To see the other types of publications on this topic, follow the link: Internet attack.
Journal articles on the topic 'Internet attack'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 journal articles for your research on the topic 'Internet attack.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.
1
Ourston, Dirk, Sara Matzner, William Stump, and Bryan Hopkins. "Coordinated Internet attacks: responding to attack complexity." Journal of Computer Security 12, no. 2 (2004): 165–90. http://dx.doi.org/10.3233/jcs-2004-12201.
Full text
2
Peterson, A. Padgett. "Internet attack mechanisms." Network Security 1996, no. 5 (1996): 17–19. http://dx.doi.org/10.1016/1353-4858(96)81911-8.
Full text
3
R., Ramkumar, Rahul R., and Gowtham Sri. "Anomaly Based Approach for Defending Denial of Service Attack in Web Traffic." COMPUSOFT: An International Journal of Advanced Computer Technology 04, no. 04 (2015): 1657–64. https://doi.org/10.5281/zenodo.14776346.
Full textAbstract:
Distributed Denial of Service (DDOS) attacks has become a great threat for internet security. This attackis an advanced form of DOS (Denial of Service) attack. This attack changes its whole origin ID and it gives trouble to find it out and it has become a serious threat for internet security. Almost all traditional services such as bank websites, power resources, medical, educational institutions and military are extended to World Wide Web and thus many people widely use internet services. As many users of Internet is mandatory, network security for attacks are also increasing. Current DDoS attacks are carried out by hacking tools, viruses and botnets using different packet-transmission strategies and various forms of attack packets to beat defense system networks. These problems lead to defense system network requiring various detection methods in order to identify attacks. But DDoS attacks can mix their traffics during flash crowds. By doing this, the network of defense systems cannot detect the attack traffic in time. Denial of service (DOS) attack is potential damaging attack which degrades the performance of online servers in no time. This attack performs an intensive attack on the target server by flooding it with large useless packets. Our Triangular MCAbased DoS attack detection system employs the principle of anomaly based detection in attack recognition. To cope with such damaging attacks becomes challenge for the researchers. Preventing and avoiding this attack mainly focuses on the development of network-based detection mechanisms. Detection systems based on these techniques monitor traffic transmitting over the protected networks. This makes our solution capable of detecting known and unknown DoS attacks effectively by learning the patterns of legitimate network traffic only. In this paper Detection of denial of service attack is done using anamoly based approach, multivariate correlation analysis.
4
Christopher, Mitchel, Ghaleb Baraq, M. Ghaleb Safwan, Jaroucheh Zakwan, and Ali Saleh Al-rimy Bander. "The Impact of Mobile DIS and Rank-Decreased Attacks in Internet of Things Networks." International Journal of Engineering and Advanced Technology (IJEAT) 10, no. 2 (2020): 66–72. https://doi.org/10.35940/ijeat.B1962.1210220.
Full textAbstract:
With a predicted 50 billion devices by the end of 2020, the Internet of things has grown exponentially in the last few years. This growth has seen an increasing demand for mobility support in low power and lossy sensor networks, a type of network characterized by several limitations in terms of their resources including CPU, memory and batter, causing manufactures to push products out to the market faster, without the necessary security features. IoT networks rely on the Routing Protocol for Low Power and Lossy Network (RPL) for communication, designed by the Internet Engineering Task Force (IETF). This protocol has been proven to be efficient in relation to the handling of routing in such constrained networks, However, research studies revealed that RPL was inherently designed for static networks, indicating poor handling of mobile or dynamic topologies which is worsen when introducing mobile attacker. In this paper, two IoT routing attacks are evaluated under a mobile attacker with the aim of providing a critical evaluation of the impact the attacks have on the network in comparison to the case with static attacker. The first attack is the Rank attack in which the attacker announces false routing information to its neighbour attracting them to forward their data via the attacker. The second attack is the DIS attack in which the attacker floods the network with DIS messages triggering them to reset their transmission timers and sending messages more frequently. The comparison were conducted in terms of average power consumption and also the packet delivery ratio (PDR). Based on the results collected from the simulations, it was established that when an attacking node is mobile, there’s an average increase of 36.6 in power consumption and a decrease of 14 for packet delivery ratios when compared to a static attacking node.
5
Rastenis, Justinas, Simona Ramanauskaitė, Justinas Janulevičius, Antanas Čenys, Asta Slotkienė, and Kęstutis Pakrijauskas. "E-mail-Based Phishing Attack Taxonomy." Applied Sciences 10, no. 7 (2020): 2363. http://dx.doi.org/10.3390/app10072363.
Full textAbstract:
The amount of fraud on the Internet is increasing along with the availability and the popularity of the Internet around the world. One of the most common forms of Internet fraud is phishing. Phishing attacks seek to obtain a user’s personal or secret information. The variety of phishing attacks is very broad, and usage of novel, more sophisticated methods complicates its automated filtering. Therefore, it is important to form up-to-date and detailed phishing attack taxonomy, which could be used for both human education purposes as well as phishing attack discrete notation. In this paper, we propose an e-mail-based phishing attack taxonomy, which includes six phases of the attack. Each phase has at least one criterion for the attack categorization. Each category is described, and in some cases the categories have sub-classes to present the full variety of phishing attacks. The proposed taxonomy is compared to similar taxonomies. Our taxonomy outperforms other phishing attack taxonomies in numbers of phases, criteria and distinguished classes. Validation of the proposed taxonomy is achieved by adapting it as a phishing attack notation for an incident management system. Taxonomy usage for phishing attack notation increases the level of description of phishing attacks compared to free-form phishing attack descriptions.
6
Zafri, Fizza. "Ransomware Attacks in History of Cyber World." International Journal for Research in Applied Science and Engineering Technology 10, no. 1 (2022): 39–43. http://dx.doi.org/10.22214/ijraset.2022.39758.
Full textAbstract:
Abstract: Technology advancement since last few decades creates cyber attack a critical issue. Cyber security has become an important part today. It has also become an important and crucial subject in the field of forensic science. Increased in the growth of internet technology and internet devices have increased the risk of cyber attack. Almost every organization today are depends on the internet and devices. There are many types of cyber attack. This paper is the detailed review about Ransomware attack. This paper is consisted about vast of the information about What is Ransomware Attack, how does it work, how ransomware attack emerged. After reading this paper you will learn about the ransomware attacks in history of cyber world. This will help you to learn and understand about ransomware attack, how to prevent yourself from ransomware attack. As a forensic science student, it is always important to be aware about the attacks that have happened in the history of cyber world. Before writing this paper, I have read and analyze many research paper and internet articles, so that I can write a detailed review paper which can help students and for the forensic awareness. Keywords: Cyberattack, Hacking, Ransomware, cyberworld, cyber security, ransomware, forensic, network security
7
Karthikeyani, R., and E. Karthikeyan. "A Review on Distributed Denial of Service Attack." Asian Journal of Research in Computer Science 16, no. 4 (2023): 133–44. http://dx.doi.org/10.9734/ajrcos/2023/v16i4378.
Full textAbstract:
Today’s world, technology has become an inevitable part of human life. In fact, during the Covid-19 pandemic, everything from the corporate world to educational institutions has shifted from offline to online. It leads to exponential increase in intrusions and attacks over the internet-based technologies. Distributed denial of service (DDOS) attack is one of the most dangerous attack that could cause devastating effects on the internet. These attacks are becoming more complex and expected to expand in number day after day, rendering detecting and combating these threats challenging. In network security this attack is very dangerous. The main aim of DDOS attack is to collapse the network or server with abnormal traffic to make server unavailable for the legitimate users. In this paper reviews various type of DDOS attacks, Symptoms of DDOS attack, role of botnet on DDOS attack and give some mitigation and prevention technique for DDOS attack.
8
Wu, Yixin, Cheng Huang, Xing Zhang, and Hongyi Zhou. "GroupTracer: Automatic Attacker TTP Profile Extraction and Group Cluster in Internet of Things." Security and Communication Networks 2020 (December 4, 2020): 1–14. http://dx.doi.org/10.1155/2020/8842539.
Full textAbstract:
As Advanced Persistent Threat (APT) becomes increasingly frequent around the world, security experts are starting to look at how to observe, predict, and mitigate the damage from APT attacks. In the meantime, the Internet of things devices are also risky and heavily exposed to the Internet, making them more easily used by hacker organizations to launch APT attacks. An excellent attacker can take down millions of Internet of things devices in a short time. Once the IoT botnet is built, attackers can use it to launch complex attacks which could damage Internet infrastructure and cause network disconnection. This paper proposes GroupTracer, a framework for observing and predicting the Internet of things attacks. GroupTracer is designed to automatically extract the TTP profiles (i.e., tactics, techniques, and procedures) that can describe the behavior of attackers through their tactics, techniques, and processes and dig out the potential attacker groups behind complex attacks. Firstly, it captures attacks by IoT honeypots and extracts relevant fields from logs. Then, attack behaviors are automatically mapped to the ATT&CK framework to achieve automatic TTP profiles extraction. After that, GroupTracer presents four feature groups, including TTP profiles, Time, IP, and URL features, a total of 18 features, mines potential attack groups through hierarchical clustering algorithm, and compares the clustering results with two baseline algorithms. As the ground truth labels are unknown, we apply three internal validation indexes to evaluate the cluster quantity. Experimental results showed that the proposed framework has achieved an excellent performance in exploiting potential groups as the Calinski–Harabasz index reaches 3416.93. Eventually, attack trees are generated for each cluster where nodes indicate attack commands and edges represent command sequences. These attack trees could help better understand each attack group’s actions and techniques.
9
Singh, Rajeev, and T. P. Sharma. "Present Status of Distributed Denial of Service (DDoS) Attacks in Internet World." International Journal of Mathematical, Engineering and Management Sciences 4, no. 4 (2019): 1008–17. http://dx.doi.org/10.33889/ijmems.2019.4.4-080.
Full textAbstract:
Distributed Denial of Service (DDoS) attack harms the digital availability in Internet. The user’s perspective of getting quick and effective services may be badly hit by the DDoS attackers. There are several reports of DDoS attack incidences that have caused devastating effects on the user and web services in the Internet world. In the present digital world dominated by wireless, mobile and IoT devices, the numbers of users are increasing day by day. Most of the users are novice and therefore their devices either fell prey to DDoS attacks or unknowingly add themselves to the DDoS attack Army. We soon will witness the 5G mobile revolution but there are reports that 5G networks are also falling prey to DDoS attacks and hence, the realization of DoS attack as a threat needs to be understood. The paper targets to assess the DDoS attack threat. It identifies the impact of attack and also reviews existing Indian laws.
10
Singhal, Prateek, Puneet Sharma, and Deepak Arora. "An approach towards preventing iot based sybil attack based on contiki framework through cooja simulator." International Journal of Engineering & Technology 7, no. 2.8 (2018): 261. http://dx.doi.org/10.14419/ijet.v7i2.8.10421.
Full textAbstract:
In this paper we propagate the Sybil attack in WSN (Wireless sensor network), by the researchers many attacks have been recognized in WSN till now and there are many attacks which can attack over through internet, Internet of thing means all devices is interconnected to each other M2M over internet and can be attacked by any of the attacker on any devices. Sybil attack is the detrimental attack against sensor network where several counterfeit identities and legitimate identities are used to get prohibited pass in a network. This is major attack which results an information loss and misinterpretation in the network, and it also minimizes the routing disturbance, trustworthiness and dropping sensitivity packets into a network. In this instance node can trust the imaginary node and sharing of information starts, owed to this security of node is get affected and information is lost. In this paper, a survey of CONTIKI OS-2.7, stimulation tool COOJA and the Sybil attack and proposed the defense mechanisms and CAM (Compare and Match) approach to verify the Sybil attack position and prevent it. This Sybil attack can be stimulated on the stimulation tool COOJA which helps to identify the attacker position node, whereas these attacks outcome in uni-casting as well as multicasting and in this paper specifically given the secure security for Wireless sensor network.
11
Tao, Xiao-ling, Lan Shi, Feng Zhao, Shen Lu, and Yang Peng. "A Hybrid Alarm Association Method Based on AP Clustering and Causality." Wireless Communications and Mobile Computing 2021 (March 30, 2021): 1–10. http://dx.doi.org/10.1155/2021/5576504.
Full textAbstract:
Internet of Things (IoT) brought great convenience to people’s daily lives. Meanwhile, the IoT devices are facing severe attacks from hackers and malicious attackers. Hackers and malicious attackers use various methods to invade the Internet of Things system, causing the Internet of Things to face a large number of targeted, concealed, and penetrating potential threats, which makes the privacy problem of the Internet of Things suffers serious challenges. But the existing methods and technologies cannot fully identify the attacker’s attack process and protect the privacy of the Internet of Things. Alarm correlation method can construct a complete attack scenario and identify the attacker’s intention by alarming the alarm data which provides an effective protection for user privacy. However, the existing alarm correlation methods still have the disadvantages of low correlation accuracy, poor correlation efficiency, and strong dependence on the knowledge base. To address these issues, we propose an alarm correlation method based on Affinity Propagation (AP) clustering algorithm and causal relationship. Our method considers that the alarm data triggered by the same attack process has high similarity characteristics, adopts the AP algorithm to improve the correlation efficiency, and at the same time constructs a complete attack process based on the causal correlation idea. The new alarm correlation method has a high correlation effect and builds a complete attack process to help managers identify attack intentions and prevent attacks.
12
Palacharla, Swetha, M. Chandan, K. GnanaSuryaTeja, and G. Varshitha. "Wormhole Attack: a Major Security Concern in Internet of Things (Iot)." International Journal of Engineering & Technology 7, no. 3.27 (2018): 147. http://dx.doi.org/10.14419/ijet.v7i3.27.17748.
Full textAbstract:
The Internet of Things (IoT) is nothing but a collection of wireless and wired devices, commonly termed as nodes operated remotely. This operation is done by assuming these nodes as the sensors in a wireless sensor network (WSN) administered through a base station. We start with briefing about IoT and then briefing IoT layer models. After this, we discuss attacks with regard to IoT namely Sinkhole attack, Sybil attack, HELLO flood attack, Acknowledgement spoofing attack and their respective detection methods. This paper is systematic review of existing mechanism for the detection of wormhole attack and a new method is proposed.
13
Ehmer, Jörg, Yvon Savaria, Bertrand Granado, Jean-Pierre David, and Julien Denoulet. "Network Attack Classification with a Shallow Neural Network for Internet and Internet of Things (IoT) Traffic." Electronics 13, no. 16 (2024): 3318. http://dx.doi.org/10.3390/electronics13163318.
Full textAbstract:
In recent years, there has been a tremendous increase in the use of connected devices as part of the so-called Internet of Things (IoT), both in private spaces and the industry. Integrated distributed systems have shown many benefits compared to isolated devices. However, exposing industrial infrastructure to the global Internet also generates security challenges that need to be addressed to benefit from tighter systems integration and reduced reaction times. Machine learning algorithms have demonstrated their capacity to detect sophisticated cyber attack patterns. However, they often consume significant amounts of memory, computing resources, and scarce energy. Furthermore, their training relies on the availability of datasets that accurately represent real-world data traffic subject to cyber attacks. Network attacks are relatively rare events, as is reflected in the distribution of typical training datasets. Such imbalanced datasets can bias the training of a neural network and prevent it from successfully detecting underrepresented attack samples, generally known as the problem of imbalanced learning. This paper presents a shallow neural network comprising only 110 ReLU-activated artificial neurons capable of detecting representative attacks observed on a communication network. To enable the training of such small neural networks, we propose an improved attack-sharing loss function to cope with imbalanced learning. We demonstrate that our proposed solution can detect network attacks with an F1 score above 99% for various attacks found in current intrusion detection system datasets, focusing on IoT device communication. We further show that our solution can reduce the false negative detection rate of our proposed shallow network and thus further improve network security while enabling processing at line rate in low-complexity network intrusion systems.
14
Ma, Jian Hong, and Li Xia Ji. "Shrew Attack on Internet Congestion Control Protocol in Control Engineering." Advanced Materials Research 648 (January 2013): 277–80. http://dx.doi.org/10.4028/www.scientific.net/amr.648.277.
Full textAbstract:
Low-rate Denial of Service(LDoS) attacks with their hidden,high efficiency features can significantly degrade service performance of large number of connection-oriented services,or even worse,thoroughly deny the services.Shrew attack is a typical LDoS attack.Firstly we studied the basic mechanism of the attack and congestion control.The source of adaptive congestion control mechanism in the security vulnerability was revealed according to the different levels of the intrinsic link between Internet congestion control at TCP layer and IP layer.Secondly,using the Network simulator NS2 software package,we set up attack model to simulate a large number of attack experiments with various congestion control mechanism and algorithms.Finally we draw the conclusions that continuous Shrew attack makes services nearly crash,while congestion control algorithms taking into account of fairness,such as Stochastic Fairness Queuing (SFQ) and Deficit Round Robin (DRR),can effectively suppress such kind of attack.
15
Patel, Anshuman, and Devesh Jinwala. "A Trust-Integrated RPL Protocol to Detect Blackhole Attack in Internet of Things." International Journal of Information Security and Privacy 15, no. 4 (2021): 1–17. http://dx.doi.org/10.4018/ijisp.2021100101.
Full textAbstract:
Internet of things (IoT) offers communication between user-to-machine and machine-to-machine. Due to their inherent characteristics of open medium, very dynamic topology, lack of infrastructure and lack of centralized management authority, IoT present serious vulnerabilities to security attacks. The routing protocol for low-power and lossy networks (RPL) does not have an inherent mechanism to detect routing attacks. Popular among these IoT attacks is blackhole attack. An attacker can exploit the routing system of RPL to launch blackhole attack against an IoT network. To secure IoT networks from blackhole attack, trust-integrated RPL protocol (TRPL) is proposed and implemented. The trust system is embedded in the RPL protocol to detect and isolate a blackhole attack while optimizing network performance. The trust is calculated from successful interaction between two nodes. The calculated trust value is considered in parent selection. TRPL demonstrates its superior performance over the standard RPL protocol and existing techniques in the detection and isolation of blackhole attacks.
16
Suryani, Vera, Selo Sulistyo, and Widyawan Widyawan. "Simulation of trust-based attacks in Internet of Things." MATEC Web of Conferences 154 (2018): 03014. http://dx.doi.org/10.1051/matecconf/201815403014.
Full textAbstract:
Security issue in Internet of Things (IoT) is one of the most important aspects to be resolved. Some attacks that occurred in Internet technology are also penetrated IoT. A trust-based security system is a promising solution in IoT as it is suitable for the characteristic of IoT which is distributed, decentralized control and on-off connections. Knowing the behaviors of the attacks might help up to understand of how we may prevent the attacks and plan to mitigate the attacks. In this paper, we simulated trust-based attacks in IoT environment by giving the fake reputation values of an object. For this purpose, we utilized ConTrust model, a trust-based security model. Matlab was used to simulate the attacks, and the simulation result showed that ConTrust model was outperformed on mitigating a trust-based attack. The attack was detected and resolved correctly.
17
Kamis, Noor Hisham, Warusia Yassin, Mohd Faizal Abdollah, Siti Fatimah Abdul Razak, and Sumendra Yogarayan. "Blackhole attacks in internet of things networks: a review." Indonesian Journal of Electrical Engineering and Computer Science 30, no. 2 (2023): 1080. http://dx.doi.org/10.11591/ijeecs.v30.i2.pp1080-1090.
Full textAbstract:
The internet of things (IoT) is one of data revolution area and is the following extraordinary mechanical jump after the internet. In terms of IoT, it is expected that electronic gadgets that are used on a regular basis would be connected to the current of the internet. IPv6 over low-power wireless personal area networks (6LoWPAN) is a one of IPv6 header pressure technology, and accordingly, it is vulnerable to attack. The IoT is a combination of devices with restricted resource assets like memory, battery power, and computational capability. To solve this, RPL or routing protocol for low power Lossy network is deploy by utilizing a distance vector scheme. One of denial of service (Dos) attack to RPL network is blackhole attack in which the assailant endeavors to become a parent by drawing in a critical volume of traffic to it and drop all packets. In this paper, we discuss research on numerous attacks and current protection methods, focusing on the blackhole attack. There is also discussion of challenge, open research issues and future perspectives in RPL security. Furthermore, research on blackhole attacks and specific detection technique proposed in the literature is also been presented.
18
Noor, Hisham Kamis, Yassin Warusia, Faizal Abdollah Mohd, Fatimah Abdul Razak Siti, and Yogarayan Sumendra. "Blackhole attacks in internet of things networks: a review." Blackhole attacks in internet of things networks: a review 30, no. 2 (2023): 1080–90. https://doi.org/10.11591/ijeecs.v30.i2.pp1080-1090.
Full textAbstract:
The internet of things (IoT) is one of data revolution area and is the following extraordinary mechanical jump after the internet. In terms of IoT, it is expected that electronic gadgets that are used on a regular basis would be connected to the current of the internet. IPv6 over low-power wireless personal area networks (6LoWPAN) is a one of IPv6 header pressure technology, and accordingly, it is vulnerable to attack. The IoT is a combination of devices with restricted resource assets like memory, battery power, and computational capability. To solve this, RPL or routing protocol for low power Lossy network is deploy by utilizing a distance vector scheme. One of denial of service (Dos) attack to RPL network is blackhole attack in which the assailant endeavors to become a parent by drawing in a critical volume of traffic to it and drop all packets. In this paper, we discuss research on numerous attacks and current protection methods, focusing on the blackhole attack. There is also discussion of challenge, open research issues and future perspectives in RPL security. Furthermore, research on blackhole attacks and specific detection technique proposed in the literature is also been presented.
19
Sikora, Marek, Radek Fujdiak, Karel Kuchar, Eva Holasova, and Jiri Misurec. "Generator of Slow Denial-of-Service Cyber Attacks." Sensors 21, no. 16 (2021): 5473. http://dx.doi.org/10.3390/s21165473.
Full textAbstract:
In today’s world, the volume of cyber attacks grows every year. These attacks can cause many people or companies high financial losses or loss of private data. One of the most common types of attack on the Internet is a DoS (denial-of-service) attack, which, despite its simplicity, can cause catastrophic consequences. A slow DoS attack attempts to make the Internet service unavailable to users. Due to the small data flows, these attacks are very similar to legitimate users with a slow Internet connection. Accurate detection of these attacks is one of the biggest challenges in cybersecurity. In this paper, we implemented our proposal of eleven major and most dangerous slow DoS attacks and introduced an advanced attack generator for testing vulnerabilities of protocols, servers, and services. The main motivation for this research was the absence of a similarly comprehensive generator for testing slow DoS vulnerabilities in network systems. We built an experimental environment for testing our generator, and then we performed a security analysis of the five most used web servers. Based on the discovered vulnerabilities, we also discuss preventive and detection techniques to mitigate the attacks. In future research, our generator can be used for testing slow DoS security vulnerabilities and increasing the level of cyber security of various network systems.
20
Maha, A. A. Mohammad, and M. T. Jawhar Muna. "Compare between PSO and artificial bee colony optimization algorithm in detecting DoS attacks from network traffic." TELKOMNIKA (Telecommunication, Computing, Electronics and Control) 20, no. 4 (2022): 780–87. https://doi.org/10.12928/telkomnika.v20i4.23757.
Full textAbstract:
Our world today relies heavily on informatics and the internet, as computers and communications networks have increased day by day. In fact, the increase is not limited to portable devices such as smartphones and tablets, but also to home appliances such as: televisions, refrigerators, and controllers. It has made them more vulnerable to electronic attacks. The denial of service (DoS) attack is one of the most common attacks that affect the provision of services and commercial sites over the internet. As a result, we decided in this paper to create a smart model that depends on the swarm algorithms to detect the attack of denial of service in internet networks, because the intelligence algorithms have flexibility, elegance and adaptation to different situations. The particle swarm algorithm and the bee colony algorithm were used to detect the packets that had been exposed to the DoS attack, and a comparison was made between the two algorithms to see which of them can accurately characterize the DoS attack.
21
Sharma, Vishal, Dushantha Nalin K. Jayakody, Kathiravan Srinivasan, and Ravinder Kumar. "Coagulation Attacks over Networked UAVs: Concept, Challenges, and Research Aspects." International Journal of Engineering & Technology 7, no. 3.13 (2018): 183. http://dx.doi.org/10.14419/ijet.v7i3.13.17329.
Full textAbstract:
Unmanned Aerial Vehicles (UAVs) have grasped an important role in the modern day networking. A lot of applications are being developed using aerial vehicles as a pivot. These vehicles provide a vast range of support to modern day networks. Modern computing applications such as Urban Computing, Internet of Things, Ubiquitous Computing, and the Internet for All have sought applications of UAVs to attain complex tasks. However, securing aerial vehicles in a network is not an easy task because of the difference in communication standards and range of applicability. Aerial nodes are prone to various types of attack in a network such as Sybil attack, wormhole attack, sinkhole attack, or impersonation attack. These attacks lead to a large number of vulnerabilities causing fatal incidents. A new attack is introduced in this paper termed as "Coagulation Attack". This term is derived from the clotting properties of fluids. This paper introduces the concept, issues, challenges, and research aspects of coagulation attack. A simulation study is also presented that shows the impact of such attacks over networked UAVs.
22
Egreira Ali Abuhamra, Eng Abdalgader. "Concept of Network security." International Journal of Advances in Engineering and Management 7, no. 3 (2025): 384–90. https://doi.org/10.35629/5252-0703384390.
Full textAbstract:
Network security incorporates various technologies, processes, and devices into a broad strategy that protects the integrity, confidentiality, and accessibility of computer networks. Organizations of all sizes, industries, or infrastructure types require network security to protect against an ever-evolving cyber threat landscape.The security is a most important part of every network design. Planning, building, and operating a network, it should understand the importance of a strong security rule. Network Security is a security rule that defines what people can and can't do with network components and resources. The fundamental purpose of a network security is to protect against attacks from the Internet. There are many different ways of attacking a network such as: Hacker attacks whereby a remote Internet user attempts to gain access to a network, usually with the intention to destroy or copy data. The major attacks to network security are passive attack, active attack, distributed attack, insider attack; close in attack, Phishing Attack, Hijack attack, Password attack etc. However a system must be able to limit damage and recover rapidly when attacks occur. So there are various solutions when any of above attacks occurs. Some of the common solutions of these attacks are firewalls, user account access controls and cryptography. The first major challenge for network security is the rapid evolution of the cyber threat landscape. Technologies evolve quickly, and attackers find new ways to infiltrate and exploit corporate networks, requiring businesses to implement new defenses to protect their networks.
23
D., Glăvan. "DDoS detection and prevention based on artificial intelligence techniques." Scientific Bulletin of Naval Academy XXII, no. 1 (2019): 134–43. http://dx.doi.org/10.21279/1454-864x-19-i1-018.
Full textAbstract:
Distributed Denial of Service (DDoS) attacks have been the major threats for the Internet and can bring great loss to companies and governments. With the development of emerging technologies, such as cloud computing, Internet of Things (IoT), artificial intelligence techniques, attackers can launch a huge volume of DDoS attacks with a lower cost, and it is much harder to detect and prevent DDoS attacks, because DDoS traffic is similar to normal traffic. Some artificial intelligence techniques like machine learning algorithms have been used to classify DDoS attack traffic and detect DDoS attacks, such as Naive Bayes and Random forest tree. In the paper, we survey on the latest progress on the DDoS attack detection using artificial intelligence techniques and give recommendations on artificial intelligence techniques to be used in DDoS attack detection and prevention.
24
K.M, Akhil, Rahul C.T, and Athira V.B. "Distributed Denial of Service (DDoS) Attacks and Defence Mechanism." International Journal of Computer Science and Mobile Computing 10, no. 3 (2021): 83–88. http://dx.doi.org/10.47760/ijcsmc.2021.v10i03.010.
Full textAbstract:
Denial of Service (DoS) attacks is one of the major threats to Internet sites and one of the major security problems Internet faces today. The nature of threats caused by Distributed Denial of Service (DDoS) attacks on networks. With little or no warning, a DDoS attack could easily destroy its victim's communication and network resources in a short period of time. This paper outlines the problem of DDoS attacks and developing a classification of DDoS attacks and DDoS defense mechanisms. Important features of each attack and defense system category are described and advantages and disadvantages of each proposed scheme are outlined. The goal of the paper is to set a certain order of existence methods of attack and defense mechanisms, for the better understanding DDoS attacks can be achieved with more effective methods and means of self-defense can be developed.
25
Alam, Hasibul, and Emmett Tomai. "Security Attacks and Countermeasures in Smart Homes." International Journal on Cybernetics & Informatics 12, no. 2 (2023): 109–19. http://dx.doi.org/10.5121/ijci.2023.120209.
Full textAbstract:
The Internet of Things (IoT) application is visible in all aspects of humans’ day-to-day affairs. The demand for IoT is growing at an unprecedented rate, from wearable wristwatches to autopilot cars. The smart home has also seen significant advancements to improve the quality of lifestyle. However, the security and privacy of IoT devices have become primary concerns as data is shared among intelligent devices and over the internet in a smart home network. There are several attacks - node capturing attack, sniffing attack, malware attack, boot phase attack, etc., which are conducted by adversaries to breach the security of smart homes. The security breach has a negative impact on the tenants' privacy and prevents the availability of smart home services. This article presents smart homes' most common security attacks and mitigation techniques.
26
Khan, Faheem, Abdullah A. Al-Atawi, Abdullah Alomari, et al. "Development of a Model for Spoofing Attacks in Internet of Things." Mathematics 10, no. 19 (2022): 3686. http://dx.doi.org/10.3390/math10193686.
Full textAbstract:
Internet of Things (IoT) allows the integration of the physical world with network devices for proper privacy and security in a healthcare system. IoT in a healthcare system is vulnerable to spoofing attacks that can easily represent themselves as a legal entity of the network. It is a passive attack and can access the Medium Access Control address of some valid users in the network to continue malicious activities. In this paper, an algorithm is proposed for detecting spoofing attacks in IoT using Received Signal Strength (RSS) and Number of Connected Neighbors (NCN). Firstly, the spoofing attack is detected, located and eliminated through Received Signal Strength (RSS) in an inter-cluster network. However, the RSS is not useful against intra-cluster spoofing attacks and therefore the NCN is introduced to detect, identify and eliminate the intra-cluster spoofing attack. The proposed model is implemented in Network Simulator 2 (NS-2) to compare the performance of the proposed algorithm in the presence and absence of spoofing attacks. The result is that the proposed model increases the detection and prevention of spoofing.
27
Veeraiah, Duggineni, Rajanikanta Mohanty, Shakti Kundu, et al. "Detection of Malicious Cloud Bandwidth Consumption in Cloud Computing Using Machine Learning Techniques." Computational Intelligence and Neuroscience 2022 (September 5, 2022): 1–9. http://dx.doi.org/10.1155/2022/4003403.
Full textAbstract:
The Internet of Things, sometimes known as IoT, is a relatively new kind of Internet connectivity that connects physical objects to the Internet in a way that was not possible in the past. The Internet of Things is another name for this concept (IoT). The Internet of Things has a larger attack surface as a result of its hyperconnectivity and heterogeneity, both of which are characteristics of the IoT. In addition, since the Internet of Things devices are deployed in managed and uncontrolled contexts, it is conceivable for malicious actors to build new attacks that target these devices. As a result, the Internet of Things (IoT) requires self-protection security systems that are able to autonomously interpret attacks in IoT traffic and efficiently handle the attack scenario by triggering appropriate reactions at a pace that is faster than what is currently available. In order to fulfill this requirement, fog computing must be utilised. This type of computing has the capability of integrating an intelligent self-protection mechanism into the distributed fog nodes. This allows the IoT application to be protected with the least amount of human intervention while also allowing for faster management of attack scenarios. Implementing a self-protection mechanism at malicious fog nodes is the primary objective of this research work. This mechanism should be able to detect and predict known attacks based on predefined attack patterns, as well as predict novel attacks based on no predefined attack patterns, and then choose the most appropriate response to neutralise the identified attack. In the environment of the IoT, a distributed Gaussian process regression is used at fog nodes to anticipate attack patterns that have not been established in the past. This allows for the prediction of new cyberattacks in the environment. It predicts attacks in an uncertain IoT setting at a speedier rate and with greater precision than prior techniques. It is able to effectively anticipate both low-rate and high-rate assaults in a more timely manner within the dispersed fog nodes, which enables it to mount a more accurate defence. In conclusion, a fog computing-based self-protection system is developed to choose the most appropriate reaction using fuzzy logic for detected or anticipated assaults using the suggested detection and prediction mechanisms. This is accomplished by utilising a self-protection system that is based on the development of a self-protection system that utilises the suggested detection and prediction mechanisms. The findings of the experimental investigation indicate that the proposed system identifies threats, lowers bandwidth usage, and thwarts assaults at a rate that is twenty-five percent faster than the cloud-based system implementation.
28
Tyas, Zahra Arwananing, Arizona Firdonsyah, and Wulan Ramdhani. "Analisis Keamanan Jaringan dari Serangan DoS pada Sistem Inventaris Sanggar Tari Natya Lakshita menggunakan IDS." INFORMAL: Informatics Journal 7, no. 3 (2022): 258. http://dx.doi.org/10.19184/isj.v7i3.34943.
Full textAbstract:
Information technology is advancing very quickly. Currently, there is highly qualified support for internet facilities. A quality internet service has both advantages and disadvantages for its users. An example of the negative impact of the development of internet technology is attacks from irresponsible parties or often called hackers. Sanggar Tari Natya Lakshita’s Inventory System is a newly built system, so attack testing is needed because network security is important to maintain data integration in the network. The test is carried out using a DoS attack with the LOIC tool. IDS is the method used in research with the Snort tool that functions as an intruder detector. The study aims to determine the results and effects of attacks on the Inventory System and determine how the IDS framework works with the Snort tool in detecting attacks. carried using 1 computer and 1 laptop. The results of the attack on the inventory system showed that Snort succeeded in detecting an attack sent by LOIC by displaying the attacker's IP and IP target through port 80, then the recommendations were given related to the stages of handling network security based on CSIRT guidelines at Sanggar Tari Natya Lakshita that can be implemented.
29
Neerugatti, Vikram, and A. Rama Mohan Reddy. "Acknowledgement Based Technique for Detection of the Wormhole Attack in RPL Based Internet of Things Networks." Asian Journal of Computer Science and Technology 8, S3 (2019): 100–104. http://dx.doi.org/10.51983/ajcst-2019.8.s3.2075.
Full textAbstract:
Internet of Things (IoT) is the advanced technology, were the constrained nodes/things (all the objects around us such as chair, home, car, keys, etc.) will be connected to the internet to form a network, for sharing and monitoring the data, remotely. RPL (IPv6 Routing Protocol for Low Power and Lossy networks) is a routing protocol particularly designed for the constrained (low powered, low computation, less size, etc.) networks with the protocol 6LoWPAN (IPv6 Low Powered wireless Personal Area Networks). Due to the constrained behaviour of the RPL protocol, it will leads to many RPL routing attacks such as Sinkhole, Black hole, Wormhole, Selective forwarding, rank attacks, etc. This paper was focused on the Wormhole attack. The Wormhole attack will select the packets from one location and drops those packets in some other location (malicious) by forming the Tunnelling. To detect this attack here proposed and implemented a novel approach called (ADWA). Acknowledgement based technique for detection of the wormhole attack in RPL based Internet of Things networks. This approach was shown efficient results with the Telosb sky emulator nodes in the Contiki Cooja simulator, in terms of the Packet delivery ratio, delay and detection of wormhole attack.
30
Alyami, Sarah, Randah Alharbi, and Farag Azzedin. "Fragmentation Attacks and Countermeasures on 6LoWPAN Internet of Things Networks: Survey and Simulation." Sensors 22, no. 24 (2022): 9825. http://dx.doi.org/10.3390/s22249825.
Full textAbstract:
The Internet of things is a popular concept in the current digital revolution. Nowadays, devices worldwide can be connected to the Internet, enhancing their communication, capabilities, and intelligence. Low-Power Wireless Personal Area Network (6LoWPAN) was specifically designed to build wireless networks for IoT resource-constrained devices. However, 6LoWPAN is susceptible to several security attacks. The fragmentation mechanism, in particular, is vulnerable to various attacks due to the lack of fragment authentication and verification procedures in the adaptation layer. This article provides a survey of fragmentation attacks and available countermeasures. Furthermore, the buffer reservation attack, one of the most harmful fragmentation attacks that may cause DoS, is studied and simulated in detail. A countermeasure for this attack is also implemented based on a reputation-scoring scheme. Experiments showed the harmful effects of the buffer reservation attack and the effectiveness of the implemented reputation-scoring countermeasure.
31
Dougan, Timothy, and Kevin Curran. "Man in the Browser Attacks." International Journal of Ambient Computing and Intelligence 4, no. 1 (2012): 29–39. http://dx.doi.org/10.4018/jaci.2012010103.
Full textAbstract:
Man-in-the-Browser attacks are a sophisticated new hacking technique associated with Internet crime, especially that which targets customers of Internet banking. The security community has been aware of them as such for time but they have grown in ability and success during that time. These attacks are a specialised version of Man-in-the-Middle attack, and operate by stealing authentication data and altering legitimate user transactions to benefit the attackers. This paper examines what Man-in-the-Browser attacks are capable of and how specific versions of the attack are executed, with reference to their control structure, data interaction techniques, and methods for circumventing security. Finally the authors discuss the effectiveness of counter-Man-in-the-Middle strategies, and speculate upon what these attacks tell us about the Internet environment.
32
Hisyam, Masfu, Ali Ridho Barakbah, Iwan Syarif, and Ferry Astika S. "Spatio Temporal with Scalable Automatic Bisecting-Kmeans for Network Security Analysis in Matagaruda Project." EMITTER International Journal of Engineering Technology 7, no. 1 (2019): 83–104. http://dx.doi.org/10.24003/emitter.v7i1.340.
Full textAbstract:
Internet attacks are a frequent occurrence and the incidence is always increasing every year, therefore Matagaruda project is built to monitor and analyze internet attacks using IDS (Intrusion Detection System). Unfortunately, the Matagaruda project has lacked in the absence of trend analysis and spatiotemporal analysis. It causes difficulties to get information about the usual seasonal attacks, then which sector is the most attacked and also the country or territory where the internet attack originated. Due to the number of unknown clusters, this paper proposes a new method of automatic bisecting K-means with the average of SSE is 93 percents better than K-means and bisecting K-means. The usage of big spark data is highly scalable for processing massive data attack.
33
Agghey, Abel Z., Lunodzo J. Mwinuka, Sanket M. Pandhare, Mussa A. Dida, and Jema D. Ndibwile. "Detection of Username Enumeration Attack on SSH Protocol: Machine Learning Approach." Symmetry 13, no. 11 (2021): 2192. http://dx.doi.org/10.3390/sym13112192.
Full textAbstract:
Over the last two decades (2000–2020), the Internet has rapidly evolved, resulting in symmetrical and asymmetrical Internet consumption patterns and billions of users worldwide. With the immense rise of the Internet, attacks and malicious behaviors pose a huge threat to our computing environment. Brute-force attack is among the most prominent and commonly used attacks, achieved out using password-attack tools, a wordlist dictionary, and a usernames list—obtained through a so-called an enumeration attack. In this paper, we investigate username enumeration attack detection on SSH protocol by using machine-learning classifiers. We apply four asymmetrical classifiers on our generated dataset collected from a closed-environment network to build machine-learning-based models for attack detection. The use of several machine-learners offers a wider investigation spectrum of the classifiers’ ability in attack detection. Additionally, we investigate how beneficial it is to include or exclude network ports information as features-set in the process of learning. We evaluated and compared the performances of machine-learning models for both cases. The models used are k-nearest neighbor (K-NN), naïve Bayes (NB), random forest (RF) and decision tree (DT) with and without ports information. Our results show that machine-learning approaches to detect SSH username enumeration attacks were quite successful, with KNN having an accuracy of 99.93%, NB 95.70%, RF 99.92%, and DT 99.88%. Furthermore, the results improve when using ports information.
34
Researcher. "AN ARCHITECTURE FOR A DISTRIBUTED CLOUD HONEYPOT." International Journal of Computer Engineering and Technology (IJCET) 15, no. 6 (2024): 488–500. https://doi.org/10.5281/zenodo.14183575.
Full textAbstract:
DDoS assaults present a significant menace to the Internet. With the increasing prevalence of the Internet of Things (IoT), the DDoS attack has evolved beyond a simple traffic attack. The primary focus of the attack has shifted from the network layer to the application layer. In addition, DDoS attacks utilizing botnets yield more devastating consequences. The objective of this study is to introduce a novel collaborative active defensive framework that combines Honeypot and cloud platform technologies. This framework is designed to identify and protect against future Distributed Denial of Service (DDoS) assaults inside the Internet of Things (IoT) context. The framework is capable of detecting and mitigating large volumes of malicious traffic, measured in Terabytes, in real-time.
35
Venkatachalam, Nallarasan, and Kottilingam Kottursamy. "Jamming Attack Mitigation in CR-IoT Using Game Theory." Revue d'Intelligence Artificielle 36, no. 4 (2022): 615–20. http://dx.doi.org/10.18280/ria.360414.
Full textAbstract:
Jamming attack is Cognitive radio Internet of thing network disables the spectrum sharing and reduces the spectrum utilization .detection and mitigation of such attacks is the main component in realizing cognitive radio-based spectrum sharing in cognitive radio-based internet of thing network this work proposes a game theory-based jamming attack mitigation strategy. The problem of jamming attack mitigation is modeled as a zero-sum game and solved by finding Nash equilibrium. The cognitive node which tries to share the spectrum pays the zero-sum game with the jamming attacker and selects the best strategy of selecting the best frequency without getting into the jamming attack. The result of the proposed mechanism proves that the gaming mechanism can tackle the jamming attack.
36
Singh, Har Preet. "Survey of new attack models on Cloud Infrastructure." International Journal Of Engineering And Computer Science 7, no. 03 (2018): 23742–52. http://dx.doi.org/10.18535/ijecs/v7i3.15.
Full textAbstract:
Cloud Computing is currently most useful technol- ogy around the globe which offers an innovative business models for infrastructure to the enterprise , software services to the end users and the easy to deploy platform to the developer on Internet using virtual machine , for the quick and easy accessibility. It is Internet driven technology, which gives pool of resources such as Storage , Network , Application on demand basis. As the technology is driven over the Internet and virtual machine and allow resource pooling their are various kind of security problem arise relate to the model architecture, multi-tenancy, elasticity, data confidentiality, authentication and authorization. Various kind of attack could happen in cloud infrastructure ,as there is no exact definition about the attack and attack model but here we try to group into a various levels eg. Network level, host level and application level and few other attacks and the solution to prevent the attacks. In this paper we will discuss about the different kind of attacks and solution on cloud services
37
Goth, G. "Securing the internet against attack." IEEE Internet Computing 7, no. 1 (2003): 8–10. http://dx.doi.org/10.1109/mic.2003.1167332.
Full text
38
A. Sathiya Priya and A. Sandhiya. "Machine learning based Cyber Attack detection on Internet Traffic." International Journal of Science and Research Archive 11, no. 2 (2024): 619–24. http://dx.doi.org/10.30574/ijsra.2024.11.2.0459.
Full textAbstract:
Cyber attacks on the internet have become increasingly sophisticated and frequent, posing significant challenges to cybersecurity. Traditional rule-based methods for detecting these attacks often struggle to keep pace with the evolving tactics of malicious actors. In this context, machine learning (ML) techniques have emerged as a promising approach for cyber attack detection due to their ability to analyze large volumes of data and identify patterns indicative of malicious behavior. The proposed framework for utilizing machine learning in cyber attack detection on the internet. The framework integrates various ML algorithms, including supervised, unsupervised, and reinforcement learning techniques, to enhance the detection capabilities against different types of cyber threats. Moreover, the framework incorporates feature engineering and selection methods to optimize the performance of ML models in identifying malicious activities.
39
El Kamel, Nadiya, Mohamed Eddabbah, Youssef Lmoumen, and Raja Touahni. "A Smart Agent Design for Cyber Security Based on Honeypot and Machine Learning." Security and Communication Networks 2020 (August 7, 2020): 1–9. http://dx.doi.org/10.1155/2020/8865474.
Full textAbstract:
The development of Internet and social media contributes to multiplying the data produced on the Internet and the connected nodes, but the default installation and the configuration of variety of software systems represent some security holes and shortcomings, while the majority of Internet users have not really set up safety awareness, leading to huge security risks. With the development of network attack techniques, every host on the Internet has become the target of attacks. Therefore, the network information security cannot be ignored as a problem. To deal with 0-day and future attacks, the honeypot technique can be used not only passively as an information system, but also to reinforce the traditional defense systems against future attacks. In this paper, we present an introduction of machine learning and honeypot systems, and based on these technologies, we design a smart agent for cyber-attack prevention and prediction.
40
Yan, Jingchen, Zhe Du, Jifang Li, Shiduo Yang, Jinghao Li, and Jianbin Li. "A Threat Intelligence Analysis Method Based on Feature Weighting and BERT-BiGRU for Industrial Internet of Things." Security and Communication Networks 2022 (February 25, 2022): 1–11. http://dx.doi.org/10.1155/2022/7729456.
Full textAbstract:
The combination of 5G technology and the industrial Internet of things (IIoT) makes it possible to realize the interconnection of all things. Still, it also increases the risk of attacks such as large-scale DDoS attacks and IP spoofing attacks. Threat intelligence is a collection of information causing potential and nonpotential harm to the industrial Internet. Extracting network security entities and their relationships from threat intelligence text and constructing structured threat intelligence information are particularly important for IIoT security protection. However, threat intelligence is mostly text reports, which means the value information needs to be extracted manually by security analysts, and it is highly dependent on personnel experience. Therefore, this study proposes an IIoT threat intelligence analysis method based on feature weighting and BERT-BiGRU. In this method, BERT-BiGRU is used to classify attack behavior and attack strategy. Then, the attack behavior is weighted to make the classified result more accurate according to the relationship between attack strategy and attack behavior in ATT&CK for ICS knowledge. Finally, the possibility of attack and the harm degree of attack are calculated to form the threat value of the attack. The security analysts can judge the emergency response sequence by the threat value to improve the accuracy and efficiency of emergency response. The results indicate that the proposed method in this study is more accurate than the other standard methods and is more suitable for the unstructured threat intelligence analysis of IIoT.
41
Bhargavi Goparaju and Bandla Sreenivasa Rao. "Distributed Denial-of-Service (DDoS) Attack Detection using 1D Convolution Neural Network (CNN) and Decision Tree Model." Journal of Advanced Research in Applied Sciences and Engineering Technology 32, no. 2 (2023): 30–41. http://dx.doi.org/10.37934/araset.32.2.3041.
Full textAbstract:
The major problem of internet security is a Distributed Denial-of-Service (DDoS) attack, which can’t be detected easily. This attack is said to have occurred when lots of service requests are simultaneously received at a server on the internet. This makes the server too busy to provide normal services for others. The Distributed Denial of Service (DDoS) attacks nature on large networks on the Internet demanding to develop the effective detection and response methods. The deployment of these technique should perform not only at the network core but also at the edge. A DDoS attack detection framework is presented based on transfer learning model consisting of 1D Convolution Neural Network (CNN) and decision tree classifier. The 1D CNN model utilizes for features extraction from the input network traffic data. This operation also reduces the dimension of the data thereby removing the redundancy in the data. These features are given to the decision tree model for classification. The proposed framework identified the DDoS attacks with good accuracy. This system could identify attacks in real-time and provide network security.
42
Ahanger, Tariq Ahamad. "Defense Scheme to Protect IoT from Cyber Attacks using AI Principles." International Journal of Computers Communications & Control 13, no. 6 (2018): 915–26. http://dx.doi.org/10.15837/ijccc.2018.6.3356.
Full textAbstract:
Even in its infancy, the internet of things (IoT) has enticed most of the modern industrial areas like smart cities, automobiles, medical technology. Since IoT connects everything together, it is vulnerable to a variety of devastating intrusion attacks. Being the internet of different devices makes it easy for attackers to launch their attacks. Thus, to combat all these attacks, an attack analysis is presented in this article using the basic principles of Artificial Neural Networks. Internet packet traces are used to train to the supervised ANN (Multilevel Perceptron) and evaluated after the training to decline the DDoS Attacks. This research article mainly focuses on the categorization of traffic patterns into legitimate traffic and attack traffic patterns in IoT network. The ANN processes are evaluated and tested in a simulated IoT network. The experimental results show a greater accuracy in detection of various DDoS attacks.
43
Kumavat, Kavita S., and Joanne Gomes. "Common Mechanism for Detecting Multiple DDoS Attacks." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 4 (2023): 81–90. http://dx.doi.org/10.17762/ijritcc.v11i4.6390.
Full textAbstract:
An important principle of an internet-based system is information security. Information security is a very important aspect of distributed systems and IoT (Internet of Things) based wireless systems. The attack which is more harmful to the distributed system and IoT-based wireless system is a DDoS (Distributed Denial of Service) attack since in this attack, an attacker can stop the work of all other connected devices or users to the network. For securing distributed applications, various intrusion detection mechanisms are used. But most existing mechanisms are only concentrated on one kind of DDoS attack. This paper focuses on the basic architecture of IoT systems and an overview of single intrusion detection systems. This paper presents a single detection method for different DDoS attacks on distributed systems with an IoT interface. In the future, the system will provide support for detecting and preventing different DDoS attacks in IoT-based systems.
44
Ramli, Hartini, and Maharaja Yasin Alifsyah. "Analisis Keamanan Komputer Terhadap Serangan Distributed Denial of Service (DDOS)." Journal of Renewable Energy and Smart Device 1, no. 1 (2023): 25–30. http://dx.doi.org/10.61220/joresd.v1i1.235.
Full textAbstract:
Distributed Denial of Service (DDoS) is a type of active attack, an attack that can overwhelm a system by flooding a computer or server with network traffic, disrupting user services. The goal of this attack is usually to disable services and disconnect from the compromised computer or network. The impact is very large for companies or agencies that offer services. Victims of these attacks are unable to provide the services they are supposed to. Due to a bug or constraint on the server you are trying to use and one of the ways to deal with these attacks is to use a computer network firewall, which is useful for protecting computers from various outer space attacks. If the computer has a firewall security system, it is likely that no one on the Internet can access the data on the connected computer or web server. Firewall, works like a partition or wall that blocks the computer from the Internet. This "firewall" allows you to control what data, information, and activity can be transferred from the Internet to your computer and vice versa. With better data security and can avoid DDOS attacks that want to be carried out by irresponsible parties.
45
J, Manokaran, and Vairavel G. "An Empirical Comparison of Machine Learning Algorithms for Attack Detection in Internet of Things Edge." ECS Transactions 107, no. 1 (2022): 2403–17. http://dx.doi.org/10.1149/10701.2403ecst.
Full textAbstract:
This research work is aimed to perform a comparative analysis of different machine learning algorithms for attack detection at the Internet of Things (IoT) edge. Due to the rising development of IoT, attack detection has become extremely important in network security, as it protects the IoT network from suspicious activities. The self-configuring and open nature of IoT devices is vulnerable to both internal and external attacks. The statistical method of attack detection is not suitable for fast and accurate detection due to the multi-dimensional nature of attacks. Machine learning-based edge computing can rectify these issues through automated response and shifting the computation physically closer to the device edge where the information is generated. In this paper, we have compared the performances of eight machine learning (ML) algorithms to identify the optimal ML algorithm for attack detection in IoT Edge.
46
Shi, Fan, Pengcheng Zhu, Xiangyu Zhou, Bintao Yuan, and Yong Fang. "Network attack detection and visual payload labeling technology based on Seq2Seq architecture with attention mechanism." International Journal of Distributed Sensor Networks 16, no. 4 (2020): 155014772091701. http://dx.doi.org/10.1177/1550147720917019.
Full textAbstract:
In recent years, Internet of things (IoT) devices are playing an important role in business, education, medical as well as in other fields. Devices connected to the Internet is much more than the number of world population. However, it may face all kinds of attacks from the Internet easily for its accessibility. As we all know, most attacks against IoT devices are based on Web applications. So protecting the security of Web services can effectively improve the situation of IoT ecosystem. Conventional Web attack detection methods highly rely on samples, and artificial intelligence detection results are uninterpretable. Hence, this article introduced a supervised detection algorithm based on benign samples. Seq2Seq algorithm is been chosen and applied to detect malicious web requests. Meanwhile, the attention mechanism is introduced to label the attack payload and highlight labeling abnormal characters. The results of experiments show that on the premise of training a benign sample, the precision of proposed model is 97.02%, and the recall is 97.60%. It explains that the model can detect Web attack requests effectively. Simultaneously, the model can label attack payload visually and make the model “interpretable.”
47
Ma, Yao, Yuting Wu, Dan Yu, Lv Ding, and Yongle Chen. "Vulnerability association evaluation of Internet of thing devices based on attack graph." International Journal of Distributed Sensor Networks 18, no. 5 (2022): 155013292210978. http://dx.doi.org/10.1177/15501329221097817.
Full textAbstract:
Device vulnerabilities emerge one after another in the Internet of thing environment, the attackers attack vulnerabilities on several low-level devices simultaneously by multi-step attack method to trigger the vulnerabilities on other high-level devices to damage or control the information system. Considering the correlation between device vulnerabilities, we proposed a method based on attack graph to evaluate vulnerability risk in order to ensure Internet of thing network security. First, according to the type, version, and other relevant information of device vulnerabilities in the Internet of thing environment, hidden Markov model can be used to model the association between device states. Second, analyze the possible attacks on the vulnerabilities on the device, and generate the attack graph according to the correlation between the device states and the relevant information of the vulnerabilities in the device. Finally, the vulnerabilities are objectively and accurately evaluated according to the attack graph. The experiments results show that the proposed method can map the relationship between devices more accurately and objectively and improve the efficiency and accuracy of the vulnerability evaluation.
48
Riyadh, Rahef Nuiaa, Manickam Selvakumar, and Hakem Alsaeedi Ali. "Distributed reflection denial of service attack: A critical review." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 6 (2021): 5327–41. https://doi.org/10.11591/ijece.v11i6.pp5327-5341.
Full textAbstract:
As the world becomes increasingly connected and the number of users grows exponentially and “things” go online, the prospect of cyberspace becoming a significant target for cybercriminals is a reality. Any host or device that is exposed on the internet is a prime target for cyberattacks. A denial-of-service (DoS) attack is accountable for the majority of these cyberattacks. Although various solutions have been proposed by researchers to mitigate this issue, cybercriminals always adapt their attack approach to circumvent countermeasures. One of the modified DoS attacks is known as distributed reflection denial-of-service attack (DRDoS). This type of attack is considered to be a more severe variant of the DoS attack and can be conducted in transmission control protocol (TCP) and user datagram protocol (UDP). However, this attack is not effective in the TCP protocol due to the three-way handshake approach that prevents this type of attack from passing through the network layer to the upper layers in the network stack. On the other hand, UDP is a connectionless protocol, so most of these DRDoS attacks pass through UDP. This study aims to examine and identify the differences between TCP-based and UDP-based DRDoS attacks.
49
Mishra, Chinmayee, Pullam Bhatla Laxmi Sindhu, Pruthwiraj Mohanty, and Ayush Kumar Samrat. "DDOS Attacks and Analysis of Different Defense Mechanisms." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 12 (2024): 1–6. https://doi.org/10.55041/ijsrem40061.
Full textAbstract:
Denial of Service( DOS) attacks are an immense trouble to internet spots and among the hardest security problems in moment’s Internet. Of particular concern- because of their implicit impact- are the Distributed Denial of Service( DDoS) attacks. With little or no advance advising a DDoS attack can fluently exhaust the computing and communication coffers of its victim within a short period of time. This paper presents the problem of DDoS attacks and develops a bracket of DDoS defence systems. Description of each attack and defence system order is provided ,along with the advantages and disadvantages of each approach. The purpose of the study is to organise the existing attack and defence mechanisms to improve knowledge of DDoS attacks and develop more potent defence strategies. In this work, the types of attacks, test characteristics, evaluation techniques are classified and delineated in the review; evaluation methods and test materials used in the methodology of the proposed strategic strategy. Finally, this work provides guidance and possible goals in the struggle to create better events most threat Types of cyber-attacks, or DDoS attacks.
50
Johnson, Brian David. "Efficiency is Easy to Hack." Mechanical Engineering 139, no. 08 (2017): 38–43. http://dx.doi.org/10.1115/1.2017-aug-2.
Full textAbstract:
This article focuses on the possible cyberattacks as the current generation go Internet-connected way. Connecting an appliance to the Internet provides not only the opportunity for added functions and efficiency, but also the potential for hackers to exploit security lapses. As the attack plain begins to expand and digital attacks spread and become individual, physical, or even kinetic in nature, the calculus will change. Cybersecurity experts have demonstrated that Internet-connected vehicles are vulnerable to attack by hackers. As more physical systems undergo a wave of artificial intelligence (AI)-driven automation with the driving factor being efficiency, those systems become increasingly vulnerable to attack. The article concludes that traditional engineering has long optimized for things like cost, efficiency, or simplicity. Internet-connected machines and IoT-enabled devices will allow systems to do amazing things, but they also create opportunities for bad actors to turn these systems against us.