The need for security is exceptionally pressing in the financial services industry, which has been under an unrelenting barrage of cybercrime activities over the past few years.
Organizations must continually guard against cybercrime attacks such as identity theft, fraud, intellectual property theft, and system and network intrusion. Intrusion detection systems (IDS) are vital in any cybersecurity architecture, yet current IDS solutions remain prone to high rates of false positive and false negative states.
System intrusion detection — also called host-based intrusion detection — monitors network traffic and hardware components for unauthorized or suspicious activities. These signature or behavior-based systems can help protect businesses from increasingly sophisticated and disruptive cyberattacks, including external attacks such as distributed denial of service (DDoS) and insider threats such as sabotage, espionage, and fraud.
Signature-based systems, while having the advantage of a low false positive rate, typically rely on lists of known attacks and are therefore always at risk of novel methods of intrusion, such as those that caught Facebook, Alibaba, and LinkedIn off guard in the last few years.
Behavioral-based (also called anomaly-based) systems learn, through a number of statistical methods, what constitutes normal behavior in a given context. Once “normal” has been characterized, the system looks for behavior not matching that pattern and generates alerts.
Behavioral-based IDSs historically have far more false negatives, all of which require effort and expense to adjudicate. Recent refinements in behavioral-based IDSs add the intelligence to cut false positives, at the expense of requiring vastly more computational power.
Unlike traditional signature and behavior-based IDS solutions, Katana Graph ingests, processes, unifies, learns, and analyzes system and network data drawn from multiple sources in a graph structure. It synthesizes all collected data into a single view using a labeled property graph data model, then stores and locates the structural patterns of malicious behaviors. This approach allows rapid analysis of complex structural patterns using graph query and pattern recognition algorithms, offering an unprecedented level of intrusion detection.
Katana Graph's comprehensive datasheet, System Intrusion Detection, further delineates the weaknesses of traditional IDSs and why they fail. It also provides a case study on Intrusion Detection in Action.
Cybercriminals have a large arsenal of measures to evade IDS. Detection rate is an ongoing problem for any organization attempting to protect sensitive information and devices from attacks. Security professionals must now make use of all available technology to counter hackers’ actions. For them, this System Intrusion Detection datasheet is a must read.
Click here to view this datasheet.
