scholarly journals Cyber-Attacks Detection Using Novel IDDMS Framework

2020 ◽  
Author(s):  
Gadekar Ganesh Bhivsen ◽  
Udayabhanu N P G ◽  
Dange Bapusaheb Jalindar ◽  
Vengatesan K ◽  
Abhishek Kumar
Keyword(s):  
Data Mining ◽  
Intrusion Detection ◽  
Cyber Security ◽  
Detection System ◽  
Cyber Attacks ◽  
The Internet ◽  
System Framework ◽  
Cyber Terrorism ◽  
Mining System ◽  
Data Mining System

Security of a data system is a significant property, particularly today when PCs are interconnected by means of the internet. Since no system can be totally secure, the opportune and precise detection of intrusions is essential. Cyber security is the region that manages shielding from cyber terrorism. Cyber-attacks incorporate access control infringement, unapproved intrusions, and disavowal of service just as insider risk. For this reason, IDS were planned. The IDS in the mix with DM can give security to the next level. DM is the way toward presenting inquiries and separating designs, frequently already ambiguous from huge amounts of data utilizing design coordinating or other thinking techniques. This Paper gives the IDDMS (Intrusion Detection with Data Mining system) Framework which is a mix of data mining techniques with the Intrusion detection system, this can be utilized in Cyber-security for accomplishing the next level of service.

scholarly journals Towards Deep-Learning-Driven Intrusion Detection for the Internet of Things

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 1977 ◽  
Author(s):  
Geethapriya Thamilarasu ◽  
Shiven Chawla
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Learning Algorithm ◽  
Detection System ◽  
Cyber Attacks ◽  
The Internet ◽  
Critical Systems ◽  
The Internet Of Things

Cyber-attacks on the Internet of Things (IoT) are growing at an alarming rate as devices, applications, and communication networks are becoming increasingly connected and integrated. When attacks on IoT networks go undetected for longer periods, it affects availability of critical systems for end users, increases the number of data breaches and identity theft, drives up the costs and impacts the revenue. It is imperative to detect attacks on IoT systems in near real time to provide effective security and defense. In this paper, we develop an intelligent intrusion-detection system tailored to the IoT environment. Specifically, we use a deep-learning algorithm to detect malicious traffic in IoT networks. The detection solution provides security as a service and facilitates interoperability between various network communication protocols used in IoT. We evaluate our proposed detection framework using both real-network traces for providing a proof of concept, and using simulation for providing evidence of its scalability. Our experimental results confirm that the proposed intrusion-detection system can detect real-world intrusions effectively.

Threat Detection in Cyber Security Using Data Mining and Machine Learning Techniques

2020 ◽  
pp. 234-253
Author(s):  
Daniel Kobla Gasu
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Intrusion Detection ◽  
Cyber Security ◽  
Attack Detection ◽  
Machine Learning Techniques ◽  
The Internet ◽  
Threat Detection ◽  
Use Of The Internet ◽  
Using Data

The internet has become an indispensable resource for exchanging information among users, devices, and organizations. However, the use of the internet also exposes these entities to myriad cyber-attacks that may result in devastating outcomes if appropriate measures are not implemented to mitigate the risks. Currently, intrusion detection and threat detection schemes still face a number of challenges including low detection rates, high rates of false alarms, adversarial resilience, and big data issues. This chapter describes a focused literature survey of machine learning (ML) and data mining (DM) methods for cyber analytics in support of intrusion detection and cyber-attack detection. Key literature on ML and DM methods for intrusion detection is described. ML and DM methods and approaches such as support vector machine, random forest, and artificial neural networks, among others, with their variations, are surveyed, compared, and contrasted. Selected papers were indexed, read, and summarized in a tabular format.

scholarly journals Klasifikasi Data Log Intrusion Detection Sistem (Ids) Dengan Decision Tree C4.5

2019 ◽  
Vol 1 (2) ◽  
pp. 143-153
Author(s):  
Thifal Baraas ◽  
Akbar Juliansyah ◽  
Ahmad Ashril Rizal
Keyword(s):  
Data Mining ◽  
Intrusion Detection ◽  
Decision Tree ◽  
Detection System ◽  
The Internet ◽  
Log Data ◽  
Internet Users ◽  
C4.5 Algorithm ◽  
Decision Tree Method ◽  
Data Log

Abstrak Browsing atau kegiatan menjelajahi internet menjadi salah satu aktivitas yang sering dilakukan pada zaman kini. Baik anak-anak hingga orang dewasa menjadi pengguna internet. Akan tetapi para pengguna internet tidak mengetahui jika internet juga bisa menjadi ancaman terutama adanya serangan-serangan yang menyerang sistem keamanan jaringan. Untuk mendeteksi adanya aktivitas yang mencurigakan yang melalui jaringan dibutuhkan bantuan dari IDS (Intrusion Detection Sistem). Ketika terjadi banyak serangan yang masuk, IDS tidak bisa menanganinya secara akurat, hal ini mengakibatkan aktivitas normal di dalam jaringan bisa dianggap sebagai serangan dari hacker atau sebaliknya. Data mining adalah prses yang digunakan untuk menemukan hubungan dari data-data untuk mendapatkan sebuah kesimpulan dari data tersebut. Algoritma C4.5 merupakan salah satu algoritma yang digunakan untuk membuat pohon keputusan. Metode pohon keputusan mengubah fakta yang sangat besar menjadi pohon keputusan yang merepresentasikan aturan. Aturan dapat dengan mudah dipahami dengan bahasa alami. Dengan mengklasifikasi data log IDS dengan algoritma C4.5 dapat mengurangi terjadinya kesalahan IDS dalam menentukan aktivitas yang termasuk serangan atau bukan. Hasil penelitian menunjukkan data log IDS dapat diklasifikasikan dengan algoritma C4.5 dengan tingkat akurasi model adalah 96.371% yang membuktikan bahwa model ini dapat digunakan dalam menentukan aktivitas yang termasuk serangan atau bukan. Abstract Browsing or surfing the internet is one of the activities that are often done today. Both children and adults become internet users. However, internet users do not know the internet can also be a threat, especially the attacks that attack the network security system. To detect suspicious activity through the network, assistance from IDS (Intrusion Detection System) is needed. When there are many incoming attacks, IDS cannot handle it accurately, this results in normal activities on the network can be considered as an attack from hackers or vice versa. Data mining is a process used to find relationships from data to get a conclusion from that data. C4.5 algorithm is one algorithm used to make a decision tree. The decision tree method converts very large facts into decision trees that represent rules. Rules can be easily understood with natural language. By classifying the IDS log data with the C4.5 algorithm it can reduce the occurrence of IDS errors in determining which activities are included or not. The results showed the IDS log data can be classified with the C4.5 algorithm with a 96.371% accuracy rate of the model which proves that this model can be used in determining activities that are included as attacks or not.

scholarly journals Realguard: A Lightweight Network Intrusion Detection System for IoT Gateways

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 432
Author(s):  
Xuan-Ha Nguyen ◽  
Xuan-Duong Nguyen ◽  
Hoang-Hai Huynh ◽  
Kim-Hung Le
Keyword(s):  
Deep Learning ◽  
Intrusion Detection ◽  
Real Time ◽  
Intrusion Detection System ◽  
Detection System ◽  
Cyber Attacks ◽  
Network Intrusion Detection ◽  
The Internet ◽  
Network Intrusion ◽  
Network Intrusion Detection System

Cyber security has become increasingly challenging due to the proliferation of the Internet of things (IoT), where a massive number of tiny, smart devices push trillion bytes of data to the Internet. However, these devices possess various security flaws resulting from the lack of defense mechanisms and hardware security support, therefore making them vulnerable to cyber attacks. In addition, IoT gateways provide very limited security features to detect such threats, especially the absence of intrusion detection methods powered by deep learning. Indeed, deep learning models require high computational power that exceeds the capacity of these gateways. In this paper, we introduce Realguard, an DNN-based network intrusion detection system (NIDS) directly operated on local gateways to protect IoT devices within the network. The superiority of our proposal is that it can accurately detect multiple cyber attacks in real time with a small computational footprint. This is achieved by a lightweight feature extraction mechanism and an efficient attack detection model powered by deep neural networks. Our evaluations on practical datasets indicate that Realguard could detect ten types of attacks (e.g., port scan, Botnet, and FTP-Patator) in real time with an average accuracy of 99.57%, whereas the best of our competitors is 98.85%. Furthermore, our proposal effectively operates on resource-constraint gateways (Raspberry PI) at a high packet processing rate reported about 10.600 packets per second.

scholarly journals Performance Analysis of Proposed Hybrid Machine Learning Model for Efficient Intrusion Detection

2020 ◽  
Vol 9 (6) ◽  
pp. 904-911
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Detection System ◽  
Hybrid Approach ◽  
Attack Detection ◽  
Cyber Attacks ◽  
Machine Learning Algorithms ◽  
The Internet ◽  
Detection Techniques ◽  
Networking Technologies

At present networking technologies has provided a better medium for people to communicate and exchange information on the internet. This is the reason in the last ten years the number of internet users has increased exponentially. The high-end use of network technology and the internet has also presented many security problems. Many intrusion detection techniques are proposed in combination with KDD99, NSL-KDD datasets. But there are some limitations of available datasets. Intrusion detection using machine learning algorithms makes the detection system more accurate and fast. So in this paper, a new hybrid approach of machine learning combining feature selection and classification algorithms is presented. The model is examined with the UNSW NB15 intrusion dataset. The proposed model has achieved better accuracy rate and attack detection also improved while the false attack rate is reduced. The model is also successful to accurately classify rare cyber attacks like worms, backdoor, and shellcode.

Intrusion Detection Over Encrypted Network Data

2019 ◽  
Vol 63 (4) ◽  
pp. 604-619 ◽  
Author(s):  
Leyli Karaçay ◽  
Erkay Savaş ◽  
Halit Alptekin
Keyword(s):  
Intrusion Detection ◽  
Cyber Security ◽  
Detection System ◽  
Information Leakage ◽  
Cyber Attacks ◽  
Data Sets ◽  
Sensitive Information ◽  
Log Files ◽  
System Data ◽  
Lattice Based Cryptography

Abstract Effective protection against cyber-attacks requires constant monitoring and analysis of system data in an IT infrastructure, such as log files and network packets, which may contain private and sensitive information. Security operation centers (SOC), which are established to detect, analyze and respond to cyber-security incidents, often utilize detection models either for known types of attacks or for anomaly and applies them to the system data for detection. SOC are also motivated to keep their models private to capitalize on the models that are their propriety expertise, and to protect their detection strategies against adversarial machine learning. In this paper, we develop a protocol for privately evaluating detection models on the system data, in which privacy of both the system data and detection models is protected and information leakage is either prevented altogether or quantifiably decreased. Our main approach is to provide an end-to-end encryption for the system data and detection models utilizing lattice-based cryptography that allows homomorphic operations over ciphertext. We employ recent data sets in our experiments which demonstrate that the proposed privacy-preserving intrusion detection system is feasible in terms of execution times and bandwidth requirements and reliable in terms of accuracy.

scholarly journals A Review on Intrusion Detection System Based on Various Learning Techniques

2021 ◽  
pp. 36-42
Author(s):  
Shiladitya Raj ◽  
◽  
Megha Jain ◽  
Megha kamble ◽  
◽  
...  
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Internet Security ◽  
Cyber Attacks ◽  
The Internet ◽  
Learning Approaches ◽  
Internet Users ◽  
Learning Techniques ◽  
Use Of The Internet

In this world of the Internet, security plays an important role as Internet users grow rapidly. Security in the network is one of the modern periods’ main issues. In the last decade, the exponential growth and massive use of the Internet have enabled system security vulnerabilities a critical aspect. Intrusion detection system to track unauthorized access as well as exceptional attacks through secured networks. Several experiments on the IDS have been carried out in recent years. And to know the current state of machine learning approaches to address the issue of intrusion detection. IDS is commonly used for the detection and recognition of cyberattacks at the network and host stage, in a timely and automatic manner. This research assesses the creation of a deep neural network (DNN), a form of deep learning model as well as ELM to detect unpredictable and unpredictable cyber-attacks.

scholarly journals A Review on Intrusion Detection System Based on Various Learning Techniques

2021 ◽  
Vol 1 (2) ◽  
pp. 36-42
Author(s):  
Shiladitya Raj ◽  
Megha Jain ◽  
Megha kamble
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Internet Security ◽  
Cyber Attacks ◽  
The Internet ◽  
Learning Approaches ◽  
Internet Users ◽  
Learning Techniques ◽  
Use Of The Internet

In this world of the Internet, security plays an important role as Internet users grow rapidly. Security in the network is one of the modern periods' main issues. In the last decade, the exponential growth and massive use of the Internet have enabled system security vulnerabilities a critical aspect. Intrusion detection system to track unauthorized access as well as exceptional attacks through secured networks. Several experiments on the IDS have been carried out in recent years. And to know the current state of machine learning approaches to address the issue of intrusion detection. IDS is commonly used for the detection and recognition of cyberattacks at the network and host stage, in a timely and automatic manner. This research assesses the creation of a deep neural network (DNN), a form of deep learning model as well as ELM to detect unpredictable and unpredictable cyber-attacks

scholarly journals Issues and Challenges for Preventing Cyber - Attacks

2020 ◽  
pp. 212-215
Author(s):  
Arvind Kishanrao Rathod ◽  
Bhushan Shivaji Kulkarni
Keyword(s):  
Intrusion Detection ◽  
Cyber Security ◽  
Intrusion Detection System ◽  
Preventive Measures ◽  
Detection System ◽  
Social Engineering ◽  
Cyber Attacks ◽  
Security Policies ◽  
Data Corruption ◽  
Individual System

The main objective of cyber security is to prevent various types of attacks on individual user system or organizations system or network by implementing some preventive measures such as by enforcing security policies, providing security awareness among the peoples by organizing frequent trainings or workshop to avoid social engineering attacks. Also implementing some tools such as intrusion detection system, firewall, antiviruses in individual system on organizations network and avoid from data corruption or alteration attacks by attackers via internet or some other means.

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