SecMTL-Rail: A Multi-Task Learning Framework for Railway Security

Internet-based computing has lead to an emergence of a large number of threats. One of the major threat is DDoS (Distributed Denial of Service) attack. Recent incidents have shown that DDoS attacks have the capability of shutting a business not for a day but weeks. DDoS attacks have a greater impact on multi-tenant clouds than traditional infrastructure. DDoS attacks in the cloud, take the shape of EDoS (Economic denial of sustainability) attacks. In EDoS, instead of “Service Denial”, economic harms occur due to fake resource usage and subsequent addition or buying of resources using on-demand provisioning. To detect and mitigate DDoS attacks in the cloud, we argue that on-demand resource allocation (known as auto-scaling) should also be looked, in addition to network or application layer mitigation. We have proposed a novel mitigation strategy, DARAC, which makes auto-scaling decisions by accurately differentiating between legitimate requests and attacker traffic. Attacker traffic is detected and dropped based on human behavior analysis based detection. We also argue that most of the solutions in the literature, do not pay much attention to the service quality to legitimate requests during an attack. We calculate the share of legitimate clients in resource addition/buying and make subsequent accurate auto-scaling decisions. Experimental results show that DARAC mitigates various DDoS attack sets and take accurate and quick auto-scaling decisions for various legitimate and attacker traffic combinations saving from EDoS. We also show how proposed mechanism could make “arms-race” very difficult for the attackers as the resource need to defeat DARAC mechanism on a very small capacity server is huge. Results also show significant improvements in the average response time of the web-service under attack, in addition to infrastructure cost savings up to 50 % in heavy attack cases.KeywordsCloud ComputingVirtual MachineAttack ScenarioIdle ResourceCloud ConsumerThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

DDoS (Distributed Denial of Service) attacks consist of a large number of compromised computer systems that launch joint attacks at a targeted victim, such as a server, website, or other network equipment, simultaneously. DDoS has become a widespread and severe threat to the integrity of computer networks. DDoS can lead to system paralysis, making it difficult to troubleshoot. As a key building block in the development of an integrated defense system, it is essential to detect DDoS attacks as early as possible. With the popularization of artificial intelligence, more and more researchers apply machine learning (ML) and deep learning (DL) to the detection of DDoS attacks and have achieved satisfactory accomplishments. The complexity and sophistication of DDoS attacks have kept increasing and evolving since the first DDoS attack was reported in 1996. Regarding the headways in this problem, a new type of DDoS attack, named adversarial DDoS attack, is investigated in this study. The generating adversarial DDoS traffic is carried out using a symmetric Generative Adversarial Networks (GAN) architecture called Cycle-GAN to demonstrate the severe impact of adversarial DDoS attacks. Experiment results reveal that the synthesized traffic can easily penetrate ML-based detection systems, including Random Forest, KNN (k-Nearest Neighbor), SVM (Support Vector Machine), and Naïve Bayes. These alarming results intimate the urgent need for countermeasures against adversary DDoS attacks.

a serious nuisance for network security could be a Distributed Denial of Service (DDoS) attack. The advent of the technological era has also brought along the threat of DDoS attacks for a variety of services and applications that use the Internet. Firms can incur huge financial losses even if there is a disruption in services for a fraction of a period. Analogous to a DDoS attack is the Flash Crowd (FC), in which a particular service is assessed by many legitimate users concurrently, which results in the denial of service. Overloading of network resources is a common issue associated with both these events, which impact CPU, available bandwidth, and memory for legitimate users, thereby leading to limited accessibility. Many researchers have put effort to differentiate between these two kinds of traffic, but none could come up with an effective solution. In this study, we propose an adaptive agent-based model, known as an adaptive application layer flooding protection (AALFP) model, for DDoS and FC flooding attacks. The AALFP model is aimed to protect the network application layer (NAL) against such attacks. Importantly, the model distinguishes and separates normal from abnormal traffic, and then blocks illegitimate traffic. Note that, to operate two traffic filters, the agent relies on the following three parameters of normal traffic intensity, traffic attack behaviour, and IP address history log. An example scenario is provided to simulate the model's application.

Denial of Service (DoS) or Distributed Denial of Service (DDoS) is a powerful attack which prevents the system from providing services to its legitimate users. Several approaches exist to filter network-level attacks, but application-level attacks are harder to detect at the firewall. Filtering at application level can be computationally expensive and difficult to scale, while still creating bogus positives that block legitimate users. In this paper, authors show application layer DoS attack for SIP server using some open source DoS attack tools and also suggest a mechanism that can protect a given SIP server from application-level DoS attacks especially the attacks targeting the resources including CPU, sockets, memory of the victim server. In this paper author’s attempt to illustrate application layer distributed denial of Service (DDoS) attack on SIP Server such as SIP flooding attack, real time transport (RTP) flooding attack using open source DDoS attack tools. We propose a new DDoS defence mechanism that protects SIP servers from application-level DDoS attacks based on the two methodologies: IPtables and fail2ban detection. The attack flow detection mechanism detects attach flows based on the symptom or stress at the server, since it is getting more difficult to identify bad flows only based on the incoming traffic patterns. A popular software known as Wireshark which is a network protocol analyzer is used to capture the packets during DoS attack from the victim server Ethernet interface to detect the attacking host IP address and analysis the types of attack. We evaluate the performance of the proposed scheme via experiment

Cloud Computing is one of the most nurtured as well as debated topic in today’s world. Billions of data of various fields ranging from personal users to large business enterprises reside in Cloud. Therefore, availability of this huge amount of data and services is of immense importance. The DOS (Denial of Service) attack is a well-known threat to the availability of data in a smaller premise. Whenever, it’s a Cloud environment this simple DOS attack takes the form of DDOS (Distributed Denial of Service) attack. This paper provides a generic insight into the various kinds of DOS as well as DDOS attacks. Moreover, a handful of countermeasures have also been depicted here. In a nutshell, it aims at raising an awareness by outlining a clear picture of the Cloud availability issues.Our paper gives a comparative study of different techniques of detecting DOS.

Software-defined networking (SDN) has revolutionized traditional network management by decoupling the control and data planes, offering enhanced flexibility, programmability, and efficiency. However, this centralized architecture makes SDN vulnerable to Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks, which severely impact network performance and reliability. Through overloading critical network components, especially the SDN controller, these attacks reduce network availability and result in significant service interruptions. This review offers a thorough summary of mitigation strategies designed to combat DoS and DDoS attacks in SDN systems. These attacks overwhelm network resources, disrupt services, and compromise the availability of critical components such as the SDN controller. Additionally, it explores the key challenges facing SDN in defending against DoS and DDoS attacks, such as the need for real-time detection, the impact of network scale, and the complexities of adaptive responses. As such, addressing DoS and DDoS attacks has become a critical research area for ensuring the security and robustness of SDN-based networks. It categorizes existing approaches based on detection methods, response strategies, and deployment models, providing a detailed analysis of their strengths and limitations. Furthermore, it discusses problems such as scalability, concurrent detection, and adaptability that hinder the effectiveness of current solutions. Emerging trends, including integrating artificial intelligence, blockchain, and collaborative frameworks, are highlighted as promising directions for future research. This aim is to give researchers, network admins, and cyber-security practitioners an insight into the state of the art as well as a basis on which to build for future SDN security advancements. This survey aims to serve as a comprehensive resource for researchers and practitioners seeking to imporve SDN's resilience against DoS and DDoS attacks.

DoS(Denial of Service) or DDoS(Distributed DoS) attack is a major threaten and the most difficult problem to solve among many attacks. Moreover, it is very difficult to find a real origin of attackers because DoS/DDoS attacker uses spoofed IP addresses. To solve this problem, we propose a probabilistic route selection algorithm to trace the attacker's real origin. This algorithm uses three types of packets such as an alert packet, an agent packet and a reply agent packet. The alert packet is in use to notify to a victim that DDoS attacks occur. The agent packet is in use to find the attacker's real origin and the reply agent packet is in use to notify to a victim that the agent packet is reached the edge router of the attacker. When source-end detector and victim-end detector detect attacks, the victim-end detector sends the alert packet to the victim. The victim received the alert packet generates the agent packet and sends it to a victim's edge router. The attacker's edge router received the agent packet generates the reply agent packet and send it to the victim. The agent packet and the reply agent packet is forwarded refer to probabilistic packet forwarding table by routers. The probabilistic route selection algorithm runs on the distributed routers and probabilistic packet forwarding table is stored and managed by routers. We simulate for the propose of validation this algorithm by using OPNET14.0 in terms of false positive and required number of packets to trace DDoS attack traffic source.

The existing methods for backbone networks only analyze coarse-grained network traffic characteristic parameters.Thus,they cannot guarantee both the premise of abnormal flow identification and the real-time detection for DoS(Denial of Service) DDoS(Distributed Denial of Service,DDoS) attacks.Concerning this problem,a DoSDDoS attack detection and abnormal flow identification method for backbone networks was proposed.First,it analyzed coarse-grained network traffic characteristic parameters to determine the time points that abnormal behaviors occur;then,fine-grained traffic behavior characteristic parameters were analyzed in these time points to find the destination IP addresses that correspond to abnormal behaviors;finally,comprehensive analysis was conducted for extracted traffic that correspond to abnormal behaviors to determine DoS and DDoS attacks.The simulation results show that,the proposed method can effectively detect DoS attacks and DDoS attacks in backbone networks.Meanwhile,it could accurately identify the abnormal traffic,while real-time detection is ensured.

With the increasing number of Internet users, cybersecurity is becoming more and more critical. Denial of service (DoS) and distributed denial of service (DDoS) attacks are two of the most common types of attacks that can severely affect a website or a server and make them unavailable to other users. The number of DDoS attacks increased by 55% between the period January 2020 and March 2021. Some approaches for detecting the DoS and DDoS attacks employing different machine learning and deep learning techniques are reported in the literature. Recently, it is also observed that the attackers have started leveraging state-of-the-art AI tools such as generative models for generating synthetic attacks which fool the standard detectors. No concrete approach is reported for developing and training the models which are not only robust in the detection of standard DDoS attacks but which can also detect adversarial attacks which are created synthetically by the attackers with harmful intentions. To that end, in this work, we employ a generative adversarial network (GAN) to develop such a robust detector. The proposed framework can generate and classify the synthetic benign (normal) and malignant (DDoS) instances which are very similar to the corresponding real instances as evaluated by similarity scores. The GAN-based model also demonstrates how effectively the malicious actors can generate adversarial DDoS network traffic instances which look like normal instances using feature modification which are very difficult for the classifier to detect. An approach on how to make the classifiers robust enough to detect such kinds of deliberate adversarial attacks via modifying some specific attack features manually is also proposed. This work provides the first step towards developing a generic and robust detector for DDoS attacks originating from various sources.

Abstract: DDoS attacks also known as distributed denial of service (DDoS) attacks have emerged as one of the most serious and fastest-growing threats on the Internet. Denial-of-service (DDoS) attacks are an example of cyber-attacks that target a specific system or network in an attempt to render it inaccessible or unusable for a period of time. As a result improving the detection of diverse types of DDoS cyber threats with better algorithms and higher accuracy while keeping the computational cost under control has become the most significant component of detecting DDoS cyber threats. DDoS (distributed denial-of-service) attack originates from many sources scattered over multiple network locations. DoS attacks are primarily motivated by the desire to significantly degrade the performance or completely consume a certain resource and a process to exploit a machine defect and cause failure of a processing or exhausting the system resources by exploiting a system flaw. Yet another method of assaulting the target system is to flood the network and monopolies it so preventing anyone else from utilizing it. DDoS attack has a high impact on crashing the network resources making the target servers unable to support the valid users.

The Internet of Things (IoT) is a network of sensors that helps collect data 24/7 without human intervention. However, the network may suffer from problems such as the low battery, heterogeneity, and connectivity issues due to the lack of standards. Even though these problems can cause several performance hiccups, security issues need immediate attention because hackers access vital personal and financial information and then misuse it. These security issues can allow hackers to hijack IoT devices and then use them to establish a Botnet to launch a Distributed Denial of Service (DDoS) attack. Blockchain technology can provide security to IoT devices by providing secure authentication using public keys. Similarly, Smart Contracts (SCs) can improve the performance of the IoT–blockchain network through automation. However, surveyed work shows that the blockchain and SCs do not provide foolproof security; sometimes, attackers defeat these security mechanisms and initiate DDoS attacks. Thus, developers and security software engineers must be aware of different techniques to detect DDoS attacks. In this survey paper, we highlight different techniques to detect DDoS attacks. The novelty of our work is to classify the DDoS detection techniques according to blockchain technology. As a result, researchers can enhance their systems by using blockchain-based support for detecting threats. In addition, we provide general information about the studied systems and their workings. However, we cannot neglect the recent surveys. To that end, we compare the state-of-the-art DDoS surveys based on their data collection techniques and the discussed DDoS attacks on the IoT subsystems. The study of different IoT subsystems tells us that DDoS attacks also impact other computing systems, such as SCs, networking devices, and power grids. Hence, our work briefly describes DDoS attacks and their impacts on the above subsystems and IoT. For instance, due to DDoS attacks, the targeted computing systems suffer delays which cause tremendous financial and utility losses to the subscribers. Hence, we discuss the impacts of DDoS attacks in the context of associated systems. Finally, we discuss Machine-Learning algorithms, performance metrics, and the underlying technology of IoT systems so that the readers can grasp the detection techniques and the attack vectors. Moreover, associated systems such as Software-Defined Networking (SDN) and Field-Programmable Gate Arrays (FPGA) are a source of good security enhancement for IoT Networks. Thus, we include a detailed discussion of future development encompassing all major IoT subsystems.

Denial of Service (DoS) and Distributed Denial of service (DDoS) attacks are common place in today's computer networks. There are different types of attacks among which Flood based attacks are the major ones. Attacks detection mechanisms usually rely on statistical information of the traffic. As regards of the fact that statistical properties of DoS and DDoS attacks are very similar to those in legitimate traffics, in this paper we characterize the frequency domain of denial of service attacks instead of time domain. We consider the number of packets arriving to the node of victim as a random process which is acquired by sampling the packets number every 1 milli-second. Having created the process we find the normalized spectrum of the sampled data. The results show that the main energy of DoS and DDoS attacks are distributed in high and low frequencies respectively while energy is spread evenly through all range of frequencies in Normal TCP traffic.

Distributed denial-of-service (DDoS) and denial-of-service (DoS) are the most dreadful network threats in recent years. In this paper, we propose a grid-based IDS, called performance-based grid intrusion detection system (PGIDS), which exploits grid's abundant computing resources to detect enormous intrusion packets and improve the drawbacks of traditional IDSs which suffer from losing their detection effectiveness and capability when processing massive network traffic. For balancing detection load and accelerating the performance of allocating detection node (DN), we use exponential average to predict network traffic and then assign the collected actual traffic to the most suitable DN. In addition, score subtraction algorithm (SSA) and score addition algorithm (SAA) are deployed to update and reflect the current performance of a DN. PGIDS detects not only DoS/DDoS attacks but also logical attacks. Experimental results show that PGIDS is truly an outstanding system in detecting attacks.

Nowadays, the most distinct threat to the web applications is HTTP-based Denial of Service (DoS) and Distributed Denial of Service (DDoS) attack, which it causes a massive damage to them and corrupt their works completely. Up to now, various researches are accomplished in this field for the sake of identifying and determining the most convenientrequired specifications for a protective solution that helps to protect web applications from HTTP-based DoS and DDoS attacks. Unfortunately, despite of that,the optimal specifications of the protective solution have not been identified, and therefore this paper comes out to state and identify these missing specifications. In this paper, a comprehensive survey to all sorts of HTTP-based DoS and DDoS attacks is conducted in order to figure out and identify the optimal specifications for a protective framework against HTTP-based DoS and DDoS attacks.

The simulation of a modern railway system on the cyber range will investigate the supple behavior of cyber-attacks in a modern digital railway system. The contemporary digital railway simulation architecture will focus on the railway's signaling system and the control command system, which is a classic SCADA system. With the introduction of IoT technology to railway systems, they have become vulnerable to cyber-attacks. The simulation is conducted on the cyber range server by simulating STCS. The cyber threats will be analyzed through penetration testing using Kali Linux on the simulated STCS on the cyber range. We analyze the impact of the threat agent groups, especially denial of service (DoS) and quay chain attacks that mainly target the STCS. The DoS attack generates the congestion of packets in the railway system network and will compromise the SCADA components in the STCS. The quay chain attack compromises the SCADA component, resulting in a delay in the signal system of STCS. The attack tree analysis helps us to understand the behavior of threat agents on STCS. Based on the research, we identified some mitigation techniques to reduce the cyber-attacks impact on the modern digital railway system.