The advancement in IoT has prompted its application in areas such as smart homes, smart cities, etc., and this has aided its exponential growth. However, alongside this development, IoT networks are experiencing a rise in security challenges such as botnet attacks, which often appear as network anomalies. Similarly, providing security solutions has been challenging due to the low resources that characterize the devices in IoT networks. To overcome these challenges, the fog computing paradigm has provided an enabling environment that offers additional resources for deploying security solutions such as anomaly mitigation schemes. In this paper, we propose a hybrid anomaly mitigation framework for IoT using fog computing to ensure faster and accurate anomaly detection. The framework employs signature- and anomaly-based detection methodologies for its two modules, respectively. The signature-based module utilizes a database of attack sources (blacklisted IP addresses) to ensure faster detection when attacks are executed from the blacklisted IP address, while the anomaly-based module uses an extreme gradient boosting algorithm for accurate classification of network traffic flow into normal or abnormal. We evaluated the performance of both modules using an IoT-based dataset in terms response time for the signature-based module and accuracy in binary and multiclass classification for the anomaly-based module. The results show that the signature-based module achieves a fast attack detection of at least six times faster than the anomaly-based module in each number of instances evaluated. The anomaly-based module using the XGBoost classifier detects attacks with an accuracy of 99% and at least 97% for average recall, average precision, and average F1 score for binary and multiclass classification. Additionally, it recorded 0.05 in terms of false-positive rates.

Abstract-User authentication through textual passwords is very common in computer systems due to its ease of use. However textual passwords are vulnerable to different kinds of security attacks, such as spyware and dictionary attacks. In order to overcome the deficiencies of textual password scheme, many graphical password schemes have been proposed. The proposed schemes could not fully replace textual passwords, due to usability and security issues. In this paper a text based user authentication scheme is proposed which improves the security of textual password scheme by modifying the password input method and adding a password transformation layer. In the proposed scheme alphanumeric password characters are represented by random decimal numbers which resist online security attacks such as shoulder surfing and key logger attacks. In the registration process password string is converted into a completely new string of symbols or characters before encryption. This strategy improves password security against offline attacks such as brute-force and dictionary attacks. In the proposed scheme passwords consist of alphanumeric characters therefore users are not required to remember any new kind of passwords such as used in graphical authentication. Hence password memorability burden has been minimized. However mean authentication time of the proposed scheme is higher than the textual password scheme due to the security measures taken for the online attacks.

The Internet of Things (IoT) is on the rise and it is giving a new shape to several fields such as smart cities, smart homes, smart health, etc. as it facilitates the connection of physical objects to the internet. However, this advancement comes along with new challenges in terms of security of the devices in the IoT networks. Some of these challenges come as network anomalies. Hence, this has prompted the use of network anomaly mitigation schemes as an integral part of the defense mechanisms of IoT networks in order to protect the devices from malicious users. Thus, several schemes have been proposed to mitigate network anomalies. This paper covers a review of different network anomaly mitigation schemes in IoT networks. The schemes' objectives, operational procedures, and strengths are discussed. A comparison table of the reviewed schemes, as well as a taxonomy based on the detection methodology, is provided. In contrast to other surveys that presented qualitative evaluations, our survey provides both qualitative and quantitative evaluations. The UNSW-NB15 dataset was used to conduct a performance evaluation of some classification algorithms used for network anomaly mitigation schemes in IoT. Finally, challenges and open issues in the development of network anomaly mitigation schemes in IoT are discussed.

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The revolution of computer network technologies and telecommunication technologies increases the number of Internet users enormously around the world. Thus, many companies nowadays produce various devices having network chips, each device becomes part of the Internet of Things and can run on the Internet to achieve various services for its users. This led to the increase in security threats and attacks on these devices. Due to the increased number of devices connected to the Internet, the attackers have more opportunities to perform their attacks in such an environment. Therefore, security has become a big challenge more than before. In addition, confidentiality, integrity, and availability are required components to assure the security of Internet of Things. In this article, an adaptive intrusion detection and prevention system is proposed for Internet of Things (IDPIoT) to enhance security along with the growth of the devices connected to the Internet. The proposed IDPIoT enhances the security including host-based and network-based functionality by examining the existing intrusion detection systems. Once the proposed IDPIoT receives the packet, it examines the behavior, the packet is suspected, and it blocks or drops the packet. The main goal is accomplished by implementing one essential part of security, which is intrusion detection and prevention system.

Vehicular ad-hoc networks allow vehicles to exchange messages pertaining to safety and road efficiency. Building trust between nodes can, therefore, protect vehicular ad-hoc networks from malicious nodes and eliminate fake messages. Although there are several trust models already exist, many schemes suffer from varied limitations. For example, many schemes rely on information provided by other peers or central authorities, for example, roadside units and reputation management centers to ensure message reliability and build nodes’ reputation. Also, none of the proposed schemes operate in different environments, for example, urban and rural. To overcome these limitations, we propose a novel trust management scheme for self-organized vehicular ad-hoc networks. The scheme is based on a crediting technique and does not rely on other peers or central authorities which distinguishes it as an economical solution. Moreover, it is hybrid, in the sense it is data-based and entity-based which makes it capable of revoking malicious nodes and discarding fake messages. Furthermore, it operates in a dual-mode (urban and rural). The simulation has been performed utilizing Veins, an open-source framework along with OMNeT++, a network simulator, and SUMO, a traffic simulator. The scheme has been tested with two trust models (urban and rural). The simulation results prove the performance and security efficacy of the proposed scheme.

Despite numerous efforts, developing an authentication scheme that offers strong security while offering memorability and usability remains a grand challenge. In this paper, we propose a textual-graphical hybrid authentication scheme that improves the security, memorability and usability inadequacies of existing authentication schemes. This has been achieved by combining a range of mechanisms together, in a novel manner, to address weaknesses of the existing security schemes. Firstly, two dynamically selectable modes of password entry (Easy Login, and Secure Login) provide a trade-off between usability and security, allowing the user to dynamically switch to any of these methods in real-time based on the security of the surrounding environment (e.g., secure home environment versus insecure public places) or the criticality of the user account (e.g., a bank account). The other mechanisms included a novel use of the drawmetric mechanism for setting the password to improve memorability, multistep authentication, a novel adaptation of one-time password (OTP) concept using a random selection of password elements, random placement of password elements in different steps, assigning random numbers to the password elements to increase security, and use of simple addition to improve security. We have implemented and analysed the proposed scheme for its security against brute-force attacks, dictionary, shoulder surfing, random guessing, phishing or forming, keystroke/mouse logger, and multiple recording attacks. We have also investigated its usability and memorability, reporting various trends of password elements used and the respective authentication times. Moreover, we have compared the proposed scheme with eight other well-known authentication schemes in terms of its resilience and authentication time. The results and analyses demonstrate the effectiveness of the proposed scheme. We believe that a range of novel methods introduced in this proposed scheme opens several doors for innovation in security techniques.

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