While there have been extensive studies of denial of service (DoS) attacks and DDoS attack mitigation, such attacks remain challenging to mitigate. For example, Low-Rate DDoS (LR-DDoS) attacks are known to be difficult to detect, particularly in a software-defined network (SDN). Hence, in this paper we present a flexible modular architecture that allows the identification and mitigation of LR-DDoS attacks in SDN settings. Specifically, we train the intrusion detection system (IDS) in our architecture using six machine learning (ML) models (i.e., J48, Random Tree, REP Tree, Random Forest, Multi-Layer Perceptron (MLP), and Support Vector Machines (SVM)) and evaluate their performance using the Canadian Institute of Cybersecurity (CIC) DoS dataset. The findings from the evaluation demonstrate that our approach achieves a detection rate of 95%, despite the difficulty in detecting LR-DoS attacks. We also remark that in our deployment, we use the open network operating system (ONOS) controller running on Mininet virtual machine in order for our simulated environment to be as close to real-world production networks as possible. In our testing topology, the intrusion prevention detection system mitigates all attacks previously detected by the IDS system. This demonstrates the utility of our architecture in identifying and mitigating LR-DDoS attacks.

/pdf/a-flexible-sdn-based-architecture-for-identifying-and-2h3oonc43x.pdf

A Flexible SDN-Based Architecture for Identifying and Mitigating Low-Rate DDoS Attacks Using Machine Learning

The problem of low rate attack detection has been well studied in different situations. However the methods suffer to achieve higher performance in low rate attack detection. The multimedia transmission is focused on transmitting video and audio which claims higher bandwidth conditions. There exists no such algorithm in detecting low rate attacks for invariant network conditions. To solve this issue, an invariant feature based approach is presented in this paper. The method maintains the network features like the routes, bandwidth conditions and traffic. Based on these features, a set of routes has been identified for each data transmission. Here, low rate attack detection is performed at the reception of any packet and the data transmission is performed using cooperative routing. From the packet features, and the route being followed, the method identifies the class of route, traffic and bandwidth conditions of the route. Using these features, the method computes Network Transmission Support measure. Based on the NTS value, the method performs low rate attack detection and improves the performance.

Invariant packet feature with network conditions for efficient low rate attack detection in multimedia networks for improved QoS

Orthogonal frequency division multiplexing (OFDM) is a modulation technique which is now used in most new and emerging broadband wired and wireless communication systems because it is an effective solution to intersymbol interference caused by a dispersive channel. Very recently a number of researchers have shown that OFDM is also a promising technology for optical communications. This paper gives a tutorial overview of OFDM highlighting the aspects that are likely to be important in optical applications. To achieve good performance in optical systems OFDM must be adapted in various ways. The constraints imposed by single mode optical fiber, multimode optical fiber and optical wireless are discussed and the new forms of optical OFDM which have been developed are outlined. The main drawbacks of OFDM are its high peak to average power ratio and its sensitivity to phase noise and frequency offset. The impairments that these cause are described and their implications for optical systems discussed.

OFDM for Optical Communications

With the growing number of heterogeneous resource-constrained devices connected to the Internet, it becomes increasingly challenging to secure the privacy and protection of data. Strong but efficient cryptography solutions must be employed to deal with this problem, along with methods to standardize secure communications between these devices. The PRISEC module of the UbiPri middleware has this goal. In this work, we present the performance of the AES (Advanced Encryption Standard), RC6 (Rivest Cipher 6), Twofish, SPECK128, LEA, and ChaCha20-Poly1305 algorithms in Internet of Things (IoT) devices, measuring their execution times, throughput, and power consumption, with the main goal of determining which symmetric key ciphers are best to be applied in PRISEC. We verify that ChaCha20-Poly1305 is a very good option for resource constrained devices, along with the lightweight block ciphers SPECK128 and LEA.

/pdf/prisec-comparison-of-symmetric-key-algorithms-for-iot-38tjhk30nh.pdf

PRISEC: Comparison of Symmetric Key Algorithms for IoT Devices

The problem of forgery detection has been well studied and the forged finger prints produces highly impacting results in the biometric based security systems. There are many algorithms discussed earlier to detect forged finger prints. However, they suffer to achieve higher performance in terms of security. In this paper, a region centric minutiae propagation measure (RCMPM) based approach. First, the finger print image is read and removes the noisy points by applying the multi level Gabor filters. The Gabor filter has been applied in multiple level which helps to remove the noise from finger print image. The enhanced image is converted into number of integral image. The integral images are generated by splitting the image into number of tiny images according to the size of window. From the integral image produced, the island, dot, enclosure, bifurcation features are extracted. Extracted features are framed as feature vector and used to estimate the RCMPM measure. Based on the RCMPM measure, the presence of forged finger print has been identified and the same has been used to identify the region which has been modified. The accuracy of forged print detection has been improved and reduces the false classification ratio.

https://link.springer.com/content/pdf/10.1007/s11063-020-10407-4.pdf

Region Centric Minutiae Propagation Measure Orient Forgery Detection with Finger Print Analysis in Health Care Systems

Internet of things (IoT), also referred to as the internet of objects, is a dynamic global information network consisting of internet — connected objects. Wireless sensor networks (WSN) and RFID enabled Internet of Things finds a plethora of applications in almost all the fields such as education, health, agriculture, transportation and entertainment. Compact smart devices constitute an essential part of IoT. These compact smart devices range widely in size, energy, use, capacity and computation power. On the other hand, the integration of these smart things into the standard internet introduces several security challenges because the majority of internet technologies and protocols were not designed to support IoT. Though number of researchers have explored about such security challenges and open problems in IoT, there is a lack of a systematic study of the security challenges in the IoT landscape. This paper briefs the motivation for IoT, secured IoT layered architecture, IoT applications, Security issues with various attacks in each layer of the IoT and existing methods for providing security solutions and their limitations.

A systematic study of security issues in Internet-of-Things (IoT)

Developing Efficient Intrusion Tracking System Using Region Based Traffic Impact Measure Towards the Denial of Service Attack Mitigation

The problem of distributed denial of service attacks has been considered for several environments and number of approaches has been described. The basic analysis of flow rates does not help in modern attacks. To improve the performance of mitigating denial of service attacks, a novel multi model frequency analysis scheme has been described in this paper. The traffic approximation is required in different time domain as the modern attacks have been carried out in various strategic conditions. Similarly the method performs stream analysis which considers the payload data of different requests produced by various clients. Further the method performs the frequency approximation which counts the number of request being produced towards various servers. Using all the approximated values of different factors, the method compute the multi mode trust measure and decide the trustworthy of the request. The method produces efficient results in intrusion detection and accuracy in mitigating denial of service attacks.

Multi Model Network Analysis for Improved Intrusion Tracing towards Mitigating DDoS Attack

Distributed Denial of Service (DDoS) attacks is measured to be a vital threat to the net. Because of memory less feature of the net routing mechanism, it's extraordinarily onerous to trace back to the supply of those attacks. Ancient packet marking techniques are not any longer applied because of its high memory consumption and poor measurability. During this paper, we have a tendency to build use of entropy variation to observe the attack that differentiates between traditional and DDoS attack traffic. However it will solely be applied to observe the attack during which rate of the traffic flow is incredibly high. There are also many attacks during which the strength of the attack is also meager, however the results of those attacks might be still severe. So it's essential to observe such attacks that square measure in lower order in terms of information rate. We have a tendency to propose an Adaptive Attack Detection System (AADS) that is capable of detection DDoS attacks regardless of the attack strength.

T. Gnanasekaran

Papers

A systematic study of security issues in Internet-of-Things (IoT)

Developing Efficient Intrusion Tracking System Using Region Based Traffic Impact Measure Towards the Denial of Service Attack Mitigation

Multi Model Network Analysis for Improved Intrusion Tracing towards Mitigating DDoS Attack

Adaptive IP traceback mechanism for detecting low rate DDoS attacks

Retinal Vessel Extraction Framework Using Modified Adaboost Extreme Learning Machine