned into the pET28a vector, and the recombinant proteins were expressed in E. coli C43 (DE3) and E. coli BL21 (DE3), respectively. Both recombinant protein domains were expressed in soluble form and purified using nickel-chelating chromatography. The rLigA7’-13’ and rLigB1’-7’ domains exhibited binding to several types of integrins, with most interactions occurring in a dose-dependent and saturable manner, consistent with the characteristics of typical receptor-ligand interactions. The recombinant domain LigA7’-13’ demonstrated affinity for the glycosaminoglycans (GAGs) chondroitin-4-sulfate, chondroitin sulfate, heparin, chondroitin sulfate B, and heparan sulfate, while no binding was detected for LigB1’-7’ with these molecules. Both rLigA7’-13’ and rLigB1’-7’ interacted with components of the terminal complement pathway and were capable of recruiting C9 from normal human serum (NHS). These interactions may inhibit the formation of polyC9, ultimately preventing the assembly of the membrane attack complex (MAC). Collectively, our data expand the repertoire of host components that interact with rLigA7’-13’ and rLigB1’-7’, opening new avenues for understanding leptospiral immune evasion and broadening the roles of these domains in bacterial virulence. Full article

(1) The primary prostate cancer treatment involves androgen deprivation therapy, with or without chemotherapy. Immunotherapy has emerged as a promising strategy against cancer due to its ability to modulate the immune system, overcome immune evasion, and stimulate the attack on tumor cells. Thus, this review urges an exploration of the underlying mechanisms to validate the efficacy and safety of dendritic cell immunotherapy for prostate cancer treatment. (2) An extensive literature search identified 45 eligible studies in PubMed, Web of Science, SCOPUS, and Embase databases. Phase I and II clinical trials and in vitro studies (PROSPERO registration number CRD42024538296) were analyzed to extract information on patient selection, vaccine preparation, treatment details, and disease progression. (3) Despite significant variability in vaccine development and treatment protocols, vaccines were shown to induce satisfactory immune responses, including T-cell activation, increased CD4 and CD8 cell populations, upregulated expression of HLA-A2 and HLA-DR, enhanced migratory capacity of dendritic cells, and elevated interferon levels. Cytokine responses, particularly involving Interleukin 10 (IL-10) and Interleukin 12 (IL-12), varied across studies. Immunotherapy demonstrated potential by eliciting positive immune responses, reducing PSA levels, and showing an acceptable safety profile. However, side effects such as erythema and fever were observed. (4) The analyzed treatments were well-tolerated, but variability in clinical responses and side effects underscores the need for further research to optimize the efficacy and safety of this therapeutic approach. Full article

Broken access control vulnerabilities pose significant security risks to the protected web interfaces of IoT devices, enabling adversaries to gain unauthorized access to sensitive configurations and even use them as stepping stones for attacking the intranet. Despite its ranking as the first in the latest OWASP Top 10, there remains a lack of effective methodologies to detect these vulnerabilities systematically. We present ACBreaker, a novel methodology powered by a large language model (LLM), to effectively identify broken access control vulnerabilities in the protected web interfaces of IoT devices. Our methodology consists of three stages. The initial stage transforms firmware code that exceeds the LLM context window into semantically intact code snippets. The second stage involves using an LLM to extract device-specific information from firmware code. The final stage integrates this information into the mutation-based fuzzer to improve fuzzing effectiveness and employ differential analysis to identify vulnerabilities. We evaluated ACBreaker across 11 IoT devices, analyzing 1,274,646 lines of code and discovering 39 previously unknown vulnerabilities. We further analyzed these vulnerabilities, categorizing them into three types that contribute to protected interface evasion, and provided mitigation suggestions. These vulnerabilities were responsibly disclosed to vendors, with CVE IDs assigned to those in six IoT devices. Full article

Alveolar Echinococcosis (AE) is a serious zoonotic disease caused by infection of Echinococcus multilocularis larvae. To survive within the host, E. multilocularis has developed a complex immune evasion mechanism including the inhibition of complement activation. This study focused on a calreticulin secreted by E. multilocularis (EmCRT) and its role in binding ability to human MBL and inhibiting MBL-mannose-mediated lectin pathway of complement activation. Results demonstrated the binding of recombinant EmCRT protein to both external and natural MBL in serum and the subsequent inhibition of MBL-mannose-initiated lectin pathway reflected by the reduced formation of complement intermediate products C3b and C4b. Fragment mapping determined that the MBL binding site was located within the S-domain of EmCRT. Combining with its role in inhibiting C1q-initiated classical complement activation in our previous study, the inhibition of MBL-mannose-initiated lectin pathway identified in this study suggests EmCRT plays an important role in the immune evasion of E. multilocularis alveolar larvae against host complement attack as a survival strategy within human tissue. This study supports the approach of using EmCRT as a good candidate for vaccine and drug development against E. multilocularis infection. Full article

Neutrophils, the most abundant white blood cells, play a dual role in cancer progression. While they can promote tumor growth, metastasis, and immune suppression, they also exhibit anti-tumorigenic properties by attacking cancer cells and enhancing immune responses. This review explores the complex interplay between neutrophils and the tumor microenvironment (TME), highlighting their ability to switch between pro- and anti-tumor phenotypes based on external stimuli. Pro-tumorigenic neutrophils facilitate tumor growth through mechanisms such as neutrophil extracellular traps (NETs), secretion of pro-inflammatory cytokines, and immune evasion strategies. They contribute to angiogenesis, tumor invasion, and metastasis by releasing vascular endothelial growth factor (VEGF) and matrix metalloproteinases (MMPs). Conversely, anti-tumor neutrophils enhance cytotoxicity by generating reactive oxygen species (ROS), promoting antibody-dependent cell-mediated cytotoxicity (ADCC), and activating other immune cells such as cytotoxic T lymphocytes (CTLs) and natural killer (NK) cells. Recent advances in neutrophil-based drug delivery systems have harnessed their tumor-homing capabilities to improve targeted therapy. Neutrophil-mimicking nanoparticles and membrane-coated drug carriers offer enhanced drug accumulation in tumors, reduced systemic toxicity, and improved therapeutic outcomes. Additionally, strategies to modulate neutrophil activity, such as inhibiting their immunosuppressive functions or reprogramming them towards an anti-tumor phenotype, are emerging as promising approaches in cancer immunotherapy. Understanding neutrophil plasticity and their interactions with the TME provides new avenues for therapeutic interventions. Targeting neutrophil-mediated mechanisms could enhance existing cancer treatments and lead to the development of novel immunotherapies, ultimately improving patient survival and clinical outcomes. Full article

This paper investigates the impact of different Large Language Models (DeepSeek, Kimi and Doubao) on the attack detection success rate of evasive Spectre attacks while accessing text, image, and code tasks. By running different Large Language Models (LLMs) tasks concurrently with evasive Spectre attacks, a unique dataset with LLMs noise was constructed. Subsequently, clustering algorithms were employed to reduce the dimension of the data and filter out representative samples for the test set. Finally, based on a random forest detection model, the study systematically evaluated the impact of different task types on the attack detection success rate. The experimental results indicate that the attack detection success rate follows the pattern of “code > text > image” in both the evasive Spectre memory attack and the evasive Spectre nop attack. To further assess the influence of different architectures on evasive Spectre attacks, additional experiments were conducted on an NVIDIA RTX 3060 GPU. The results reveal that, on the RTX 3060, the attack detection success rate for code tasks decreased, while those for text and image tasks increased compared to the 2080 Ti. This finding suggests that architectural differences impact the manifestation of Hardware Performance Counters (HPCs), influencing the attack detection success rate. Full article

Incorporating Artificial Intelligence (AI) in healthcare has transformed disease diagnosis and treatment by offering unprecedented benefits. However, it has also revealed critical cybersecurity vulnerabilities in Deep Learning (DL) models, which raise significant risks to patient safety and their trust in AI-driven applications. Existing studies primarily focus on theoretical vulnerabilities or specific attack types, leaving a gap in understanding the practical implications of multiple attack scenarios on healthcare AI. In this paper, we provide a comprehensive analysis of key attack vectors, including adversarial attacks, such as the gradient-based Fast Gradient Sign Method (FGSM), evasion attacks (perturbation-based), and data poisoning, which threaten the reliability of DL models, with a specific focus on breast cancer detection. We propose the Healthcare AI Vulnerability Assessment Algorithm (HAVA) that systematically simulates these attacks, calculates the Post-Attack Vulnerability Index (PAVI), and quantitatively evaluates their impacts. Our findings revealed that the adversarial FGSM and evasion attacks significantly reduced model accuracy from 97.36% to 61.40% (PAVI: 0.385965) and 62.28% (PAVI: 0.377193), respectively, demonstrating their severe impact on performance, but data poisoning had a milder effect, retaining 89.47% accuracy (PAVI: 0.105263). The confusion matrices also revealed a higher rate of false positives in the adversarial FGSM and evasion attacks than more balanced misclassification patterns observed in data poisoning. By proposing a unified framework for quantifying and analyzing these post-attack vulnerabilities, this research contributes to formulating resilient AI models for critical domains where accuracy and reliability are important. Full article

Machine learning (ML) promises advances in automation and threat detection for the future generations of communication networks. However, new threats are introduced, as adversaries target ML systems with malicious data. Adversarial attacks on tree-based ML models involve crafting input perturbations that exploit non-smooth decision boundaries, causing misclassifications. These so-called evasion attacks are imperceptible, as they do not significantly alter the input data distribution and have been shown to degrade the performance of tree-based models across various tasks. Adversarial training and genetic algorithms have been proposed as potential defenses against these attacks. In this paper, we explore the robustness of tree-based models for network intrusion detection systems. This study evaluates an optimization approach inspired by genetic algorithms to generate adversarial samples and studies the impact of adversarial training on the accuracy of attack detection. This paper exposed random forest and extreme gradient boosting classifiers to various adversarial samples generated from communication network-related CIC-IDS2019 and 5G-NIDD datasets. The results indicate that the improvements of robustness to adversarial attacks come with a cost to the accuracy of the network intrusion detection models. These costs can be optimized with intelligent, use case-specific feature engineering. Full article

The growing use of VPNs, proxy servers, and Tor browsers has significantly enhanced online privacy and anonymity. However, these technologies are also exploited by cybercriminals to obscure their identities, posing serious cybersecurity threats. Existing detection methods face challenges in accurately tracing the real IP addresses hidden behind these anonymization tools. This study presents a novel approach to unmasking true identities by leveraging honeypots and Canarytokens to track concealed connections. By embedding deceptive tracking mechanisms within decoy systems, we successfully capture the real IP addresses of users attempting to evade detection. Our methodology was rigorously tested across various network environments and payload types, ensuring effectiveness in real-world scenarios. The findings demonstrate the practicality and scalability of using Canarytokens for IP unmasking, providing a non-intrusive, legally compliant solution to combat online anonymity misuse. This research contributes to strengthening cyber threat intelligence, offering actionable insights for law enforcement, cybersecurity professionals, and digital forensics. Future work will focus on enhancing detection accuracy and addressing the advanced evasion tactics used by sophisticated attackers. Full article