Journal of Network and Computer Applications最新文献

Skin lesions encompass various skin conditions, including cancerous growths resulting from uncontrolled proliferation of skin cells. Globally, this disease affects a significant portion of the population, with millions of fatalities recorded. Over the past three decades, there has been a concerning escalation in diagnosed cases of skin cancer. Early detection is crucial for effective treatment, as late diagnosis significantly heightens mortality risk. Existing research often focuses on either handcrafted or deep features, neglecting the diverse textural and structural properties inherent in skin lesion images. Additionally, reliance on a single optimizer in CNN-based schemes poses efficiency challenges. To tackle these issues, this paper presents two novel approaches for classifying skin lesions in dermoscopic images to assess cancer severity. The first approach enhances classification accuracy by leveraging a modified VGG-16 network and employing both RMSProp and Adam optimizers. The second approach introduces a Hybrid CNN Model, integrating deep features from the modified VGG-16 network with handcrafted color and multi-directional texture features. Color features are extracted using a non-uniform cumulative probability-based histogram method, while texture features are derived from a 45${}^{\circ }$ rotated complex wavelet filter-based dual-tree complex wavelet transform. The amalgamated features facilitate accurate prediction of skin lesion classes. Evaluation on ISIC 2017 skin cancer classification challenge images demonstrates significant performance enhancements over existing techniques.

The capabilities of accurate prediction of cyberattacks have long been desired as detection methods cannot avoid the damages caused by occurrences of cyberattack. Attack prediction still remains an open issue especially to specify the upcoming steps of an attack with the quickly evolving intelligent techniques at the attackers’ side. This study proposes a composite learning approach (namely CL-AP${}^2$), which fulfills this task in two phases of “attack portraying” and “attack prediction”: (1) (Attack Portraying) CL-AP${}^2$ generates a Temporal Attack Knowledge Graph (TAKG) from real-time system logs providing full knowledge that formulates time-aware entities related to attacks and the relations amongst them; Over the TAKG, a Tactic-based Cyber Kill Chain (TCKC) model highlights the attacker’s portrait via evaluation of behaviors in the past, i.e., presenting the tactical path and attack steps taken by the attacker; (2) (Attack Prediction) The Soft Actor–Critic algorithm applies to identify the most possible attack trajectory confined in the attack portrait; The transformer model finally derives the specific attack technique to be taken next.

Experiments have been performed versus the state-of-the-art counterparts over a public dataset and results indicate that: (1) CL-AP${}^2$ can effectively reveal the tactical path taken by the attacker and form a complete portrait of the attack; and (2) CL-AP${}^2$ excels in predicting attack techniques to be taken by attackers and providing the defense guidance against the predicted attacks.

In real-time application domains, like finance, healthcare and defence, delay in service or stealing information may lead to unrecoverable consequences. So, early detection of intrusion is important to prevent security breaches. In recent days, anomaly-based intrusion detection using Hybrid Deep Learning approaches are becoming more popular. The most used benchmark datasets in the literature are NSL-KDD and UNSW-NB15, and these datasets are imbalanced. The models built on imbalanced datasets may lead to biased results towards majority classes by neglecting the minority class, even though they are equally important. In many cases, high accuracy is achieved for majority classes in the imbalanced datasets. But, the class-level performances are poor with respect to the minority class. The class balancing will also play an important role in attenuating the bias in prediction for imbalanced datasets. In this paper, a Hybrid Deep Learning Based Intrusion Detection (HDLBID) framework is proposed with CNN-BiLSTM combination. The four techniques, namely, Random Oversampling (ROS), ADASYN, SMOTE, and SMOTE-Tomek, are used for class balancing in the proposed HDLBID framework. The proposed HDLBID with SMOTE-Tomek achieves an overall accuracy of 99.6% with NSL-KDD and 89.02% for UNSW-NB15. It results in an improvement of 13.67% for NSL-KDD and 10.62% for UNSW-NB15 over the existing recent related models. In the proposed HDLBID, in addition to overall accuracy, the class-level $F_1$ score is also calculated. A comparative study is presented to show the effectiveness of balancing dataset compared to imbalanced dataset, and observed that the SMOTE-Tomek class balancing comparatively performed well. An improvement of 37.43% is observed in the U2R class of the NSL-KDD dataset and 61.65% improvement is seen in the Worms class of the UNSW-NB15 dataset, both with SMOTE-Tomek class balancing. Therefore, the proposed HDLBID with SMOTE-Tomek class balancing reports the best results in terms of overall accuracy compared to existing recent related approaches. Also, in terms of class-level analysis, HDLBID reports best results with SMOTE-Tomek over imbalanced version of datasets.

With the rising of Internet in early ’90s, many fraudulent activities have migrated from physical to digital: one of them is phishing. Phishing is a deceptive practice focused on exploiting the human factor, which is the most vulnerable aspect of any security process. In this scam, social engineering techniques are extensively utilized, specifically focusing on the principles of persuasion, to deceive individuals into disclosing sensitive information or engaging in malicious actions. This research explores the use of message subjectivity for detecting phishing attacks. It does so by assessing the impact of various data representations and classifiers on automatically identifying principles of persuasion. Furthermore, it investigates how these detected principles of persuasion can be leveraged for identifying phishing attacks. The experiments conducted revealed that there is no universal solution for data representation and classifier selection to effectively detect all principles of persuasion. Instead, a tailored combination of data representation and classifiers is required for detecting each principle. The Machine Learning models created automatically detect principles of persuasion with confidence levels ranging from 0.7306 to 0.8191 for AUC-ROC. Next, principles of persuasion detected are used for phishing detection. This study also emphasizes the need for user-friendly and comprehensible models. To validate the proposal presented, several families of classifiers were tested, but among all of them, tree-based models (and Random Forest in particular) stand out as preferred option. These models achieve similar level of effectiveness as alternative methods while offering improved clarity and user-friendliness, with an AUC-ROC of 0.859842.

The popularity of commercial content delivery applications has led to dissension among three embroiled parties: Content Service Providers (CSPs), Internet Service Providers (ISPs), and End Users (EUs). This dissension is not only a technical problem but an economic problem. To harmonize this dissension, this paper takes live streaming as a typical content delivery application. It proposes a profitable system model that enables all three parties to enlarge their benefits with the help of a prevalent content delivery architecture integrated with edge caching and traffic engineering technologies. Specifically, the interactions among CSPs, ISPs, and EUs are modeled as a tripartite game where more and more ISPs and CSPs are involved in the market. A pricing scheme is introduced to capture the application features. The tripartite game is studied in different market scenarios and a dynamic three-stage Stackelberg game is proposed that is combined with the Cournot game that characterizes the interdependent, interactive, and competitive relationship among the three parties. Moreover, how the market competition motivates ISPs to upgrade the cache service infrastructure is further investigated. The theoretical analysis and empirical study show that the model can result in a win-win-win outcome.

Satellite-assisted internet of things (IoT) networks have emerged as a beacon of promise, offering global coverage and uninterrupted connectivity. However, the challenges of resource allocation and task offloading in such networks are intricate due to the unique characteristics of satellite communication systems. This research’s findings enrich the landscape of energy-efficient and dependable satellite-assisted IoT networks. The paper navigates the delicate balance between energy efficiency, network throughput, and fairness in distributing resources among IoT devices. The proposed techniques, notably the Outer Approximation Algorithm (OAA), usher in seamless connectivity and resource optimization. The central challenge at hand, a concave fractional programming problem, transforms through the Charnes–Cooper transformation, presenting as a concave optimization enigma. Herein, the proposed outer approximation algorithm takes flight, navigating the intricate paths of concave optimization. The performance of the epsilon-optimal solution faces scrutiny under diverse system parameters—the constellation of IoT devices, their affiliations, fairness considerations, and the equitable distribution of resource blocks. This contribution not only enriches research but also opens doors to the boundless possibilities of satellite-assisted IoT networks.

The generation of trajectory data has increased dramatically with the advent and widespread use of GPS-enabled devices. This rich source of data provides invaluable insights for various applications such as traffic optimization, urban planning, crowd management, and public safety. However, the increasing demand for the publication and sharing of trajectory data for big data analytics raises significant privacy concerns due to the sensitive nature of the location information embedded in the trajectory data. Privacy-preserving trajectory publishing (PPTP) has been an active research area to address these concerns, and synthetic trajectory generation has emerged as a promising direction within PPTP. This survey paper provides a comprehensive overview of PPTP with a focus on synthetic trajectory generation methods, which have been insufficiently covered in previous surveys. Our contributions include a comparison of existing PPTP techniques based on their applicability and effectiveness for data analysis tasks. We then review and discuss the existing work on synthetic trajectory generation in the context of PPTP. Specifically, we classify the existing studies into two main categories, algorithm-based and deep learning-based approaches, and within each category, we perform a comparative analysis of the studied methods, focusing on their different characteristics. Finally, in order to encourage further research in this area, we identify and highlight a number of promising directions for future investigation that deserve to be explored in greater depth.

Named Data Networking (NDN) is a promising Internet architecture that aims to supersede the current IP-based architecture and shift the host-centric model to a data-centric one. Within NDN, forwarding Interest packets remains a significant challenge and has attracted considerable recent research attention. The momentum behind machine learning techniques, especially reinforcement learning, is steadily growing, offering the potential to deliver intelligent, adaptable, and reliable NDN forwarding algorithms. In this context, this paper proposes efficient NDN forwarding strategies based on Contextual Multi-Armed Bandit (CMAB). Initially, we employ CMAB to address the challenge of forwarding Interest packets and introduce a new CMAB model tailored for NDN, dubbed CMAB4NDN. Subsequently, we construct the CMAB context using information derived from the content name and the network congestion state, which are then fed into the CMAB4NDN learning algorithm to decide on the best forwarding action. Further, we develop three CMAB strategies, namely Lin-$\varepsilon$-Greedy, Linear Upper Confidence Bound, and Contextual Thompson Sampling, and deploy them within our proposal. CMAB4NDN was implemented in ndnSIM, thoroughly evaluated, and compared with multiple state-of-the-art NDN forwarding algorithms across various scenarios. The obtained results confirm the relevance and superiority of our approach in terms of delay, throughput, and packet loss.

This paper first shows that Prophet, Spray and Wait, Epidemic, and First Contact routing protocols in Opportunistic Mobile Networks (OMNs) are vulnerable to the Jamhole attack. In Jamhole attack, an attacker, Eve, compromises two different locations in OMNs by (i) jamming the GPS signal of victim nodes in these locations and (ii) by establishing a pair-wise hidden wormhole tunnel among these locations to route packets and to achieve high packet delivery ratio. The Jamhole attack enables Eve to disrupt the routing, obtain more packets of victim nodes, and possibly launch more severe attacks like packet modification, packet dropping, and packet injection attacks.

In this paper, the impact of Jamhole attack on OMNs routing protocols is investigated using different attack parameters (i.e., area of compromised locations, attack frequency, and attack duration). To identify the Jamhole attack, this paper proposes JamholeHunter, a detection protocol that employs nodes’ wireless ranges, velocities, and last available GPS locations. The paper measured the impact of Jamhole attack and evaluated the JamholeHunter technique through extensive simulation experiments using synthetic and real-world mobility traces. The results demonstrate that (i) the Jamhole attack can cause a serious impact on the OMNs routing protocols, (ii) the effectiveness of JamholeHunter in identifying Jamhole attack with Detection Rate (75% to 100%) depending on various attack parameters with $\sim$95% Accuracy, and low False Positive Rate ($\le$ 3.7%), and (iii) the reliability of JamholeHunter in real-world scenarios of OMNs under different attack parameters, mobility models, and nodes velocity.

Advancements in drone technology and high-frequency millimeter-wave communications are transforming unmanned-aerial-vehicles (UAV)-aided networks, expanding their potential across diverse applications. Despite the advantages of broad frequency bandwidth and enhanced line of sight connectivity in the UAV-aided millimeter-wave networks, it is challenging to provide high network performance because of the inherent limitations of limited UAV energy and millimeter-wave’s large path loss. This challenge becomes more important in dynamically changing multi-UAV environments. To address this challenge in multi-UAV networks, we propose a novel approach based on multi-agent deep reinforcement learning called action-branching QMIX. Our method determines nearly optimal codebook-based discrete beamforming vectors and UAV trajectories while maintaining a balance between communication efficiency and energy consumption. The proposed approach employs a new Long Short-Term Memory module to control long sequences effectively and enables it to adapt to changing environmental variables in real-time. We thoroughly evaluate the proposed control with a real-world measurement-based channel model. The evaluation confirms that the proposed control converges stably and consistently, and provides enhanced performance in terms of downlink data rate, success rate of reaching the destination, and service duration when compared to traditional benchmark multi-agent reinforcement learning schemes. These results emphasize the enhanced energy sustainability, robustness, and stability of the proposed approach in dynamically changing multi-UAV environments when compared to the existing benchmark algorithms.