Types Of Attacks Research Articles

Improving the controllability robustness of complex temporal networks against intelligent attacks

Abstract The main goal of controllability network methods on complex temporal networks is to control all nodes with the minimum number of control nodes. Real-world complex temporal networks are faced with many errors and attacks that cause the network structure to be changed in some way so that the controllability processes are disturbed and after that, the controllability robustness of the network decreases. One of the most important attacks on complex temporal networks is intelligent attacks. In this paper, the types of intelligent attacks and their destructive effects on the controllability of complex temporal networks have been investigated. In order to increase the controllability robustness of the network against intelligent attacks, a novel graph model and strategies have been proposed on complex dynamic graph by adding new control nodes or adding new links to the network so that the network is protected against intelligent attacks. The results of simulation and comparing them with conventional methods demonstrate that the proposed node addition strategy has performed better than other methods and the improvement rate in terms of execution time is 60%. On the other hand, the proposed immunization strategy by adding links has kept the network controllable with a smaller number of links (38%) and less execution time (52%) compared to other methods.

Psychological Effects of Continuity Threatening Cyber Incidents

Working in the field of cybersecurity has been compared to working in a warlike environment. Understanding what types of psychological strain cyber attacks cause to the defending organisations’ workforce can aid in developing methods and processes for mitigating those stressors. This paper discusses the first-hand psychological effects of experiencing an operational continuity threatening cyber incident caused by a real threat actor. The results are based on 19 interviews from IR professionals and IT security practitioners to decision makers, CISO’s and other top executives. These individuals were working in multi-national corporations, hospitals, central government, financial sector, local government or educational institutions at the time of the incident. The interviews followed critical incident paradigm to focus on significant events during the cyber incidents, while also being semi-structured to compensate for the diversity of the incidents. Most of the interviewees raise up feelings of disbelief and despair as their first emotional response to the realization of being hit by ransomware, data theft or another severe cyber incident that could threaten operational or business continuity. Feelings of guilt and self-doubt were present, especially in those considered to be responsible for securing the network. However, at the same time, feelings of purpose and self-efficacy were also reported by some. Having scalable resources available in the time of need, with well-defined roles and responsibilities for the core incident response teams and protecting them from unnecessary inquiries seemed to alleviate the stressors and anxiety of the Incident Response (IR) team during the event. Good leadership and internal communication were seen as important to maintain the necessary situational awareness and focus during the active incident mitigation and resolve phase. Long-term negative effects of the cyber incident were increased cynicism, fear of the situation recurring, and thoughts of changing career. These negative outcomes were mitigated by increased trust in colleagues, processes and systems with experience of self-efficacy. This paper discusses what types of mental strain cyber incidents introduce to cybersecurity professionals and top executives. It deepens understanding on what factors need to be considered in developing and enhancing the overall resilience of organisations against cyber attacks.

Exploring Zero-Day Attacks on Machine Learning and Deep Learning Algorithms

In the rapidly evolving field of artificial intelligence, machine learning (ML) and deep learning (DL) algorithms have emerged as powerful tools for solving complex problems in various domains, including cyber security. However, as these algorithms become increasingly prevalent, they also face new security challenges. One of the most significant of these challenges is the threat of zero-day attacks, which exploit unknown and unpredictable vulnerabilities in the algorithms or the data they process. This paper provides a comprehensive overview of zero-day attacks on ML/DL algorithms, exploring their types, causes, effects, and potential countermeasures. The paper begins by introducing the concept and definition of zero-day attacks, providing a clear understanding of this emerging threat. It then reviews the existing research on zero-day attacks on ML/DL algorithms, focusing on three main categories: data poisoning attacks, adversarial input attacks, and model stealing attacks. Each of these attack types poses unique challenges and requires specific countermeasures. The paper also discusses the potential impacts and risks of these attacks on various application domains. For instance, in facial expression recognition, an adversarial input attack could lead to misclassification of emotions, with serious implications for user experience and system integrity. In object classification, a data poisoning attack could cause the algorithm to misidentify critical objects, potentially endangering human lives in applications like autonomous driving. In satellite intersection recognition, a model stealing attack could compromise national security by revealing sensitive information. Finally, the paper presents some possible protection methods against zero-day attacks on ML/DL algorithms. These include anomaly detection techniques to identify unusual patterns in the data or the algorithm’s behaviour, model verification and validation methods to ensure the algorithm’s correctness and robustness, federated learning approaches to protect the privacy of the training data, and differential privacy techniques to add noise to the data or the algorithm’s outputs to prevent information leakage. The paper concludes by highlighting some open issues and future directions for research in this area, emphasizing the need for ongoing efforts to secure ML/DL algorithms against zero-day attacks.

A Novel Dataset and Approach for Adversarial Attack Detection in Connected and Automated Vehicles

Adversarial attacks have received much attention as communication network applications rise in popularity. Connected and Automated Vehicles (CAVs) must be protected against adversarial attacks to ensure passenger and vehicle safety on the road. Nevertheless, CAVs are susceptible to several types of attacks, such as those that target intra- and inter-vehicle networks. These harmful attacks not only cause user privacy and confidentiality to be lost, but they also have more grave repercussions, such as physical harm and death. It is critical to precisely and quickly identify adversarial attacks to protect CAVs. This research proposes (1) a new dataset comprising three adversarial attacks in the CAV network traffic and normal traffic, (2) a two-phased adversarial attack detection technique named TAAD-CAV, where in the first phase, an ensemble voting classifier having three machine learning classifiers and one separate deep learning classifier is trained, and the output is used in the next phase. In the second phase, a meta classifier (i.e., Decision Tree is used as a meta classifier) is trained on the combined predictions from the previous phase to detect adversarial attacks. We preprocess the dataset by cleaning data, removing missing values, and adjusting the Z-score normalization. Evaluation metrics such as accuracy, recall, precision, F1-score, and confusion matrix are employed to evaluate and compare the performance of the proposed model. Results reveal that TAAD-CAV achieves the highest accuracy with a value of 70% compared with individual ML and DL classifiers.

Unveiling the Threat: Investigating Distributed and Centralized Backdoor Attacks in Federated Graph Neural Networks

Graph neural networks (GNNs) have gained significant popularity as powerful deep learning methods for processing graph data. However, centralized GNNs face challenges in data-sensitive scenarios due to privacy concerns and regulatory restrictions. Federated learning has emerged as a promising technology that enables collaborative training of a shared global model while preserving privacy. Although federated learning has been applied to train GNNs, no research focuses on the robustness of Federated GNNs against backdoor attacks. This article bridges this research gap by investigating two types of backdoor attacks in Federated GNNs: centralized backdoor attack (CBA) and distributed backdoor attack (DBA). Through extensive experiments, we demonstrate that DBA exhibits a higher success rate than CBA across various scenarios. To further explore the characteristics of these backdoor attacks in Federated GNNs, we evaluate their performance under different scenarios, including varying numbers of clients, trigger sizes, poisoning intensities, and trigger densities. Additionally, we explore the resilience of DBA and CBA against two defense mechanisms. Our findings reveal that both defenses cannot eliminate DBA and CBA without affecting the original task. This highlights the necessity of developing tailored defenses to mitigate the novel threat of backdoor attacks in Federated GNNs.

Mitigation of channel tampering attacks in continuous-variable quantum key distribution

Despite significant advancements in continuous-variable quantum key distribution (CV-QKD), practical CV-QKD systems can be compromised by various attacks. Consequently, identifying new attack vectors and countermeasures for CV-QKD implementations is important for the continued robustness of CV-QKD. In particular, as CV-QKD relies on a public quantum channel, vulnerability to communication disruption persists from potential adversaries employing denial-of-service (DoS) attacks. Inspired by DoS attacks, this paper introduces a threat in CV-QKD called the channel amplification (CA) attack, wherein Eve manipulates the communication channel through amplification. We specifically model this attack in a CV-QKD optical fiber setup. To counter this threat, we propose a detection and mitigation strategy. Detection involves a machine learning (ML) model based on a decision tree classifier, classifying various channel tampering attacks, including CA and DoS attacks. For mitigation, Bob, postselects quadrature data by classifying the attack type and frequency. Our ML model exhibits high accuracy in distinguishing and categorizing these attacks. The CA attack's impact on the secret key rate (SKR) is explored concerning Eve's location and the relative intensity noise of the local oscillator (LO). The proposed mitigation strategy improves the attacked SKR for CA attacks and, in some cases, for hybrid CA-DoS attacks. Our study marks an application of both ML classification and postselection in this context. These findings are important for enhancing the robustness of CV-QKD systems against emerging threats on the channel. Published by the American Physical Society 2024

Event‐triggered extended dissipativity control for semi‐Markov jump systems under multiple types of deception attacks: A semi‐Markov model approach

AbstractThe event‐triggered extended dissipativity control problem for semi‐Markov jump systems (SMJSs) on the basis of multiple types of deception attacks is discussed in this paper. The mode‐ and disturbance‐dependent event‐triggered mechanism (ETM), unlike the traditional ETM, is designed to identify the triggered moments which can prevent network resources from being wasted. Additionally, as the attack evolves into a more intelligent and complex strategy, It is hypothesized that there are multiple deception attacks that can be applied to the communication channels between the sensor and the controller, which are modeled by a more general semi‐Markov process (SMP). As a result, by using stochastic analysis techniques and system‐mode‐dependent and attack‐mode‐dependent Lyapunov‐Krasovskii functional (LKF), a new sufficient condition is established to guarantee the stochastic stability and extended dissipativity of the closed‐loop SMJSs. Four numerical cases are given to illustrate the effectiveness of the developed method.

An optimized deep strategy for recognition and alleviation of DDoS attack in SD-IoT

ABSTRACT The attacks like distributed denial-of-service (DDoS) are termed as severe defence issues in data centres, and are considered real network threat. These types of attacks can produce huge disturbances in information technologies. In addition, it is a complex task to determine and fully alleviate DDoS attacks. The new strategy is developed to identify and alleviate DDoS attacks in the Software-Defined Internet of Things (SD-IoT) model. SD-IoT simulation is executed to gather data. The data collected through nodes of SD-IoT are fed to the selection of feature phases. Here, the hybrid process is considered to select features, wherein features, like wrapper-based technique, cosine similarity-based technique, and entropy-based technique are utilized to choose the significant features. Thereafter, the attack discovery process is done with Elephant Water Cycle (EWC)-assisted deep neuro-fuzzy network (DNFN). The EWC is adapted to train DNFN, and here EWC is obtained by grouping Elephant Herd Optimization (EHO) and water cycle algorithm (WCA). Finally, attack mitigation is carried out to secure the SD-IoT. The EWC-assisted DNFN revealed the highest accuracy of 96.9%, TNR of 98%, TPR of 90%, precision of 93%, and F1-score of 91%, when compared with other related techniques.

Parallel path selection mechanism for DDoS attack detection

DDoS attack have always been a popular topic in the field of network security. As an emerging networking paradigm, SDN’s characteristics such as centralized control and management and monitoring flow-based traffic make it an ideal platform to detect DDoS attacks. NFV can reduce equipment costs, simplify operation complexity, and improve operation performance, which provides a major opportunity for effective DDoS detection. Thus, with the help of SDN/NFV technology, this paper proposes a parallel path selection method to detect various types of DDoS attacks. This article virtualizes detection methods as service functions, and combines different service functions to form sequential paths. We first propose a HABS method to parallelize the service functions and construct a parallel path set. Then, we propose a PPCP method to reduce the delay between parallel branches. Next, the parallel path selection problem is formulated as a MDP. Then, we propose a QLBP method to choose the optimal path that balance detection performance, delay and load. Finally, the proposed QLBP method is deployed in a prototype system. We validate the performance of the QLBP method under various DDoS attack scenarios. Besides, we compare the path performance before and after applying the QLBP method. The experimental results indicate that this method can provide optimal parallel path against different attack types.

Comprehensive comparisons of gradient-based multi-label adversarial attacks

Adversarial examples which mislead deep neural networks by adding well-crafted perturbations have become a major threat to classification models. Gradient-based white-box attack algorithms have been widely used to generate adversarial examples. However, most of them are designed for multi-class models, and only a few gradient-based adversarial attack algorithms specifically designed for multi-label classification models. Due to the correlation between multiple labels, the performance of these gradient-based algorithms in generating adversarial examples for multi-label classification is worthy of analyzing and evaluating comprehensively. In this paper, we first transplant five typical gradient-based adversarial attack algorithms in the multi-class environment to the multi-label environment. Secondly, we comprehensively compared the performance of these five attack algorithms and the other four existing multi-label adversarial attack algorithms by experiments on six different attack types, and evaluated the transferability of adversarial examples generated by all algorithms under two attack types. Experimental results show that, among different attack types, the majority of multi-step attack algorithms have higher attack success rates compared to one-step attack algorithms. Additionally, these gradient-based algorithms face greater difficulty in augmenting labels than in hiding them. For transfer experimental results, the adversarial examples generated by all attack algorithms exhibit weaker transferability when attacking other different models.

Meta‐analysis and systematic review for anomaly network intrusion detection systems: Detection methods, dataset, validation methodology, and challenges

AbstractIntrusion detection systems built on artificial intelligence (AI) are presented as latent mechanisms for actively detecting fresh attacks over a complex network. The authors used a qualitative method for analysing and evaluating the performance of network intrusion detection system (NIDS) in a systematic way. However, their approach has limitations as it only identifies gaps by analysing and summarising data comparisons without considering quantitative measurements of NIDS's performance. The authors provide a detailed discussion of various deep learning (DL) methods and explain data intrusion networks based on an infrastructure of networks and attack types. The authors’ main contribution is a systematic review that utilises meta‐analysis to provide an in‐depth analysis of DL and traditional machine learning (ML) in notable recent works. The authors assess validation methodologies and clarify recent trends related to dataset intrusion, detected attacks, and classification tasks to improve traditional ML and DL in NIDS‐based publications. Finally, challenges and future developments are discussed to pose new risks and complexities for network security.

Insights into Modern Intrusion Detection Strategies for Internet of Things Ecosystems

The swift explosion of Internet of Things (IoT) devices has brought about a new era of interconnectivity and ease of use while simultaneously presenting significant security concerns. Intrusion Detection Systems (IDS) play a critical role in the protection of IoT ecosystems against a wide range of cyber threats. Despite research advancements, challenges persist in improving IDS detection accuracy, reducing false positives (FPs), and identifying new types of attacks. This paper presents a comprehensive analysis of recent developments in IoT, shedding light on detection methodologies, threat types, performance metrics, datasets, challenges, and future directions. We systematically analyze the existing literature from 2016 to 2023, focusing on both machine learning (ML) and non-ML IDS strategies involving signature, anomaly, specification, and hybrid models to counteract IoT-specific threats. The findings include the deployment models from edge to cloud computing and evaluating IDS performance based on measures such as accuracy, FP rates, and computational costs, utilizing various IoT benchmark datasets. The study also explores methods to enhance IDS accuracy and efficiency, including feature engineering, optimization, and cutting-edge solutions such as cryptographic and blockchain technologies. Equally, it identifies key challenges such as the resource-constrained nature of IoT devices, scalability, and privacy issues and proposes future research directions to enhance IoT-based IDS and overall ecosystem security.

The Most Important Types of Cyber Attacks that France is Expected to Face in the Future and the Cyber Security Measures it Must Implement to Protect Critical Infrastructure, Telecommunication Networks and Personal Data

As the digital landscape continues to evolve, nations like France find themselves on the frontline of an escalating cyber warfare scenario. This abstract provides an insightful overview of the most significant types of cyber attacks that France is expected to face in the future, offering a comprehensive analysis based on current trends and emerging technologies. The research delves into the realm of advanced persistent threats (APTs), examining the sophisticated techniques employed by state-sponsored actors and cybercriminal organizations to compromise critical infrastructure, sensitive data, and national security. It explores the evolving landscape of ransomware attacks, focusing on the potential impact on both public and private sectors, and the looming threat of extortion campaigns that could cripple essential services. Furthermore, the abstract sheds light on the rising concern of supply chain attacks, as interconnected global networks make businesses and government entities vulnerable to malicious actors seeking to exploit weaknesses in third-party systems. The research also explores the nuances of social engineering attacks, recognizing the human element as a critical vulnerability that threat actors may exploit through targeted phishing campaigns and manipulation tactics. In addition, the abstract touches upon the growing menace of Internet of Things (IoT) vulnerabilities, acknowledging the increased interconnectivity of devices and the potential for large-scale disruptions. It also considers the implications of emerging technologies such as artificial intelligence and quantum computing in shaping the future threat landscape. By examining these diverse aspects of cyber threats, this abstract aims to provide a forward-looking perspective on the challenges France is likely to encounter in the digital realm. As Cyber security becomes an integral component of national defense, understanding these anticipated threats is crucial for policymakers, security experts, and technology professionals seeking to fortify France’s resilience against the evolving nature of cyber attacks.

Position Falsification Detection Approach Using Travel Distance-Based Feature

Abstract This paper addresses the vulnerability of vehicular ad hoc networks (VANETs) to malicious attacks, specifically focusing on position falsification attacks. Detecting misbehaving vehicles in VANETs is challenging due to the dynamic nature of the network topology and vehicle mobility. The paper considers five types (constant attack, constant offset attack, random attack, random offset attack, and eventually stop attack) of position falsification attacks with varying traffic and attack densities, considered the most severe attacks in VANETs. To improve the detection of these attacks, a novel travel distance feature and an enhanced two-stage detection approach are proposed for classifying position falsification attacks in VANETs. The approach involves deploying the misbehavior detection system within roadside units (RSUs) by offloading computational work from vehicles (onboard units, or OBUs) to RSUs. The performance of the proposed approach was evaluated against different classifiers, including a wide range of paradigms (KNN, Decision Tree, and Random Forest), using the VeReMi dataset. Experimental results indicate that the proposed method based on Random Forest achieved an accuracy of 99.9% and an F1-Score of 99.9%, which are better not only than those achieved by KNN and Decision Tree but also than the most recent approaches in the literature survey.

ENHANCING NETWORK INTRUSION DETECTION WITH MOUNTAIN GAZELLE OPTIMIZER-BASED FEATURE SELECTION

Network intrusion detection systems (NIDS) play a critical role in safeguarding modern networks. Feature selection techniques are essential for optimizing NIDS performance by reducing dimensionality and enhancing classifier efficiency. This study proposes the Mountain Gazelle Optimizer (MGO), a novel nature-inspired metaheuristic, for effective feature selection within the context of network intrusion detection. MGO's performance is evaluated on the benchmark UNSW-NB15 dataset in conjunction with a diverse suite of classifiers: Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Random Forest Classifiers (RFC), Decision Tree Classifiers (DTC), and Gaussian Naive Bayes (GNB). Our analysis investigates the impact of MGO-based feature selection on key NIDS performance metrics including accuracy, specificity, sensitivity, and computational runtime. Results demonstrate the potential of MGO to identify optimal feature subsets, leading to significant improvements in network intrusion detection performance across these varied algorithmic approaches. Additionally, we explore the comparative advantages of MGO-based feature selection for specific types of network attacks represented within the UNSW-NB15 dataset. Keywords: Intrusion Detection System (IDS), Mountain Gazelle Optimizer, Feature Selection, SVM, KNN, Naive Bayes, Classification, Optimization, Machine Learning.

Enhancing IoT security: A comparative study of feature reduction techniques for intrusion detection system

Internet of Things (IoT) devices are extensively utilized but are susceptible to cyberattacks, posing significant security challenges. To mitigate these threats, machine learning techniques have been implemented for network intrusion detection in IoT environments. These techniques commonly employ various feature reduction methods, prior to inputting data into models, in order to enhance the efficiency of detection processes to meet real-time requirements. This study provides a comprehensive comparison of feature selection (FS) and feature extraction (FE) techniques for network intrusion detection systems (NIDS) in IoT environments, utilizing the TON-IoT and BoT-IoT datasets for both binary and multi-class classification tasks. We evaluated FS methods, including Pearson correlation and Chi-square, and FE methods, such as Principal Component Analysis (PCA) and Autoencoders (AE), across five classic machine learning models: Decision Tree (DT), Random Forest (RF), Naive Bayes (NB), k-Nearest Neighbors (kNN), and Multi-Layer Perceptron (MLP). Our analysis revealed that FE techniques generally achieve higher accuracy and robustness compared to FS methods, with RF paired with AE delivering superior performance despite higher computational demands. DTs are most effective with smaller feature sets, while MLPs excel with larger sets. Chi-square is identified as the most efficient FS method, balancing performance and computational efficiency, whereas PCA outperforms AE in runtime efficiency. The study also highlights that FE methods are more effective for complex datasets and less sensitive to feature set size, whereas FS methods show significant performance improvements with more informative features. Despite the higher computational costs of FE methods, they demonstrate a greater capability to detect diverse attack types, making them particularly suitable for complex IoT environments. These findings are crucial for both academic research and industry applications, providing insights into optimizing detection performance and computational efficiency in NIDS for IoT networks.

Color image encryption scheme for distributed architecture with SCFP chaotic map

Image protection mechanism in distributed cloud network is an essential component of information security field. In this paper, a novel one-dimensional sine-cosine fractional power chaotic map (SCFP) is proposed. Results of various dynamical system tests illustrate that SCFP exhibits superior chaotic behavior over its infinite positive real parameter range, whose complexity and unpredictability can guarantee the strength of image cryptosystem. Furthermore, a color image encryption scheme tailored for distributed architecture is devised. Firstly, a hybrid cryptographic mechanism is designed to perform diffusion and confusion encryption for image data and ECC public key encryption for intermediate keys. Secondly, the diffusion structure elevates processing units to row-column level, and the diffusion order is dictated by a pseudo-random sequence generated by SCFP. Thirdly, the confusion structure extends the unbiased and efficient Fisher-Yates algorithm into a 2D space, and adopts a design of dual plaintext-related key. Lastly, three techniques namely QOI lossless compression, DE information embedding and threshold secret sharing are integrated to resolve issues of data volume inflation, key synchronization difficulty and poor fault tolerance. Simulation experiments conducted on multiple color images demonstrate that the proposed scheme offers significant ciphertext randomness, sufficiently large key space and strong key sensitivity, which can ensure the integrity of image data and resist various typical cryptographic attacks, and outperforms existing schemes oriented to centralized architecture in terms of security and efficiency.

A case study on phishing detection with a machine learning net

AbstractPhishing attacks aims to steal sensitive information and, unfortunately, are becoming a common practice on the web. Email phishing is one of the most common types of attacks on the web and can have a big impact on individuals and enterprises. There is still a gap in prevention when it comes to detecting phishing emails, as new attacks are usually not detected. The goal of this work was to develop a model capable of identifying phishing emails based on machine learning approaches. The work was performed in collaboration with E-goi, a multi-channel marketing automation company. The data consisted of emails collected from the E-goi servers in the electronic mail format. The problem consisted of a classification problem with unbalanced classes, with the minority class corresponding to the phishing emails and having less than 1% of the total emails. Several models were evaluated after careful data selection and feature extraction based on the email content and the literature regarding these types of problems. Due to the imbalance present in the data, several sampling methods based on under-sampling techniques were tested to see their impact on the model’s ability to detect phishing emails. The final model consisted of a neural network able to detect more than 80% of phishing emails without compromising the remaining emails sent by E-goi clients.

A Large Scale Study and Classification of VirusTotal Reports on Phishing and Malware URLs

VirusTotal (VT) is a widely used scanning service for researchers and practitioners to label malicious entities and predict new security threats. Unfortunately, it is little known to the end-users how VT URL scanners decide on the maliciousness of entities and the attack types they are involved in (e.g., phishing or malware-hosting websites). In this paper, we conduct a systematic comparative study on VT URL scanners' behavior for different attack types of malicious URLs, in terms of 1) detection specialties, 2) stability, 3) correlations between scanners, and 4) lead/lag behaviors. Our findings highlight that the VT scanners commonly disagree with each other on their detection and attack type classification, leading to challenges in ascertaining the maliciousness of a URL and taking prompt mitigation actions according to different attack types. This motivates us to present a new highly accurate classifier that helps correctly identify the attack types of malicious URLs at the early stage. This in turn assists practitioners in performing better threat aggregation and choosing proper mitigation actions for different attack types.

Optimized Cross-Path Attacks via Adversarial Reconnaissance

While softwarization and virtualization technologies make modern communication networks appear easier to manage, they also introduce highly complex interactions within the networks that can cause unexpected security threats. In this work, we study a particular security threat due to the sharing of links between high-security paths and low-security paths, which enables a new type of DoS attacks, called cross-path attacks, that indirectly attack a set of targeted high-security paths (target paths) by congesting the shared links through a set of attacker-controlled low-security paths (attack paths). While the feasibility of such attacks has been recently demonstrated in the context of SDN, their potential performance impact has not been characterized. To this end, we develop an approach for designing an optimized cross-path attack under a constrained total attack rate, consisting of (i) novel reconnaissance algorithms that can provide consistent estimates of the locations and parameters of the shared links via network tomography, and (ii) efficient optimization methods to design the optimal allocation of attack rate over the attack paths to maximally degrade the performance of the target paths. The proposed attack has achieved a significantly larger performance impact than its non-optimized counterparts in extensive evaluations based on multiple network settings, signaling the importance of addressing such intelligent attacks in network design. For more detail, see the full paper [4].