Detecting Botnets Through Log Correlation

Botnets, which consist of thousands of compromised machines, can cause a significant threat to other systems by launching Distributed Denial of Service attacks, keylogging, and backdoors. In response to this threat, new effective techniques are needed to detect the presence of botnets. In this paper, we have used an interception technique to monitor Windows Application Programming Interface system calls made by communication applications. Existing approaches for botnet detection are based on finding bot traffic patterns. Our approach does not depend on finding patterns but rather monitors the change of behaviour in the system. In addition, we will present our idea of detecting botnet based on log correlations from different hosts

Performance evaluation of DCA and SRC on a single bot detection

Malicious users try to compromise systems using new techniques. One of the recent techniques used by the attacker is to perform complex distributed attacks such as denial of service and to obtain sensitive data such as password information. These compromised machines are said to be infected with malicious software termed a “bot”. In this paper, we investigate the correlation of behavioural attributes such as keylogging and packet flooding behaviour to detect the existence of a single bot on a compromised machine by applying (1) Spearman’s rank correlation (SRC) algorithm and (2) the Dendritic Cell Algorithm (DCA). We also compare the output results generated from these two methods to the detection of a single bot. The results show that the DCA has a better performance in detecting malicious activities

Behavioural correlation for malicious bot detection

Over the past few years, IRC bots, malicious programs which are remotely controlled by the attacker, have become a major threat to the Internet and its users. These bots can be used in different malicious ways such as to launch distributed denial of service (DDoS) attacks to shutdown other networks and services. New bots are implemented with extended features such as keystrokes logging, spamming, traffic sniffing, which cause serious disruption to targeted networks and users. In response to these threats, there is a growing demand for effective techniques to detect the presence of bots/botnets. Currently existing approaches detect botnets rather than individual bots. In our work we present a host-based behavioural approach for detecting bots/botnets based on correlating different activities generated by bots by monitoring function calls within a specified time window. Different correlation algorithms have been used in this work to achieve the required task. We start our work by detecting IRC bots' behaviours using a simple correlation algorithm. A more intelligent approach to understand correlating activities is also used as a major part of this work. Our intelligent algorithm is inspired by the immune system. Although the intelligent approach produces an anomaly value for the classification of processes, it generates false positive alarms if not enough data is provided. In order to solve this problem, we introduce a modified anomaly value which reduces the amount of false positives generated by the original anomaly value. We also extend our work to detect peer to peer (P2P) bots which are the upcoming threat to Internet security due to the fact that P2P bots do not have a centralized point to shutdown or traceback, thus making the detection of P2P bots a real challenge. Our evaluation shows that correlating different activities generated by IRC/P2P bots within a specified time period achieves high detection accuracy. In addition, using an intelligent correlation algorithm not only states if an anomaly is present, but it also names the culprit responsible for the anomaly

Behavioural correlation for malicious bot detection

Over the past few years, IRC bots, malicious programs which are remotely controlled by the attacker, have become a major threat to the Internet and its users. These bots can be used in different malicious ways such as to launch distributed denial of service (DDoS) attacks to shutdown other networks and services. New bots are implemented with extended features such as keystrokes logging, spamming, traffic sniffing, which cause serious disruption to targeted networks and users. In response to these threats, there is a growing demand for effective techniques to detect the presence of bots/botnets. Currently existing approaches detect botnets rather than individual bots. In our work we present a host-based behavioural approach for detecting bots/botnets based on correlating different activities generated by bots by monitoring function calls within a specified time window. Different correlation algorithms have been used in this work to achieve the required task. We start our work by detecting IRC bots' behaviours using a simple correlation algorithm. A more intelligent approach to understand correlating activities is also used as a major part of this work. Our intelligent algorithm is inspired by the immune system. Although the intelligent approach produces an anomaly value for the classification of processes, it generates false positive alarms if not enough data is provided. In order to solve this problem, we introduce a modified anomaly value which reduces the amount of false positives generated by the original anomaly value. We also extend our work to detect peer to peer (P2P) bots which are the upcoming threat to Internet security due to the fact that P2P bots do not have a centralized point to shutdown or traceback, thus making the detection of P2P bots a real challenge. Our evaluation shows that correlating different activities generated by IRC/P2P bots within a specified time period achieves high detection accuracy. In addition, using an intelligent correlation algorithm not only states if an anomaly is present, but it also names the culprit responsible for the anomaly

Cache-Aided Non-Orthogonal Multiple Access for 5G-Enabled Vehicular Networks

The increasing demand for rich multimedia services and the emergence of the Internet-of-Things (IoT) pose challenging requirements for the next generation vehicular networks. Such challenges are largely related to high spectral efficiency and low latency requirements in the context of massive content delivery and increased connectivity. In this respect, caching and non-orthogonal multiple access (NOMA) paradigms have been recently proposed as potential solutions to effectively address some of these key challenges. In the present contribution, we introduce cache-aided NOMA as an enabling technology for vehicular networks. In this context, we first consider the full file caching case, where each vehicle caches and requests entire files using the NOMA principle. Without loss of generality, we consider a two-user vehicular network communication scenario under double Nakagami$-m$ fading conditions and propose an optimum power allocation policy. To this end, an optimization problem that maximizes the overall probability of successful decoding of files at each vehicle is formulated and solved. Furthermore, we consider the case of split file caching, where each file is divided into two parts. A joint power allocation optimization problem is formulated, where power allocation across vehicles and cached split files is investigated. The offered analytic results are corroborated by extensive results from computer simulations and interesting insights are developed. Indicatively, it is shown that the proposed caching-aided NOMA outperforms the conventional NOMA technique.Comment: Accepted for publication in IEEE Transactions on Vehicular Technolog

BROSMAP: A Novel Broadcast Based Secure Mobile Agent Protocol for Distributed Service Applications

Mobile agents are smart programs that migrate from one platform to another to perform the user task. Mobile agents offer flexibility and performance enhancements to systems and service real-time applications. However, security in mobile agent systems is a great concern. In this paper, we propose a novel Broadcast based Secure Mobile Agent Protocol (BROSMAP) for distributed service applications that provides mutual authentication, authorization, accountability, nonrepudiation, integrity, and confidentiality. The proposed system also provides protection from man in the middle, replay, repudiation, and modification attacks. We proved the efficiency of the proposed protocol through formal verification with Scyther verification tool