A drastic increase in cyberattacks targeting Internet of Things (IoT) devices using telnet protocols has been observed. IoT malware continues to evolve, and the diversity of OS and environments increases the difficulty of executing malware samples in an observation setting. To address this problem, we sought to develop an alternative means of investigation by using the telnet logs of IoT honeypots and analyzing malware without executing it. In this paper, we present a malware classification method based on malware binaries, command sequences, and meta-features. We employ both unsupervised or supervised learning algorithms and text-mining algorithms for handling unstructured data. Clustering analysis is applied for finding malware family members and revealing their inherent features for better explanation. First, the malware binaries are grouped using similarity analysis. Then, we extract key patterns of interaction behavior using an N-gram model. We also train a multiclass classifier to identify IoT malware categories based on common infection behavior. For misclassified subclasses, second-stage sub-training is performed using a file meta-feature. Our results demonstrate 96.70% accuracy, with high precision and recall. The clustering results reveal variant attack vectors and one denial of service (DoS) attack that used pure Linux commands.

Distributed Denial of Service (DDoS) attacks based on HTTP and HTTPS (i.e., HTTP(S)-DDoS) are increasingly popular among attackers. Overlay-based mitigation solutions attract small and medium-sized enterprises mainly for their low cost and high scalability. However, conventional overlay-based solutions assume content inspection to remotely mitigate HTTP(S)-DDoS attacks, prompting trust concerns. This paper reports on a new overlay-based method which practically adds a third level of client identification (to conventional per-IP and per-connection). This enhanced identification enables remote mitigation of more complex HTTP(S)-DDoS categories without content inspection. A novel behavior-based reputation and penalty system is designed, then a simplified proof of concept prototype is implemented and deployed on DeterLab. Among several conducted experiments, two are presented in this paper representing a single-vector and a multi-vector complex HTTP(S)-DDoS attack scenarios (utilizing LOIC, Slowloris, and a custom-built attack tool for HTTPS-DDoS). Results show nearly 99.2% reduction in attack traffic and 100% chance of legitimate service. Yet, attack reduction decreases, and cost in service time (of a specified file) rises, temporarily during an approximately 2 minutes mitigation time. Collateral damage to non-attacking clients sharing an attack IP is measured in terms of a temporary extra service time. Only the added identification level was utilized for mitigation, while future work includes incorporating all three levels to mitigate switching and multi-request per connection attack categories.

This letter proposes a novel intrusion tolerant system consisting of several virtual machines (VMs) that refresh the target system periodically and by live migration, which monitors the many features of the VMs to identify and replace exhausted VMs. The proposed scheme provides adequate performance and dependability against denial of service (DoS) attacks. To show its efficiency and security, we conduct experiments on the CSIM20 simulator, which showed 22% improvement in a normal situation and approximately 77.83% improvement in heavy traffic in terms of the response time compared to that reported in the literature. We measure and compare the response time. The result show that the proposed scheme has shorter response time and maintains than other systems and supports services during the heavy traffic.

Content-centric networking (CCN) is one of candidates being spotlighted as the technologies of the future Internet to solve the problems of the current Internet. Since DoS/DDoS attack is the most serious threat to the current Internet, this letter introduces the possibility of DoS/DDoS attack on CCN for the first time. And we introduce an attack method using fake-request packets and propose countermeasures in order to detect and/or react to CCN DoS/DDoS attack, and then analyze the result of our proposal.

As a result of the rapid development of the Internet in recent years, network security has become an urgent issue. Distributed denial of service (DDoS) attacks are one of the most serious security issues. In particular, 60 percent of the DDoS attacks found on the Internet are TCP attacks, including SYN flood attacks. In this paper, we propose adaptive timer-based countermeasures against SYN flood attacks. Our proposal utilizes the concept of soft-state protocols that are widely used for resource management on the Internet. In order to avoid deadlock, a server releases resources using a time-out mechanism without any explicit requests from its clients. If we change the value of the timer in accordance with the network conditions, we can add more flexibility to the soft-state protocols. The timer is used to manage the resources assigned to half-open connections in a TCP 3-way handshake mechanism, and its value is determined adaptively according to the network conditions. In addition, we report our simulation results to show the effectiveness of our approach.

As an increasing number of businesses and services depend on the Internet, protecting them against DDoS (Distributed Denial of Service) attacks becomes a critical issue. A traceback is used to discover technical information concerning the ingress points, paths, partial paths or sources of a packet or packets causing a problematic network event. The traceback mechanism is a useful tool to identify the attack source of the (DDoS) attack, which ultimately leads to preventing against the DDoS attack. There are numerous traceback mechanisms that have been proposed by many researchers. In this paper, we analyze the existing traceback mechanisms, describe the common security capabilities of traceback mechanisms, and evaluate them in terms of the various criteria. In addition, we identify typical application of traceback mechanisms.

The Distributed Denial of Service attack (DDoS) is one of the major threats to network security that exhausts network bandwidth and resources. Recently, an efficient approach Live Baiting was proposed for detecting the identities of DDoS attackers in web service using low state overhead without requiring either the models of legitimate requests nor anomalous behavior. However, Live Baiting has two limitations. First, the detection algorithm adopted in Live Baiting starts with a suspects list containing all clients, which leads to a high false positive probability especially for large web service with a huge number of clients. Second, Live Baiting adopts a fixed threshold based on the expected number of requests in each bucket during the detection interval without the consideration of daily and weekly traffic variations. In order to address the above limitations, we first distinguish the clients activities (Active and Non-Active clients during the detection interval) in the detection process and then further propose a new adaptive threshold based on the Change Point Detection method, such that we can improve the false positive probability and avoid the dependence of detection on sites and access patterns. Extensive trace-driven simulation has been conducted on real Web trace to demonstrate the detection efficiency of the proposed scheme in comparison with the Live Baiting detection scheme.

Distributed denial-of-service attacks on public servers have recently become more serious. Most of them are SYN flood attacks, since the malicious attackers can easily exploit the TCP specification to generate traffic making public servers unavailable. We need a defense method which can protect legitimate traffic so that end users can connect the target servers during such attacks. In this paper, we propose a new framework, in which all of the TCP connections to the victim servers from a domain are maintained at the gateways of the domain (i.e., near the clients). We call the nodes maintaining the TCP connection defense nodes. The defense nodes check whether arriving packets are legitimate or not by maintaining the TCP connection. That is, the defense nodes delegate reply packets to the received connection request packets and identify the legitimate packets by checking whether the clients reply to the reply packets. Then, only identified traffic are relayed via overlay networks. As a result, by deploying the defense nodes at the gateways of a domain, the legitimate packets from the domain are relayed apart from other packets including attack packets and protected. Our simulation results show that our method can protect legitimate traffic from the domain deploying our method. We also describe the deployment scenario of our defense mechanism.

Denial of service (DoS) attacks have become one of the most serious threats to the Internet. Enabling detection of attacks in network traffic is an important and challenging task. However, most existing volume-based schemes can not detect short-term attacks that have a minor effect on traffic volume. On the other hand, feature-based schemes are not suitable for real-time detection because of their complicated calculations. In this paper, we develop an IP packet size entropy (IPSE)-based DoS/DDoS detection scheme in which the entropy is markedly changed when traffic is affected by an attack. Through our analysis, we find that the IPSE-based scheme is capable of detecting not only long-term attacks but also short-term attacks that are beyond the volume-based schemes' ability to detect. Moreover, we test our proposal using two typical Internet traffic data sets from DARPA and SINET, and the test results show that the IPSE-based detection scheme can provide detection of DoS/DDoS attacks not only in a local area network (DARPA) and but also in academic backbone network (SINET).

Distributed denial-of-service attacks on public servers have recently become more serious. The most effective way to prevent this type of traffic is to identify the attack nodes and detach (or block) attack nodes at their egress routers. However, existing traceback mechanisms are currently not widely used for several reasons, such as the necessity of replacement of many routers to support traceback capability, or difficulties in distinguishing between attacks and legitimate traffic. In this paper, we propose a new scheme that enables a traceback from a victim to the attack nodes. More specifically, we identify the egress routers that attack nodes are connecting to by estimating the traffic matrix between arbitral source-destination edge pairs. By monitoring the traffic variations obtained by the traffic matrix, we identify the edge routers that are forwarding the attack traffic, which have a sharp traffic increase to the victim. We also evaluate the effectiveness of our proposed scheme through simulation, and show that our method can identify attack sources accurately.

A remote user authentication scheme is a two-party protocol whereby an authentication server in a distributed system confirms the identity of a remote individual logging on to the server over an untrusted, open network. Recently, Lee et al. have proposed an efficient nonce-based scheme for remote user authentication using smart cards. This work reviews Lee et al.'s authentication scheme and provides a security analysis on the scheme. Our analysis shows that Lee et al.'s scheme does not achieve its basic aim of authenticating remote users and furthermore has a very hazardous method for changing passwords. In addition, we recommend some changes to the scheme so that it can attain at least its main security goal.

Distributed denial-of-service attacks on public servers have recently become more serious. More are SYN Flood attacks, since the malicious attackers can easily exploit the TCP specification to generate traffic making public servers unavailable. To assure that network services will not be interrupted, we need faster and more accurate defense mechanisms against malicious traffic, especially SYN Floods. One of the problems in detecting SYN Flood traffic is that server nodes or firewalls cannot distinguish the SYN packets of normal TCP connections from those of SYN Flood attack. Moreover, since the rate of normal network traffic may vary, we cannot use an explicit threshold of SYN arrival rates to detect SYN Flood traffic. In this paper we introduce a mechanism for detecting SYN Flood traffic more accurately by taking into consideration the time variation of arrival traffic. We first investigate the statistics of the arrival rates of both normal TCP SYN packets and SYN Flood attack packets. We then describe our new detection mechanism based on the statistics of SYN arrival rates. Our analytical results show that the arrival rate of normal TCP SYN packets can be modeled by a normal distribution and that our proposed mechanism can detect SYN Flood traffic quickly and accurately regardless of time variance of the traffic.

In this letter, we show that some stream authentication schemes using hash chaining are highly vulnerable to denial of service (DoS) attacks. An adversary can disrupt all receivers of group by making use of modifying a few packets in those schemes.

We investigate the effects of DoS (Denial of Service) attacks in wireless ad hoc networks using simulations, concentrating on the problem of energy availability. Our results show that the damages due to the DoS attack may quite different with those in wired networks: First, the nodes along the transmission route mostly suffer damages rather than the victim node itself. Second, if the mobile nodes are crowded and close together, the damage becomes more severe. Lastly, if the nodes have random mobility, the attacker itself consumes more energy.

Hash-based IP traceback is a technique to generate audit trails for traffic within a network. Using the audit trails, it reconstructs not only the true attack paths of a Distributed Denial of Service attack (DDoS attack), but also the true path of a single packet attack. However, hash-based IP traceback cannot identify attacker nodes themselves because it has no audit trail on the subnet's layer-2 network under the detected leaf router, which is the nearest node to an attacker node on a layer-3 network. We propose a layer-2 extension to hash-based IP traceback, which stores two identifiers with packets' audit trails while reducing the memory requirement for storing identifiers. One of these identifiers shows the leaf router's interface through which an attacking packet came, and the other represents the ingress port on a layer-2 switch through which the attacking packet came. We implement a prototype on FreeBSD and evaluate it in a preliminary experiment.

Recently, Hwang and Yeh demonstrated that Peyravian-Zunic's password authentication scheme is vulnerable to several attacks, and then proposed a modified version. In this letter, we show that Hwang-Yeh's scheme still has several weaknesses and drawbacks.