The Internet of Things (IoT) is becoming an integral part of our daily life including health, environment, homes, military, etc. The enormous growth of IoT in recent years has attracted hackers to take advantage of their computation and communication capabilities to perform different types of attacks. The major concern is that IoT devices have several vulnerabilities that can be easily exploited to form IoT botnets consisting of millions of IoT devices and posing significant threats to Internet security. In this context, DDoS attacks originating from IoT botnets is a major problem in today’s Internet that requires immediate attention. In this paper, we propose a Security Information and Event Management-based IoT botnet DDoS attack detection and mitigation system. This system detects and blocks DDoS attack traffic from compromised IoT devices by monitoring specific packet types including TCP SYN, ICMP and DNS packets originating from these devices. We discuss a prototype implementation of the proposed system and we demonstrate that SIEM based solutions can be configured to accurately identify and block malicious traffic originating from compromised IoT devices.

The large expansion in network services and applications seen in the last few years requires new network architectures to satisfy an increasing number of users and enhance content delivery. Named Data Networking (NDN) has recently appeared as a new paradigm to solve many shortcomings in the current TCP/IP architecture. Its main characteristics like stateful forwarding and in-network caching made NDN networks an efficient environment for data delivery where the data is retrieved based on content names rather than IP addresses. The NDN, by its nature, defends against the well-known Distributed Denial of Service (DDoS) attacks that take place in the traditional TCP/IP architecture. However, a special kind of DoS attack called Collusive Interest Flooding Attack (CIFA) has appeared to overwhelm the resources of NDN routers by filling their Pending Interest Tables (PIT) with long-lasting malicious entries. The network throughput and consumer satisfaction rate are highly affected by CIFA. A lightweight yet efficient stateless CIFA detection algorithm is proposed in this research utilizing the non-parametric CUSUM algorithm; a change point detection approach that detects the point in time when a transition occurs in the network. The proposed algorithm is characterized by its low computational overhead, highly accurate detection, and quick response. To detect the malicious name prefixes and eliminate the CIFA effect, a mitigation algorithm that uses the average response time vales of all name prefixes is proposed in this research. Experimental results show that this approach detects CIFA after 199.5 ms from when an attack is launched in the large-scale topology. In addition, the mitigation approach effectively reduces the PIT utilization and increases the average consumer satisfaction rate.

In recent years, various platforms have witnessed an unprecedented increase in the number of ransomware attacks targeting hospitals, governments, enterprises, and end-users. The purpose of this is to maliciously encrypt documents and files on infected machines, depriving victims of access to their data, whereupon attackers would seek some sort of a ransom in return for restoring access to the legitimate owners; hence the name. This cybersecurity threat would inherently cause substantial financial losses and time wastage for affected organizations and users. A great deal of research has taken place across academia and around the industry to combat this threat and mitigate its danger. These ongoing endeavors have resulted in several detection and prevention schemas. Nonetheless, these approaches do not cover all possible risks of losing data. In this paper, we address this facet and provide an efficient solution that would ensure an efficient recovery of XML documents from ransomware attacks. This paper proposes a self-healing version-aware ransomware recovery (SH-VARR) framework for XML documents. The proposed framework is based on the novel idea of using the link concept to maintain file versions in a distributed manner while applying access-control mechanisms to protect these versions from being encrypted or deleted. The proposed SH-VARR framework is experimentally evaluated in terms of storage overhead, time requirement, CPU utilization, and memory usage. Results show that the snapshot size increases proportionately with the original size; the time required is less than 120 ms for files that are less than 1 MB in size; and the highest CPU utilization occurs when using the bzip2. Moreover, when the zip and gzip are used, the memory usage is almost fixed (around 6.8 KBs). In contrast, it increases to around 28 KBs when the bzip2 is used.